diff --git a/.gitignore b/.gitignore index b6a8f9b9..f069f6c5 100644 --- a/.gitignore +++ b/.gitignore @@ -70,3 +70,16 @@ docs/_build/ *.extract *.gz *.cts +*.log +*.delete +*cov +*.sumstats +*.delete_k +*asdf* +*qwer* +*M_5_50* +*.results + +# sublime text +*.idea* +*sublime* diff --git a/README.md b/README.md index 660bb3d6..1fc783b9 100644 --- a/README.md +++ b/README.md @@ -1,45 +1,70 @@ -LDSC (LD SCore) v 0.0.1 (alpha) -============================== -Copyright (c) 2014 Brendan Bulik-Sullivan & Hilary Finucane +#LDSC (LD SCore) `v1.0.0` -(NB We're still working on it. The dev branch is going to become v1.0.0 any day now. To report bugs, please either raise an issue on github or email us with LDSC in the subject line and the log file copied into the body of your message. Note that the in-progress tutorials on the wiki refer to the dev branch). +`ldsc` is a command line tool for estimating heritability and genetic correlation from GWAS summary statistics. `ldsc` also computes LD Scores. -What can I do with LDSC? ---------------------- +## Getting Started -1. Estimate LD Score -2. Quantify the inflation in GWAS test statistics from confounding bias. -3. Estimate heritability from GWAS summary statistics. -4. Estimate partitioned heritability from GWAS summary statistcs. -5. Estimate genetic covariance and correlation from GWAS summary statistics. +First, you will need to install python as well as the packages listed under the requirements header below. The easiest way to do this is with the [Anaconda](https://store.continuum.io/cshop/anaconda/) python distribution. All of the required packages come standard with Ananconda. -Contact -------- +In order to download `ldsc`, you should clone this repository -Brendan Bulik-Sullivan, bulik@broadinstitute.org + git clone https://github.com/bulik/ldsc.git -Hilary Finucane, hilaryf@mit.edu +Once you have installed `ldsc` as well as the required packages, typing +``` +$ python ldsc.py -h +``` +will print a list of all command-line options. Short tutorials describing the four basic functions of `ldsc` (estimating LD Scores, h2 and partitioned h2, genetic correlation, the LD Score regression intercept) can be found in the wiki. If you would like to run the tests, please see the wiki. -Citations ---------- +## Where Can I Get LD Scores? -For now, please cite +You can download LD Scores that are suitable for basic LD Score analyses (the LD Score regression intercept, heritability, partitioned heritability with the baseline model, genetic correlation) from $URL_GOES_HERE. -Bulik-Sullivan, et al. LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies. -In Press at Nature Genetics. -(preprint: http://biorxiv.org/content/early/2014/02/21/002931) +##Support -We are currently preparing manuscripts describing the methods for estimating partitioned h2 and rg. +Before contacting us, please try the following: -Requirements ------------- +1. The [tutorials](tutorials/) have basic advice on running `ldsc` and interpreting the output +2. Common issues are described in the [FAQ](docs/FAQ) +2. The methods are described in the papers (citations below) +3. Search the [issue tracker](https://github.com/bulik/ldsc/issues) -1. Python 2.7 -2. argparse 1.2.1 -3. bitarray 0.8.1 -4. numpy 1.8.0 -5. pandas 0.15.0 -6. scipy 0.10.1 +Please report bugs on the [issue tracker](https://github.com/bulik/ldsc/issues). +##Citation + +If you use the software or the LD Score regression intercept, please cite + +[Bulik-Sullivan, et al. LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies. +Nature Genetics, 2015.](http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3211.html) + +For genetic correlation, please also cite + +Bulik-Sullivan, et al. An Atlas of Genetic Correlations across Human Diseases and Traits. bioRxiv doi: http://dx.doi.org/10.1101/014498 + +For partitioned heritability, please also cite + +Finucane, HK, et al. Partitioning Heritability by Functional Category using GWAS Summary Statistics. bioRxiv doi: http://dx.doi.org/10.1101/014241 + + +##Requirements + +1. `Python 2.7` +2. `argparse 1.2.1` +3. `bitarray 0.8.1` +4. `numpy 1.8.0` +5. `pandas 0.15.0` +6. `scipy 0.10.1` + +##License + +This project is licensed under GNU GPL v3. + + +##Authors + +Brendan Bulik-Sullivan (Broad Institute of MIT and Harvard) + +Hilary Finucane (MIT Department of Mathematics) diff --git a/docs/FAQ b/docs/FAQ deleted file mode 100644 index 9ae5d724..00000000 --- a/docs/FAQ +++ /dev/null @@ -1,272 +0,0 @@ -Frequently Asked Questions -========================== - -General -======= - -Q: Is it cool if I fork / modify / etc LDSC? -A: Of course! - -Q: If I use LDSC in my research, which publications should I cite? -A: If you use LDSC at all, please cite Bulik-Sullivan et al 2014. For the LD Score -regression intercept, please cite Bulik-Sullivan et al 2014. For estimating rg, please -cite Bulik-Sullivan and Fincuane, et al 2015. For partitioned h2 or pathway analysis, -please cite Finucane and Bulik-Sullivan et al 2015. - -LD Score Estimation -=================== - -Q: Should I parallelize over chromosomes when estimating LD Score? -A: Yes. - -Q: How much time/memory does it take to estimate LD Scores? -A: Using a 1cM window, it takes about 1 hour and 1GB RAM on my 1.7GHz Macbook Air to -compute LD Scores for chr 1 using the N=378 1000 Genomes Europeans. The time and memory -complexity are both linear in the number of samples and the number of SNPs. Computing -partitioned LD Scores with a large number of partitions increases the memory usage -(~8GB for 50 categories) but has only minimal impact on the runtime. - -Q: Why are some LD Scores negative? -A: The naive estimator of r2 is biased upwards by approximately 1/N where N is sample -size. The amount of bias per pair of SNPs is small, but when summing over thousands -of r2 estimate, the bias becomes substantial. LDSC therefore uses an unbiased estimator -of r2; which necessarily has the property of returning negative r2 estimates occasionally. -It is normal for a small number of LD Scores to be negative (e.g., a few percent of all -SNPs). This has minimal impact on the regression. If more than a few percent of all SNPs -have negative LD Scores, this probably means that something is wrong, either that the -sample size used for estimating LD Scores is too small or the window size used is too -large. - -Q: Why am I getting the error message -'The .annot file must contain the same SNPs in the same order as the .bim file.' -A: Because your .annot file does not contain the same SNPs in the same order as your -.bim file. - -Merging files on SNP ID's (rs numbers) or chromosomal coordinates is a frequent -source of bugs, so LDSC tries to avoid doing this whenever possible. - -Q: Why am I getting the error message -"Some SNPs have no annotation in --cts-bin. This is a bug!" -A: This error message should never appear. Email me. - -Q: Why am I getting the error message -"Do you really want to compute whole-chomosome LD Score? If so, set the ---yes-really flag (warning: it will use a lot of time / memory)" -A: Your --ld-wind-[snp | kb | cm] option told LDSC to compute whole-chromosome LD Scores, -i.e., sum r^2 with the sum taken over all the SNPs in your .bim file. This is more often -a time-consuming mistake than the real intent of the user (whole-chromosome LD Score -contains more information about population structure in the sample than anything else, -and this information can be obtained much more rapidly via e.g., PCA). If you actually -want to compute sum r^2 with the sum taken over all SNPs in your .bim file, set the ---yes-really flag, but be warned that if you .bed file is large, this could take a very -long time to finish running. - -Q: Why are the entries in my [.M | .M_5_50] file non-integers? -A: If you set any of the flags in the set {--per-allele, --pq-exp, --maf-exp} or you -passed a .annot file with non-integer entries, then this is to be expected. For example, ---per-allele computes LD Scores as sum_k p_k(1-p_k)r^2_jk, where p_k denotes the MAF -of k. The entry in the .M file will be M = sum_k p_k(1-p_k). LDSC does not normalize -by M in order to facilitate parallelization over chromosome. - -Q: Why am I getting the error message -"WARNING: ill-conditioned LD Score Matrix!" -A: This only applies to partitioned LD Scores. This error message means that some of -the columns of the LD Score matrix are linearly dependent or are sufficiently close -to linearly dependent (condition number > 100000) that LDSC will run into numerical -difficulties when attempting to perform LD Score regression with these LD Scores. -There are several reasons why this might occur: if the number of SNPs is smaller than -the number of LD Scores, if the annot matrix has linearly dependent columns or if the number -of categories is very large. Sometimes the LD Score matrix for one of the smaller -chromosomes will be ill-conditioned, but the whole-genome LD Score matrix will be fine. -Increasing the sample size can also help. - - - -Variance Components -=================== - -Q: What sample size do I need for LD Score regression? -A: As a rule of thumb, anything below ~5k samples is too small for even univariate h2 -estimation with LD Score regression, and the results will be very noisy. One needs even -larger sample sizes for asking more complicated questions, e.g., partitioned h2. -If your sample size is so small that SE is the limiting factor, consider using REML. - -Q: Why am I getting the error message -"WARNING: one of the h2\'s was < 0 in one of the jackknife blocks. -Consult the documentation." -A: This warning message means that the LD Score regression slope obtained after deleting -a block of SNPs was < 0. This usually means either that the h2 is very low -or the sample size is very small. You can try changing the number of jackknife blocks -with the --num-blocks flag, but even if this works, it is likely to give an rg estimate -with unusably high standard error. You can override this error message with the ---return-silly-things flag, which will cause LDSC to return silly results without -complaint. - -Q: Why am I getting the error message -"WARNING: asymptotic P-values may not be valid when SE(rg) is very high." -A: The rg p-values reported by LDSC are only valid asymptotically. At low sample -sizes or low heritabilities (i.e., in situations where power to detect rg is very low), -the p-values may be too low. We know of no clear-cut rule describing when this is a problem, -so LDSC gives a warning message whenever the rg standard error is > 0.25. You can override this error message with the ---return-silly-things flag, which will cause LDSC to return silly results without -complaint. - -Q: Why am I getting the error message -"WARNING: One of the h2 estimates was < 0." -A: This means that the slope of the LD Score regression was < 0. -Since rg has sqrt(h2) in the denominator, LDSC cannot estimate rg if one of the -heritabilities is nonnegative, because the square root of a negative number is not -a real number. Negative heritabilites are not physically meaningful, but sometimes -LD Score regression will return a slope, especially if the true value of h2 is low -or the sample size is small and the data are noisy. You can override this error message with the ---return-silly-things flag, which will cause LDSC to return silly results without -complaint. - -Q: Why am I getting the error message -"WARNING: rg was out of bounds." -A: LDSC returns this error message whenever the rg estimate is above 1.25 or below -1.25. -This usually means that one of the h2 estimates was very close to zero. -Since h2 is in the denominator of rg, if h2 is close to zero, rg estimates can blow up. - -Q: Why am I getting the error message -"WARNING: returning silly results because you asked for them." -A: This is because you set the --return-silly-things flag. Setting this flag is usually -a bad idea. - -Q: Why am I getting the error message -"Error parsing summary statistics" -A: Something is funky with your summary statistics. Consult the documentation on -summary statistics formats. Also check for duplicated SNP identifiers in your chisq files. - -Q: Why is by rg standard error so high? -A: The usual culprits are low sample size and low h2 (since h2 is in the denominator -of rg, low h2 makes rg hard to estimate). - -Q: How long does it take to compute rg / h2 with LDSC? -A: The regression itself takes a few seconds. Reading files into memory and merging them -can take a few minutes, especially if you are using a large number of regression SNPs -and many partitioned LD Scores. - -Q: Why is my h2 estimate negative? -A: If your h2 estimate is within a few SE's of zero, this probably just means that the -true h2 is low and/or your sample size is small. Check whether mean chi^2 is above ~1.02. -If no, this means there is very little polygenic signal for LDSC to work with. - -If your h2 estimate is significantly negative and the mean chi^2 is reasonable large, -then something is wrong, either model specification or data processing. Check for -warning messages and data munging errors, then consider employing MAF or LD partitioned -LD Score regression. - -Q: Why is my h2 estimate for a binary disease trait different from previous reports? -A: LDSC reports heritability on the observed scale. Most publications report heritability -on the liability scale, since this allows for comparison across studies with different -proportions of cases and controls, and across diseases with different population -prevalences. Did you convert the observed scale h2 estimate to liability scale? - -Q: Why is my h2 estimate above one? -A: If you ar estimating h2 from an ascertained study of a highly heritable rare polygenic -disease, then it is possible for the observed scale h2 to be above one. If this is not -the case, then something is probably wrong. Liability scale h2 should never be above one. -If the LD Score regression intercept is way -below one, then there might be something wrong with your data or the model. If the LD -Score regression intercept is equal to one, then you may have just used the wrong value -of N or M. - -Q: Why is one of my partitioned h2 estimates negative? -A: If your partitioned h2 estimate is only somewhat negative (a couple SE's), then this -is probably just sampling noise. As an example, suppose we partitioned the genome into -1000 randomly categories. If the true h2 is 0.5, then the partitioned h2 for each category -is ~5e-4. When we estimate partitioned h2 from these data, we are generating 1000 noisy -estimates of the number 5e-4. Some of those estimates will be negative, just by chance. - -If on of your partitioned h2 estimates is significantly negative, then this could indicate -model misspecification bias. Producing well-specified models of functionally partitioned -h2 is hard. See Finucane et al. - -Q: I estimated partitioned LD Score regression using an annot file with overlapping -categories. What do the h2 estimates mean? -A: Nothing. If you are using overlapping categories, you can suppress meaningless -reports with the --overlap-annot flag - -Q: Why is my LD Score regression intercept below one? -A: If the LD Score regression intercept is only a few SE's below one, this may just -be sampling noise. If your summary statistics were generated from GC corrected data -(even data that were only single GC corrected), the intercept should be less than one -(see the Supplementary Note of Bulik-Sullivan et al 2014). If your summary statistcs -contain chi^2 statistics for SNPs with low minor allele count (note: MAC not MAF), then -the chi^2 statistics for these SNPs may be deflated relative to the asymptotic -distribution. Try filtering out low MAF SNPs. Check the mean chi^2. If it is below one, -LDSC won't be of much help (really if it is below ~1.02). - -Q: When should I constrain the intercept? -A: If you are confident in your QC. - -Q: Why is my h2 estimate so low? -A: Double- or single-GC correction both bias the h2 estimate downwards. Check to make -sure that you have the right sample size in your chisq file. Most GWAS provide summary -statistics from their discovery sample, but mention their replication sample size in -the abstract, since the replication sample size is always larger and more impressive- -sounding. If you mistakenly use the replication sample size in your chisq file, this -will bias your h2 estimate downwards. - -This may also result from model misspecification bias. If the LD Score regression -intercept is much higher than one despite trustworthy QC, this can indicate that the -model of genetic architecture used when computing LD Scores does not fit the data well. - -If you are comparing an h2 estimate from GWAS data to an h2 estimate from a twin study, -you should be aware that these are different quantities (twin study h2 includes the -contributions of all genetic variation, not just common SNPs), and twin study h2 is -probably biased upwards (see Zuk, et al PNAS 2012). Almost all h2 estimates from -GWAS data have been much lower than h2 estimates from twin data. - -Q: Why is the SE on my h2 estimate so high? -A: The common culprits are small sample size, small number of regression SNPs, -too many partitioned LD Scores and not excluding huge-effect loci. - -Q: Why am I getting the warning message -"WARNING: number of SNPs less than 200k; this is almost always bad." -A: Having fewer than 200k regression SNPs is often a sign that something went wrong -when merging summary statistics and LD Score - -LD Score regression is designed to be used with > 1 million SNPs included in the -regression. There is nothing wrong with using a smaller number of SNPs (e.g., -600k SNPs off a genotyping array), though the standard error will tend to be lower -with a larger set of regression SNPs. - -Q: I used [immuno | metabo | psych | exome]-chip data and got funky results. What gives? -A: LD Score regression doesn't work well with targeted genotyping arrays. Email us if -you would like to think about how to improve this. - -Q: Why does LDSC filter out SNPs with very high chi^2? -A: Linear regression deals badly with outliers; huge effect SNPs will increase the SE -enormously. It is easy to estimate h2 and rg from a small subset of large-effect SNPs, -so if you are lucky enough to have a large number of large-effect SNPs, -the best approach is to estimate h2 and rg among the polygenic dust using LD Score -regression (excluding huge effect SNPs), then combine these numbers with h2 and rg -estimated from huge effect SNPs - -Q: I am estimating rg between the same trait using the same set of summary statistics -twice, and LDSC is not returning 1. What is wrong? -A: By default, LDSC weights the two-phenotype LD Score regression assuming no sample -overlap. If there is sample overlap, then these regression weights are inefficient, -so plugging the same set of summary statistics into LDSC twice and trying to estimate -rg returns the slope from an inefficiently weighted LD Score regression divided by -the slope from an efficiently weighted LD Score regression, i.e., a noisy estimate of 1. - -If you set the flags --overlap [number of samples] --rho 1, then you should get 1 exactly. - -Q: I am estimating rg between the same trait using summary statistics from different -studies, and LDSC is not returning 1. What is wrong? -A: This may not be an error. The genetic correlation between different definition of the -same phenotype, or the same phenotype in different countries need not be one, so this -could potentially be an interesting result. There are also some less exciting explanations. -For example, the genetic correlation for x and log(x) will be less than 1; the genetic -correlation between WHR and WHR adjusted for BMI is not 1, etc. After excluding obvious -data-munging errors, it would be a good idea to look carefully at the phenotype definition -in each study. - -Q: Why is my rg estimate not exactly equal to gencov / sqrt(h2_1 * h2_2) ? -A: LDSC uses the block jackknife estimate of the ratio. This should generally be very -close to the naive estimate of the ratio. If it is not, this usually means that one of -the h2's not significantly different from zero and the various ratio estimates are not -behaving asymptotically (this is bad) diff --git a/docs/file_formats_ld b/docs/file_formats_ld deleted file mode 100644 index 984436cf..00000000 --- a/docs/file_formats_ld +++ /dev/null @@ -1,117 +0,0 @@ -This document describes all new file formats introduced for use with the --l1, --l1sq, ---l2 and --l4 flags in the ldsc.py software package. - -Glossary - -(0) bed/bim/fam -- see Plink documentation http://pngu.mgh.harvard.edu/~purcell/plink/ -(1) annot -(2) ldscore, lN.M -(3) jknife.ldscore -(4) bin/ind/snp - -*NOTE* chromosomes are assumed to be integers. This software does not yet deal with sex -chromosomes. - - -(1) annot ---------- -With header. Columns are -(0) CHR -- chromosome -(1) BP -- physical position (base pairs) -(2) SNP -- SNP identifier (RS number) -(3) CM -- genetic position in centimorgans -(4 - end) -- Annotations - -Note that one generally thinks of annotations as binary (e.g., DHS = 1 if a SNP is in a -DHS peak and 0 otherwise), but ldsc.py stores annotations as floats for forwards -compatibility with future LD Scores (e.g., weighted LD Scores instead of partitioned LD -Scores). - -Example: -CHR BP SNP CM AN1 AN2 -1 1 rs1 0 1 0 -1 2 rs2 0 1 0 -1 3 rs3 0 0 1 - - -(2.1) ldscore -------------- -Each .ldscore field should always be paired with a .ldscore.M file. Files ending in -.ldscore contain LD Scores (and optionally standard errors); files ending in .ldscore.M -contain information about thenumber of SNPs per annotation. Both file formats are -whitespace-delimited text. - -.ldscore files have a header and one SNP per row. The columns are -(0) CHR -- chromosome -(1) BP -- physical position (base pairs) -(2) SNP -- SNP identifier (RS number) -(3) MAF -- minor allele frequency -(4 - end) -- LD Scores - -The CM column can be safely set to zero. Genetic positions are only used by the ---ld-wind-cm flag, which reads cM coordinates from the .bim or .snp file. - -Example: -CHR SNP BP CM MAF LD -22 rs146752890 16050612 0 0.06728232189973615 71.0634336207099 -22 rs139377059 16050678 0 0.0554089709762533 100.06863866975021 -22 rs6518357 16051107 0 0.05936675461741425 108.64701224811621 -22 rs62224609 16051249 0 0.08970976253298157 20.11209132360909 -22 rs62224610 16051347 0 0.3337730870712401 19.593657102402894 -22 rs143503259 16051453 0 0.09366754617414252 21.340927392797948 - - -(2.2) lN.M ---------------- -One line, # of columns = number of annotations in the accompanying .ldscore file in the -same order. Each column contains the number of SNPs in the corresponding annotation -category. - -Example: -100 1000 - - -(3) jknife.ldscore ------------------- -Same as ldscore files, except each LD Score column is followed by a standard error column. -If C is the name of the LD Score column, the name of the standard error column will be -SE(C). - -Example: -CHR SNP BP CM MAF L2 SE(L2) -1 rs_0 1 0 0.3025 1.9864004626159044 0.12269017939910966 -1 rs_0_M 2 0 0.3025 1.9864004626159044 0.12269017939910966 -1 rs_1 3 0 0.3035 2.0083733247639373 0.11311973299680031 - - -(4.1) bin ---------- -The .bin format is a binary format for storing phased best-guess genotypes with no missing -data. Genotypes are stored as a magic number followed by a bitstring with between 0 and 7 -zeros padding the end of the file so that the length of the file in bits is divisible by -8. One bits correspond to the reference allele in .snp; zero bits correspond to the non- -reference allele - -The magic number is given by ->>> import bitarray as ba ->>> ldsc = ba.bitarray(endian="big") ->>> ldsc.fromstring('ldsc') ->>> print ldsc -bitarray('01101100011001000111001101100011') - - -(4.2) ind ---------- -This file format has no header, and a single column consisting of individual IDs. - - -(4.3) snp ---------- -This file format is the same as the eigenstrat .snp format. No header, with columns -(0) chromosome -(1) physical coordinate in base pairs -(2) SNP ID (rs number) -(3) genetic position in centimorgans - - - diff --git a/docs/file_formats_sumstats.txt b/docs/file_formats_sumstats.txt deleted file mode 100644 index 9c494cb6..00000000 --- a/docs/file_formats_sumstats.txt +++ /dev/null @@ -1,44 +0,0 @@ -This document describes all new file formats introduced for use with the --sumstats* flags -in the ldsc.py software package. - -Contents -(0) ldscore, M -- see file_formats_ld.txt -(1) chisq -(2) allele - -*NOTE* chromosomes are assumed to be integers. This software does not yet deal with sex -chromosomes. - -*NOTE* gzip your chisq / allele files - -(1) chisq ---------- -This file format is used for chi-square statistics or P-values for a single GWAS. Tab- -delimited text, one row per SNP with a header row. The first four columns are the same as -the first four columns of a Plink .bim file: - -Required -(0) CHR -- chromosome -(1) SNP -- SNP identifier (e.g., rs number. Non-unique identifiers, NA's or .'s will - result in a ValueError being raised). -(2) CM -- genetic position (centimorgans). Set to zero by default. -(3) BP -- physical position (base pairs). -(4) N -- sample size (which may vary from SNP to SNP). -(5) CHISQ or P -- GWAS summary statistic. ldsc.py will automatically convert P-values to - chi-square statistics. The advantage of using chi-square statistics is that many - statistical software packages have limited ability to deal with very low p-values (e.g., - will report all P-values less than 10e-303 as 0, which will result in ldsc.py raising - a ValueError). - -Optional -(6) INFO -- imputation quality (for use with the --info-min and --info-max flags) -(7) MAF -- study minor allele frequency (for use with the --maf flag. ldsc.py will - automatically convert frequencies in [0,1] to minor allele frequencies in [0,0.5]) - - -(2) allele ------------ - -Required -(1) SNP -(2) INC_ALLELE -- trait increasing allele (for QT) OR risk allele \ No newline at end of file diff --git a/docs/tutorial b/docs/tutorial deleted file mode 100644 index 7b7f6c00..00000000 --- a/docs/tutorial +++ /dev/null @@ -1,161 +0,0 @@ -This tutorial assumes that you have downloaded plink: - https://www.cog-genomics.org/plink2/ - -All commands should be called from the ldsc/ directory. - -1. Estimating LD Scores -======================= - -1.1 Single LD Scores --------------------- - -$ python ldsc.py --bfile tutorial/plink --l2 --ld-wind-snp 10 --out tutorial/ld - -This should run almost instantaneously, and will produce four files - -(a) ld.l2.ldscore.gz -- LD Scores -(b) ld.l2.M -- # of SNPs in the .bed file -(c) ld.l2.M_5_50 -- # of SNPs with MAF between 5% and 50% in the .bed file -(d) ld.l2.log -- log file - -The log file contains some useful summary information about your LD Scores. - -When estimating LD Scores from real data, it is advisable to parallelize -over chromosomes. - -You will get better results using --ld-wind-cm 1; however, -this requires a .bim file with centiMorgan coordinates. We provide a script -(interpolate_cm.py) which takes as input a .bim file and a SHAPEIT genetic map -(see https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html) -and produces a .bim file with the cM column filled in. This can also be accomplished -via the --cm-map flag in plink. - -1.2 Partitioned LD Scores -------------------------- - -1.2.1 From annot matrix - -The annot matrix in tutorial/ldsc.annot contains two annotations, even SNPs and odd -SNPs (this isn't a sensible thing to do with real data, just an example). -You can compute even/odd partitioned LD Scores with the following command - -$ python ldsc.py --bfile tutorial/plink --l2 --ld-wind-snp 10 --annot tutorial/ ldsc.annot --out ld - -1.2.2 Binning continuous annotations - -The file tutorial/one.cts contains a trivial continuous annotation correp - -python ldsc.py --bfile plink --l2 --ld-wind-cm 1 --cts-bin cts.txt ---cts-breaks 10,20 --cts-names ANNOT --out ld - -python ldsc.py --bfile plink --l2 --ld-wind-cm 1 --cts-bin cts1.txt,cts2,txt ---cts-breaks 10,20x10,40 --cts-names ANNOT1,ANNOT2 --out ld - - -2. Variance Components -====================== - -2.1 Summary Statistics ----------------------- - -2.1.1 Minimum Requirements --------------------------- - -The minimum summary data required for estimating h2 or rg with LD Score -regression are the following: - -(1) Genome-wide summary statistics from cohorts with similar ancestry -(2) The summary statistics must be signed (allele and direction of effect) for rg - estimation. This is not necessary for h2 or partitioned h2 estimations. -(3) The summary statistics should not be "corrected" via genomic control (GC) correction. - Using GC'ed summary statistics will result in downward bias in the LD Score regression - estimates of heritability and genetic covariance, and deflated LD Score regression - intercepts. Estimates of genetic correlation will not be affected. -(4) The summary statistics should not be meta-analyzed with targeted genotyping at - significant loci (e.g., specialty genotyping arrays like immunochip, exome chip, - psychchip, metabochip, or replication cohorts). LD Score regression is currently not - applicable to data generated using custom genotyping arrays - -The next details are convenient, but are only used for filtering SNPs, and so are not -strictly necessary: - -(1) A measure of imputation quality (e.g., INFO) for each SNP -(2) Sample size at each SNP (for binary traits, number of cases and number of controls) -(3) Sample MAF - -Alternatively, one can simply share a list of SNPs passing reasonable MAF and INFO cutoffs -(usually sample MAF above 3% and INFO above 0.9). - -If these data are not available, we recommend retaining only HM3 SNPs with reference panel -MAF above 3% for the LD Score regression as a workaround (note: for regression, not for -estimation of the LD Scores), since HM3 SNPs seem to be well-imputed in most studies. - -For newer studies with dense imputation, restricting to HM3 SNPs is an inefficient use of -data. Using a larger set of SNPs for the regression will lower the standard error using -all common 1kG SNPs instead of all common HM3 SNPs for the regression reduces the standard -error by about 10% in simulations). - -2.1.2 Reformatting Summary Statistics -------------------------------------- - -python sumstats_to_chisq.py --sumstats plink.qassoc --N 1000 --out test - - - -2.2 Heritability ----------------- - -2.2.1 Univariate Heritability - -python ldsc.py --h2 sumstats.chisq.gz --w-ld-chr w_ld --ref-ld-chr ld --out h2 - -2.2.2 Partitioned Heritability - -python ldsc.py --h2 sumstats.chisq.gz --w-ld-chr w_ld --ref-ld-chr part_ld --out h2 - -2.2.3 Partitioned Heritability with Overlapping Categories - -python ldsc.py --h2 sumstats.chisq.gz --w-ld-chr w_ld --ref-ld-chr overlap_ld --overlap-annot ---out h2 - -2.2.4 Adding Categories to the Baseline Model from Finucane, et al - -2.2.5 Constraining the Intercept - -To constrain the intercept to equal 1, simply append --no-intercept to any of the above -commands. To constrain the intercept to a number different from one, say 0.9, append ---constrain-intercept 0.9 to any of the above commands. - -WARNING: only constrain the intercept if you trust the QC. LD Score regression with -constrained intercept is not protected from population stratification or cryptic -relatedness. However, PCA provides much better protection from population stratification -than the LD Score regression intercept does. - -WARNING: do NOT constrain the intercept to one when working with GC corrected data. -For GC corrected data, the expected intercept is less than one (see the Supplementary -Note of Bulik-Sullivan et al 2014). - -2.3 Genetic Correlation ------------------------ - -2.3.1 Basic Genetic Correlation - -python ldsc.py --rg sumstats1,sumstats2 --w-ld-chr w_ld --ref-ld-chr overlap_ld --out rg - -2.3.2 Modifying the Regression Weights for Overlapping Samples - -python ldsc.py --rg sumstats1,sumstats2 --w-ld-chr w_ld --ref-ld-chr overlap_ld --out rg ---overlap 10000 --rho 0.4 - -2.3.2 Constraining the Intercept - -To constrain the intercepts to equal 1,1,0, simply append --no-intercept to any of the above -commands. To constrain the intercepts to a different set of numbers, say 1,1,4000, then -append --constrain-intercept 1,1,4000 to the above commands. - -NOTE: Constraining the intercept will not automatically set the regression weights -properly. In the above example with 10000 overlapping samples and a phenotypic correlation -of 0.4, the full command should be - -python ldsc.py --rg sumstats1,sumstats2 --w-ld-chr w_ld --ref-ld-chr overlap_ld --out rg ---overlap 10000 --rho 0.4 --constrain-intercept 1,1,4000 diff --git a/interpolate_cm.py b/interpolate_cm.py deleted file mode 100644 index 73172b28..00000000 --- a/interpolate_cm.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import division -import pandas as pd -import numpy as np -import argparse - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument('--bim', default=None, type=str, - help='.bim file prefix (.bim is appended)') - parser.add_argument('--map', default=None, type=str, - help='SHAPEIT map file') - parser.add_argument('--out', default=None, type=str, - help='Output file prefix (.bim is appended)') - args = parser.parse_args() - - if args.bim is None or args.map is None or args.out is None: - raise ValueError('Must set all of --bim, --map, --out.') - - args.bim += '.bim' - print 'Reading bim file at {B}'.format(B=args.bim) - bim_array = pd.read_csv(args.bim, header=None, delim_whitespace=True).values - print 'Reading SHAPEIT map file at {M}'.format(M=args.map) - map_array = pd.read_csv(args.map, header=0, usecols=[0,2], delim_whitespace=True).values - if not np.all(np.diff(map_array[:,0] > 0)): - print 'Map file not sorted by physical coordinate. Sorting.' - map_array = map_array[map_array[:,0].argsort()] - - min_cm = np.min(map_array[:,1]) - max_cm = np.max(map_array[:,1]) - print 'Interpolating centimorgan coordinates' - bim_cm = np.interp(bim_array[:,3].astype(float), map_array[:,0], map_array[:,1], left=min_cm, - right=max_cm) - bim_array[:,2] = bim_cm - print 'Writing output to {O}'.format(O=args.out) - bim_array = pd.DataFrame(bim_array) - bim_array.to_csv(args.out, header=False, index=False, sep='\t') \ No newline at end of file diff --git a/ldsc.py b/ldsc.py index 7d24748a..5f672760 100644 --- a/ldsc.py +++ b/ldsc.py @@ -1,36 +1,31 @@ ''' (c) 2014 Brendan Bulik-Sullivan and Hilary Finucane -This is a command line application for estimating - 1. LD Score and friends (L1, L1^2, L2 and L4) - 2. heritability / partitioned heritability - 3. genetic covariance - 4. genetic correlation - 5. block jackknife standard errors for all of the above. - - +LDSC is a command line tool for estimating + 1. LD Score + 2. heritability / partitioned heritability + 3. genetic covariance / correlation + ''' from __future__ import division import ldscore.ldscore as ld import ldscore.parse as ps -import ldscore.jackknife as jk import ldscore.sumstats as sumstats -import argparse +import ldscore.regressions as reg import numpy as np import pandas as pd from subprocess import call from itertools import product +import time, sys, traceback, argparse -__version__ = '0.0.2 (alpha)' - +__version__ = '1.0.0' MASTHEAD = "*********************************************************************\n" MASTHEAD += "* LD Score Regression (LDSC)\n" MASTHEAD += "* Version {V}\n".format(V=__version__) -MASTHEAD += "* (C) 2014 Brendan Bulik-Sullivan and Hilary Finucane\n" +MASTHEAD += "* (C) 2014-2015 Brendan Bulik-Sullivan and Hilary Finucane\n" MASTHEAD += "* Broad Institute of MIT and Harvard / MIT Department of Mathematics\n" MASTHEAD += "* GNU General Public License v3\n" MASTHEAD += "*********************************************************************\n" - pd.set_option('display.max_rows', 500) pd.set_option('display.max_columns', 500) pd.set_option('display.width', 1000) @@ -40,919 +35,594 @@ np.set_printoptions(precision=4) +def sec_to_str(t): + '''Convert seconds to days:hours:minutes:seconds''' + [d, h, m, s, n] = reduce(lambda ll, b : divmod(ll[0], b) + ll[1:], [(t, 1), 60, 60, 24]) + f = '' + if d > 0: + f += '{D}d:'.format(D=d) + if h > 0: + f += '{H}h:'.format(H=h) + if m > 0: + f += '{M}m:'.format(M=m) + + f += '{S}s'.format(S=s) + return f + + def _remove_dtype(x): - '''Removes dtype: float64 and dtype: int64 from pandas printouts''' - x = str(x) - x = x.replace('\ndtype: int64','') - x = x.replace('\ndtype: float64','') - return x - - -class logger(object): - ''' - Lightweight logging. - - TODO: replace with logging module - - ''' - - def __init__(self, fh): - self.log_fh = open(fh, 'wb') - - def log(self, msg): - ''' - Print to log file and stdout with a single command. - - ''' - print >>self.log_fh, msg - print msg - + '''Removes dtype: float64 and dtype: int64 from pandas printouts''' + x = str(x) + x = x.replace('\ndtype: int64', '') + x = x.replace('\ndtype: float64', '') + return x + + +class Logger(object): + ''' + Lightweight logging. + TODO: replace with logging module + + ''' + def __init__(self, fh): + self.log_fh = open(fh, 'wb') + + def log(self, msg): + ''' + Print to log file and stdout with a single command. + + ''' + print >>self.log_fh, msg + print msg + def __filter__(fname, noun, verb, merge_obj): - merged_list = None - if fname: - f = lambda x,n: x.format(noun=noun, verb=verb, fname=fname, num=n) - x = ps.FilterFile(fname) - c = 'Read list of {num} {noun} to {verb} from {fname}' - print f(c, len(x.IDList)) - merged_list = merge_obj.loj(x.IDList) - len_merged_list = len(merged_list) - if len_merged_list > 0: - c = 'After merging, {num} {noun} remain' - print f(c, len_merged_list) - else: - error_msg = 'No {noun} retained for analysis' - raise ValueError(f(error_msg, 0)) - - return merged_list - - -def annot_sort_key(s): - '''For use with --cts-bin. Fixes weird pandas crosstab column order.''' - if type(s) == tuple: - s = [x.split('_')[0] for x in s] - s = map(lambda x: float(x) if x != 'min' else -float('inf'), s) - else: #type(s) = str: - s = s.split('_')[0] - if s == 'min': - s = float('-inf') - else: - s = float(s) - - return s - - -def ldscore(args, header=None): - ''' - Wrapper function for estimating l1, l1^2, l2 and l4 (+ optionally standard errors) from - reference panel genotypes. - - Annot format is - chr snp bp cm - - ''' - log = logger(args.out+'.log') - if header: - log.log(header) - #log.log(args) - - #if args.bin: - # snp_file, snp_obj = args.bin+'.bim', ps.PlinkBIMFile - # ind_file, ind_obj = args.bin+'.ind', ps.VcfINDFile - # array_file, array_obj = args.bin+'.bin', ld.VcfBINFile - if args.bfile: - snp_file, snp_obj = args.bfile+'.bim', ps.PlinkBIMFile - ind_file, ind_obj = args.bfile+'.fam', ps.PlinkFAMFile - array_file, array_obj = args.bfile+'.bed', ld.PlinkBEDFile - - # read bim/snp - array_snps = snp_obj(snp_file) - m = len(array_snps.IDList) - log.log('Read list of {m} SNPs from {f}'.format(m=m, f=snp_file)) - - # read --annot - if args.annot is not None: - annot = ps.AnnotFile(args.annot) - num_annots, ma = len(annot.df.columns) - 4, len(annot.df) - log.log("Read {A} annotations for {M} SNPs from {f}".format(f=args.annot, - A=num_annots, M=ma)) - annot_matrix = np.array(annot.df.iloc[:,4:]) - annot_colnames = annot.df.columns[4:] - keep_snps = None - if np.any(annot.df.SNP.values != array_snps.df.SNP.values): - raise ValueError('The .annot file must contain the same SNPs in the same'+\ - ' order as the .bim file.') - # read --extract - elif args.extract is not None: - keep_snps = __filter__(args.extract, 'SNPs', 'include', array_snps) - annot_matrix, annot_colnames, num_annots = None, None, 1 - - # read cts_bin_add - elif args.cts_bin_add is not None and args.cts_breaks is not None: - # read filenames - cts_fnames = args.cts_bin_add.split(',') - # read breaks - # replace N with negative sign - args.cts_breaks = args.cts_breaks.replace('N','-') - # split on x - try: - breaks = [[float(x) for x in y.split(',')] for y in args.cts_breaks.split('x')] - except ValueError as e: - raise ValueError('--cts-breaks must be a comma-separated list of numbers: ' - +str(e.args)) - - if len(breaks) != len(cts_fnames): - raise ValueError('Need to specify one set of breaks for each file in --cts-bin.') - - if args.cts_names: - cts_colnames = [str(x) for x in args.cts_names.split(',')] - if len(cts_colnames) != len(cts_fnames): - msg = 'Must specify either no --cts-names or one value for each file in --cts-bin.' - raise ValueError(msg) - - else: - cts_colnames = ['ANNOT'+str(i) for i in xrange(len(cts_fnames))] - - log.log('Reading numbers with which to bin SNPs from {F}'.format(F=args.cts_bin_add)) - - cts_levs = [] - full_labs = [] - first_lev = np.zeros((m,)) - for i,fh in enumerate(cts_fnames): - vec = ps.read_cts(cts_fnames[i], array_snps.df.SNP.values) - - max_cts = np.max(vec) - min_cts = np.min(vec) - cut_breaks = list(breaks[i]) - name_breaks = list(cut_breaks) - if np.all(cut_breaks >= max_cts) or np.all(cut_breaks <= min_cts): - raise ValueError('All breaks lie outside the range of the cts variable.') - - if np.all(cut_breaks <= max_cts): - name_breaks.append(max_cts) - cut_breaks.append(max_cts+1) - - if np.all(cut_breaks >= min_cts): - name_breaks.append(min_cts) - cut_breaks.append(min_cts-1) - - name_breaks.sort() - cut_breaks.sort() - n_breaks = len(cut_breaks) - # so that col names are consistent across chromosomes with different max vals - name_breaks[0] = 'min' - name_breaks[-1] = 'max' - name_breaks = [str(x) for x in name_breaks] - labs = [name_breaks[i]+'_'+name_breaks[i+1] for i in xrange(n_breaks-1)] - cut_vec = pd.Series(pd.cut(vec, bins=cut_breaks, labels=labs)) - full_labs.append(labs) - small_annot_matrix = cut_vec - # crosstab -- for now we keep empty columns - small_annot_matrix = pd.crosstab(small_annot_matrix.index, - small_annot_matrix, dropna=False) - small_annot_matrix = small_annot_matrix[sorted(small_annot_matrix.columns, key=annot_sort_key)] - cts_levs.append(small_annot_matrix.ix[:,1:]) - # first column defaults to no annotation - first_lev += small_annot_matrix.ix[:,0] - - if len(cts_colnames) == 1: - annot_colnames = [cts_colnames[0]+'_'+bin for bin in full_labs[0]] - else: - annot_colnames = [] - for i,cname in enumerate(cts_colnames): - for bin in full_labs[i][1:]: - annot_colnames.append(cts_colnames[i]+'_'+bin) - - annot_colnames.insert(0, "BOTTOM_BINS") - first_lev = np.minimum(first_lev, 1) - cts_levs.insert(0, pd.DataFrame(first_lev)) - annot_matrix = pd.concat(cts_levs, axis=1) - annot_matrix = np.matrix(annot_matrix) - keep_snps = None - num_annots = annot_matrix.shape[1] - - # read --cts-bin plus --cts-breaks - elif args.cts_bin is not None and args.cts_breaks is not None: - # read filenames - cts_fnames = args.cts_bin.split(',') - # read breaks - # replace N with negative sign - args.cts_breaks = args.cts_breaks.replace('N','-') - # split on x - try: - breaks = [[float(x) for x in y.split(',')] for y in args.cts_breaks.split('x')] - except ValueError as e: - raise ValueError('--cts-breaks must be a comma-separated list of numbers: ' - +str(e.args)) - - if len(breaks) != len(cts_fnames): - raise ValueError('Need to specify one set of breaks for each file in --cts-bin.') - - if args.cts_names: - cts_colnames = [str(x) for x in args.cts_names.split(',')] - if len(cts_colnames) != len(cts_fnames): - msg = 'Must specify either no --cts-names or one value for each file in --cts-bin.' - raise ValueError(msg) - - else: - cts_colnames = ['ANNOT'+str(i) for i in xrange(len(cts_fnames))] - - log.log('Reading numbers with which to bin SNPs from {F}'.format(F=args.cts_bin)) - - cts_levs = [] - full_labs = [] - for i,fh in enumerate(cts_fnames): - vec = ps.read_cts(cts_fnames[i], array_snps.df.SNP.values) - - max_cts = np.max(vec) - min_cts = np.min(vec) - cut_breaks = list(breaks[i]) - name_breaks = list(cut_breaks) - if np.all(cut_breaks >= max_cts) or np.all(cut_breaks <= min_cts): - raise ValueError('All breaks lie outside the range of the cts variable.') - - if np.all(cut_breaks <= max_cts): - name_breaks.append(max_cts) - cut_breaks.append(max_cts+1) - - if np.all(cut_breaks >= min_cts): - name_breaks.append(min_cts) - cut_breaks.append(min_cts-1) - - name_breaks.sort() - cut_breaks.sort() - n_breaks = len(cut_breaks) - # so that col names are consistent across chromosomes with different max vals - name_breaks[0] = 'min' - name_breaks[-1] = 'max' - name_breaks = [str(x) for x in name_breaks] - labs = [name_breaks[i]+'_'+name_breaks[i+1] for i in xrange(n_breaks-1)] - cut_vec = pd.Series(pd.cut(vec, bins=cut_breaks, labels=labs)) - cts_levs.append(cut_vec) - full_labs.append(labs) - - annot_matrix = pd.concat(cts_levs, axis=1) - annot_matrix.columns = cts_colnames - # crosstab -- for now we keep empty columns - annot_matrix = pd.crosstab(annot_matrix.index, - [annot_matrix[i] for i in annot_matrix.columns], dropna=False, - colnames=annot_matrix.columns) - - # add missing columns - if len(cts_colnames) > 1: - for x in product(*full_labs): - if x not in annot_matrix.columns: - annot_matrix[x] = 0 - else: - for x in full_labs[0]: - if x not in annot_matrix.columns: - annot_matrix[x] = 0 - - annot_matrix = annot_matrix[sorted(annot_matrix.columns, key=annot_sort_key)] - if len(cts_colnames) > 1: - # flatten multi-index - annot_colnames = ['_'.join([cts_colnames[i]+'_'+b for i,b in enumerate(c)]) - for c in annot_matrix.columns] - else: - annot_colnames = [cts_colnames[0]+'_'+b for b in annot_matrix.columns] - - annot_matrix = np.matrix(annot_matrix) - keep_snps = None - num_annots = len(annot_colnames) - if np.any(np.sum(annot_matrix, axis=1) == 0): - # This exception should never be raised. For debugging only. - raise ValueError('Some SNPs have no annotation in --cts-bin. This is a bug!') - - else: - annot_matrix, annot_colnames, keep_snps = None, None, None, - num_annots = 1 - - # read fam/ind - array_indivs = ind_obj(ind_file) - n = len(array_indivs.IDList) - log.log('Read list of {n} individuals from {f}'.format(n=n, f=ind_file)) - # read keep_indivs - if args.keep: - keep_indivs = __filter__(args.keep, 'individuals', 'include', array_indivs) - else: - keep_indivs = None - - # read genotype array - log.log('Reading genotypes from {fname}'.format(fname=array_file)) - geno_array = array_obj(array_file, n, array_snps, keep_snps=keep_snps, - keep_indivs=keep_indivs, mafMin=args.maf) - - # filter annot_matrix down to only SNPs passing MAF cutoffs - if annot_matrix is not None: - annot_keep = geno_array.kept_snps - annot_matrix = annot_matrix[annot_keep,:] - - # determine block widths - x = np.array((args.ld_wind_snps, args.ld_wind_kb, args.ld_wind_cm), dtype=bool) - if np.sum(x) != 1: - raise ValueError('Must specify exactly one --ld-wind option') - - if args.ld_wind_snps: - max_dist = args.ld_wind_snps - coords = np.array(xrange(geno_array.m)) - elif args.ld_wind_kb: - max_dist = args.ld_wind_kb*1000 - coords = np.array(array_snps.df['BP'])[geno_array.kept_snps] - elif args.ld_wind_cm: - max_dist = args.ld_wind_cm - coords = np.array(array_snps.df['CM'])[geno_array.kept_snps] - - block_left = ld.getBlockLefts(coords, max_dist) - if block_left[len(block_left)-1] == 0 and not args.yes_really: - error_msg = 'Do you really want to compute whole-chomosome LD Score? If so, set the ' - error_msg += '--yes-really flag (warning: it will use a lot of time / memory)' - raise ValueError(error_msg) - - scale_suffix = '' - if args.pq_exp is not None: - log.log('Computing LD with pq ^ {S}.'.format(S=args.pq_exp)) - msg = 'Note that LD Scores with pq raised to a nonzero power are' - msg += 'not directly comparable to normal LD Scores.' - log.log(msg) - scale_suffix = '_S{S}'.format(S=args.pq_exp) - pq = np.matrix(geno_array.maf*(1-geno_array.maf)).reshape((geno_array.m,1)) - pq = np.power(pq, args.pq_exp) - - if annot_matrix is not None: - annot_matrix = np.multiply(annot_matrix, pq) - else: - annot_matrix = pq - - elif args.maf_exp is not None: - log.log('Computing LD with MAF ^ {S}.'.format(S=args.maf_exp)) - msg = 'Note that LD Scores with MAF raised to a nonzero power are' - msg += 'not directly comparable to normal LD Scores.' - log.log(msg) - scale_suffix = '_S{S}'.format(S=args.maf_exp) - mf = np.matrix(geno_array.maf).reshape((geno_array.m,1)) - mf = np.power(mf, args.maf_exp) - - if annot_matrix is not None: - annot_matrix = np.multiply(annot_matrix, mf) - else: - annot_matrix = mf - -# if args.se: # block jackknife -# -# # block size -# if args.block_size: -# jSize = args.block_size -# elif n > 50: -# jSize = 10 -# else: -# jSize = 1 -# -# jN = int(np.ceil(n / jSize)) -# if args.l1: -# col_prefix = "L1"; file_suffix = "l1.jknife" -# raise NotImplementedError('Sorry, havent implemented L1 block jackknife yet.') -# -# elif args.l1sq: -# col_prefix = "L1SQ"; file_suffix = "l1sq.jknife" -# raise NotImplementedError('Sorry, havent implemented L1^2 block jackknife yet.') -# -# elif args.l2: -# col_prefix = "L2"; file_suffix = "l2.jknife" -# c = "Computing LD Score (L2) and block jackknife standard errors with {n} blocks." -# -# elif args.l4: -# col_prefix = "L4"; file_suffix = "l4.jknife" -# c = "Computing L4 and block jackknife standard errors with {n} blocks." -# -# print c.format(n=jN) -# (lN_est, lN_se) = geno_array.ldScoreBlockJackknife(block_left, args.chunk_size, jN=jN, -# annot=annot_matrix) -# lN = np.c_[lN_est, lN_se] -# if num_annots == 1: -# ldscore_colnames = [col_prefix+scale_suffix, 'SE('+col_prefix+scale_suffix+')'] -# else: -# ldscore_colnames = [x+col_prefix+scale_suffix for x in annot_colnames] -# ldscore_colnames += ['SE('+x+scale_suffix+')' for x in ldscore_colnames] - -# else: # not block jackknife -# if args.l1: -# log.log("Estimating L1.") -# lN = geno_array.l1VarBlocks(block_left, args.chunk_size, annot=annot_matrix) -# col_prefix = "L1"; file_suffix = "l1" -# -# elif args.l1sq: -# log.log("Estimating L1 ^ 2.") -# lN = geno_array.l1sqVarBlocks(block_left, args.chunk_size, annot=annot_matrix) -# col_prefix = "L1SQ"; file_suffix = "l1sq" -# -# elif args.l2: -# log.log("Estimating LD Score (L2).") -# lN = geno_array.ldScoreVarBlocks(block_left, args.chunk_size, annot=annot_matrix) -# col_prefix = "L2"; file_suffix = "l2" -# -# elif args.l4: -# col_prefix = "L4"; file_suffix = "l4" -# raise NotImplementedError('Sorry, havent implemented L4 yet. Try the jackknife.') -# lN = geno_array.l4VarBlocks(block_left, c, annot) - - log.log("Estimating LD Score.") - lN = geno_array.ldScoreVarBlocks(block_left, args.chunk_size, annot=annot_matrix) - col_prefix = "L2"; file_suffix = "l2" - - if num_annots == 1: - ldscore_colnames = [col_prefix+scale_suffix] - else: - ldscore_colnames = [x+col_prefix+scale_suffix for x in annot_colnames] - - # print .ldscore - # output columns: CHR, BP, CM, RS, MAF, [LD Scores and optionally SEs] - out_fname = args.out + '.' + file_suffix + '.ldscore' - new_colnames = geno_array.colnames + ldscore_colnames - df = pd.DataFrame.from_records(np.c_[geno_array.df, lN]) - df.columns = new_colnames - if args.print_snps: - if args.print_snps.endswith('gz'): - print_snps = pd.read_csv(args.print_snps, header=None, compression='gzip') - elif args.print_snps.endswith('bz2'): - print_snps = pd.read_csv(args.print_snps, header=None, compression='bz2') - else: - print_snps = pd.read_csv(args.print_snps, header=None) - if len(print_snps.columns) > 1: - raise ValueError('--print-snps must refer to a file with a one column of SNP IDs.') - log.log('Reading list of {N} SNPs for which to print LD Scores from {F}'.format(\ - F=args.print_snps, N=len(print_snps))) - - print_snps.columns=['SNP'] - df = df.ix[df.SNP.isin(print_snps.SNP),:] - if len(df) == 0: - raise ValueError('After merging with --print-snps, no SNPs remain.') - else: - msg = 'After merging with --print-snps, LD Scores for {N} SNPs will be printed.' - log.log(msg.format(N=len(df))) - - if not args.pickle: - l2_suffix = '.gz' - log.log("Writing LD Scores for {N} SNPs to {f}.gz".format(f=out_fname, N=len(df))) - df.to_csv(out_fname, sep="\t", header=True, index=False) - call(['gzip', '-f', out_fname]) - elif args.pickle: - l2_suffix = '.pickle' - log.log("Writing LD Scores for {N} SNPs to {f}.pickle".format(f=out_fname, N=len(df))) - df.set_index('SNP') - out_fname_pickle = out_fname+l2_suffix - df.reset_index(drop=True).to_pickle(out_fname_pickle) - - # print .M - if annot_matrix is not None: - M = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix, axis=0)))) - ii = geno_array.maf > 0.05 - M_5_50 = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix[ii,:], axis=0)))) - else: - M = [geno_array.m] - M_5_50 = [np.sum(geno_array.maf > 0.05)] - - # print .M - fout_M = open(args.out + '.'+ file_suffix +'.M','wb') - print >>fout_M, '\t'.join(map(str,M)) - fout_M.close() - - # print .M_5_50 - fout_M_5_50 = open(args.out + '.'+ file_suffix +'.M_5_50','wb') - print >>fout_M_5_50, '\t'.join(map(str,M_5_50)) - fout_M_5_50.close() - - # print annot matrix - if (args.cts_bin is not None or args.cts_bin_add is not None) and not args.no_print_annot: - out_fname_annot = args.out + '.annot' - new_colnames = geno_array.colnames + ldscore_colnames - annot_df = pd.DataFrame(np.c_[geno_array.df, annot_matrix]) - annot_df.columns = new_colnames - del annot_df['MAF'] - log.log("Writing annot matrix produced by --cts-bin to {F}".format(F=out_fname+'.gz')) - if args.gzip: - annot_df.to_csv(out_fname_annot, sep="\t", header=True, index=False) - call(['gzip', '-f', out_fname_annot]) - else: - out_fname_annot_pickle = out_fname_annot + '.pickle' - annot_df.reset_index(drop=True).to_pickle(out_fname_annot_pickle) - - # print LD Score summary - pd.set_option('display.max_rows', 200) - log.log('\nSummary of LD Scores in {F}'.format(F=out_fname+l2_suffix)) - t = df.ix[:,4:].describe() - log.log( t.ix[1:,:] ) - - # print correlation matrix including all LD Scores and sample MAF - log.log('') - log.log('MAF/LD Score Correlation Matrix') - log.log( df.ix[:,4:].corr() ) - - # print condition number - if num_annots > 1: # condition number of a column vector w/ nonzero var is trivially one - log.log('\nLD Score Matrix Condition Number') - cond_num = np.linalg.cond(df.ix[:,5:]) - log.log( jk.kill_brackets(str(np.matrix(cond_num))) ) - if cond_num > 10000: - log.log('WARNING: ill-conditioned LD Score Matrix!') - - # summarize annot matrix if there is one - - if annot_matrix is not None: - # covariance matrix - x = pd.DataFrame(annot_matrix, columns=annot_colnames) - log.log('\nAnnotation Correlation Matrix') - log.log( x.corr() ) - - # column sums - log.log('\nAnnotation Matrix Column Sums') - log.log(_remove_dtype(x.sum(axis=0))) - - # row sums - log.log('\nSummary of Annotation Matrix Row Sums') - row_sums = x.sum(axis=1).describe() - log.log(_remove_dtype(row_sums)) + merged_list = None + if fname: + f = lambda x,n: x.format(noun=noun, verb=verb, fname=fname, num=n) + x = ps.FilterFile(fname) + c = 'Read list of {num} {noun} to {verb} from {fname}' + print f(c, len(x.IDList)) + merged_list = merge_obj.loj(x.IDList) + len_merged_list = len(merged_list) + if len_merged_list > 0: + c = 'After merging, {num} {noun} remain' + print f(c, len_merged_list) + else: + error_msg = 'No {noun} retained for analysis' + raise ValueError(f(error_msg, 0)) + + return merged_list +def annot_sort_key(s): + '''For use with --cts-bin. Fixes weird pandas crosstab column order.''' + if type(s) == tuple: + s = [x.split('_')[0] for x in s] + s = map(lambda x: float(x) if x != 'min' else -float('inf'), s) + else: #type(s) = str: + s = s.split('_')[0] + if s == 'min': + s = float('-inf') + else: + s = float(s) + + return s + +def ldscore(args, log): + ''' + Wrapper function for estimating l1, l1^2, l2 and l4 (+ optionally standard errors) from + reference panel genotypes. + + Annot format is + chr snp bp cm + + ''' + + if args.bfile: + snp_file, snp_obj = args.bfile+'.bim', ps.PlinkBIMFile + ind_file, ind_obj = args.bfile+'.fam', ps.PlinkFAMFile + array_file, array_obj = args.bfile+'.bed', ld.PlinkBEDFile + + # read bim/snp + array_snps = snp_obj(snp_file) + m = len(array_snps.IDList) + log.log('Read list of {m} SNPs from {f}'.format(m=m, f=snp_file)) + if args.annot is not None: # read --annot + try: + annot = ps.AnnotFile(args.annot) + n_annot, ma = len(annot.df.columns) - 4, len(annot.df) + log.log("Read {A} annotations for {M} SNPs from {f}".format(f=args.annot, + A=n_annot, M=ma)) + annot_matrix = np.array(annot.df.iloc[:,4:]) + annot_colnames = annot.df.columns[4:] + keep_snps = None + if np.any(annot.df.SNP.values != array_snps.df.SNP.values): + raise ValueError('The .annot file must contain the same SNPs in the same'+\ + ' order as the .bim file.') + except Exception: + log.log('Error parsing .annot file') + raise + + elif args.extract is not None: # --extract + keep_snps = __filter__(args.extract, 'SNPs', 'include', array_snps) + annot_matrix, annot_colnames, n_annot = None, None, 1 + + + elif args.cts_bin is not None and args.cts_breaks is not None: # --cts-bin + cts_fnames = args.cts_bin.split(',') # read filenames + args.cts_breaks = args.cts_breaks.replace('N','-') # replace N with negative sign + try: # split on x + breaks = [[float(x) for x in y.split(',')] for y in args.cts_breaks.split('x')] + except ValueError as e: + raise ValueError('--cts-breaks must be a comma-separated list of numbers: ' + +str(e.args)) + + if len(breaks) != len(cts_fnames): + raise ValueError('Need to specify one set of breaks for each file in --cts-bin.') + + if args.cts_names: + cts_colnames = [str(x) for x in args.cts_names.split(',')] + if len(cts_colnames) != len(cts_fnames): + msg = 'Must specify either no --cts-names or one value for each file in --cts-bin.' + raise ValueError(msg) + + else: + cts_colnames = ['ANNOT'+str(i) for i in xrange(len(cts_fnames))] + + log.log('Reading numbers with which to bin SNPs from {F}'.format(F=args.cts_bin)) + + cts_levs = [] + full_labs = [] + for i,fh in enumerate(cts_fnames): + vec = ps.read_cts(cts_fnames[i], array_snps.df.SNP.values) + + max_cts = np.max(vec) + min_cts = np.min(vec) + cut_breaks = list(breaks[i]) + name_breaks = list(cut_breaks) + if np.all(cut_breaks >= max_cts) or np.all(cut_breaks <= min_cts): + raise ValueError('All breaks lie outside the range of the cts variable.') + + if np.all(cut_breaks <= max_cts): + name_breaks.append(max_cts) + cut_breaks.append(max_cts+1) + + if np.all(cut_breaks >= min_cts): + name_breaks.append(min_cts) + cut_breaks.append(min_cts-1) + + name_breaks.sort() + cut_breaks.sort() + n_breaks = len(cut_breaks) + # so that col names are consistent across chromosomes with different max vals + name_breaks[0] = 'min' + name_breaks[-1] = 'max' + name_breaks = [str(x) for x in name_breaks] + labs = [name_breaks[i]+'_'+name_breaks[i+1] for i in xrange(n_breaks-1)] + cut_vec = pd.Series(pd.cut(vec, bins=cut_breaks, labels=labs)) + cts_levs.append(cut_vec) + full_labs.append(labs) + + annot_matrix = pd.concat(cts_levs, axis=1) + annot_matrix.columns = cts_colnames + # crosstab -- for now we keep empty columns + annot_matrix = pd.crosstab(annot_matrix.index, + [annot_matrix[i] for i in annot_matrix.columns], dropna=False, + colnames=annot_matrix.columns) + + # add missing columns + if len(cts_colnames) > 1: + for x in product(*full_labs): + if x not in annot_matrix.columns: + annot_matrix[x] = 0 + else: + for x in full_labs[0]: + if x not in annot_matrix.columns: + annot_matrix[x] = 0 + + annot_matrix = annot_matrix[sorted(annot_matrix.columns, key=annot_sort_key)] + if len(cts_colnames) > 1: + # flatten multi-index + annot_colnames = ['_'.join([cts_colnames[i]+'_'+b for i,b in enumerate(c)]) + for c in annot_matrix.columns] + else: + annot_colnames = [cts_colnames[0]+'_'+b for b in annot_matrix.columns] + + annot_matrix = np.matrix(annot_matrix) + keep_snps = None + n_annot = len(annot_colnames) + if np.any(np.sum(annot_matrix, axis=1) == 0): + # This exception should never be raised. For debugging only. + raise ValueError('Some SNPs have no annotation in --cts-bin. This is a bug!') + + else: + annot_matrix, annot_colnames, keep_snps = None, None, None, + n_annot = 1 + + # read fam + array_indivs = ind_obj(ind_file) + n = len(array_indivs.IDList) + log.log('Read list of {n} individuals from {f}'.format(n=n, f=ind_file)) + # read keep_indivs + if args.keep: + keep_indivs = __filter__(args.keep, 'individuals', 'include', array_indivs) + else: + keep_indivs = None + + # read genotype array + log.log('Reading genotypes from {fname}'.format(fname=array_file)) + geno_array = array_obj(array_file, n, array_snps, keep_snps=keep_snps, + keep_indivs=keep_indivs, mafMin=args.maf) + + # filter annot_matrix down to only SNPs passing MAF cutoffs + if annot_matrix is not None: + annot_keep = geno_array.kept_snps + annot_matrix = annot_matrix[annot_keep,:] + + # determine block widths + x = np.array((args.ld_wind_snps, args.ld_wind_kb, args.ld_wind_cm), dtype=bool) + if np.sum(x) != 1: + raise ValueError('Must specify exactly one --ld-wind option') + + if args.ld_wind_snps: + max_dist = args.ld_wind_snps + coords = np.array(xrange(geno_array.m)) + elif args.ld_wind_kb: + max_dist = args.ld_wind_kb*1000 + coords = np.array(array_snps.df['BP'])[geno_array.kept_snps] + elif args.ld_wind_cm: + max_dist = args.ld_wind_cm + coords = np.array(array_snps.df['CM'])[geno_array.kept_snps] + + block_left = ld.getBlockLefts(coords, max_dist) + if block_left[len(block_left)-1] == 0 and not args.yes_really: + error_msg = 'Do you really want to compute whole-chomosome LD Score? If so, set the ' + error_msg += '--yes-really flag (warning: it will use a lot of time / memory)' + raise ValueError(error_msg) + + scale_suffix = '' + if args.pq_exp is not None: + log.log('Computing LD with pq ^ {S}.'.format(S=args.pq_exp)) + msg = 'Note that LD Scores with pq raised to a nonzero power are' + msg += 'not directly comparable to normal LD Scores.' + log.log(msg) + scale_suffix = '_S{S}'.format(S=args.pq_exp) + pq = np.matrix(geno_array.maf*(1-geno_array.maf)).reshape((geno_array.m, 1)) + pq = np.power(pq, args.pq_exp) + + if annot_matrix is not None: + annot_matrix = np.multiply(annot_matrix, pq) + else: + annot_matrix = pq + + log.log("Estimating LD Score.") + lN = geno_array.ldScoreVarBlocks(block_left, args.chunk_size, annot=annot_matrix) + col_prefix = "L2"; file_suffix = "l2" + + if n_annot == 1: + ldscore_colnames = [col_prefix+scale_suffix] + else: + ldscore_colnames = [y+col_prefix+scale_suffix for y in annot_colnames] + + # print .ldscore. Output columns: CHR, BP, RS, [LD Scores] + out_fname = args.out + '.' + file_suffix + '.ldscore' + new_colnames = geno_array.colnames + ldscore_colnames + df = pd.DataFrame.from_records(np.c_[geno_array.df, lN]) + df.columns = new_colnames + if args.print_snps: + if args.print_snps.endswith('gz'): + print_snps = pd.read_csv(args.print_snps, header=None, compression='gzip') + elif args.print_snps.endswith('bz2'): + print_snps = pd.read_csv(args.print_snps, header=None, compression='bz2') + else: + print_snps = pd.read_csv(args.print_snps, header=None) + if len(print_snps.columns) > 1: + raise ValueError('--print-snps must refer to a file with a one column of SNP IDs.') + log.log('Reading list of {N} SNPs for which to print LD Scores from {F}'.format(\ + F=args.print_snps, N=len(print_snps))) + + print_snps.columns=['SNP'] + df = df.ix[df.SNP.isin(print_snps.SNP),:] + if len(df) == 0: + raise ValueError('After merging with --print-snps, no SNPs remain.') + else: + msg = 'After merging with --print-snps, LD Scores for {N} SNPs will be printed.' + log.log(msg.format(N=len(df))) + + l2_suffix = '.gz' + log.log("Writing LD Scores for {N} SNPs to {f}.gz".format(f=out_fname, N=len(df))) + df.drop(['CM','MAF'], axis=1).to_csv(out_fname, sep="\t", header=True, index=False, + float_format='%.3f') + call(['gzip', '-f', out_fname]) + if annot_matrix is not None: + M = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix, axis=0)))) + ii = geno_array.maf > 0.05 + M_5_50 = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix[ii,:], axis=0)))) + else: + M = [geno_array.m] + M_5_50 = [np.sum(geno_array.maf > 0.05)] + + # print .M + fout_M = open(args.out + '.'+ file_suffix +'.M','wb') + print >>fout_M, '\t'.join(map(str,M)) + fout_M.close() + + # print .M_5_50 + fout_M_5_50 = open(args.out + '.'+ file_suffix +'.M_5_50','wb') + print >>fout_M_5_50, '\t'.join(map(str,M_5_50)) + fout_M_5_50.close() + + # print annot matrix + if (args.cts_bin is not None) and not args.no_print_annot: + out_fname_annot = args.out + '.annot' + new_colnames = geno_array.colnames + ldscore_colnames + annot_df = pd.DataFrame(np.c_[geno_array.df, annot_matrix]) + annot_df.columns = new_colnames + del annot_df['MAF'] + log.log("Writing annot matrix produced by --cts-bin to {F}".format(F=out_fname+'.gz')) + annot_df.to_csv(out_fname_annot, sep="\t", header=True, index=False) + call(['gzip', '-f', out_fname_annot]) + + # print LD Score summary + pd.set_option('display.max_rows', 200) + log.log('\nSummary of LD Scores in {F}'.format(F=out_fname+l2_suffix)) + t = df.ix[:,4:].describe() + log.log( t.ix[1:,:] ) + + # print correlation matrix including all LD Scores and sample MAF + log.log('') + log.log('MAF/LD Score Correlation Matrix') + log.log( df.ix[:,4:].corr() ) + + # print condition number + if n_annot > 1: # condition number of a column vector w/ nonzero var is trivially one + log.log('\nLD Score Matrix Condition Number') + cond_num = np.linalg.cond(df.ix[:,5:]) + log.log( reg.remove_brackets(str(np.matrix(cond_num))) ) + if cond_num > 10000: + log.log('WARNING: ill-conditioned LD Score Matrix!') + + # summarize annot matrix if there is one + if annot_matrix is not None: + # covariance matrix + x = pd.DataFrame(annot_matrix, columns=annot_colnames) + log.log('\nAnnotation Correlation Matrix') + log.log( x.corr() ) + + # column sums + log.log('\nAnnotation Matrix Column Sums') + log.log(_remove_dtype(x.sum(axis=0))) + + # row sums + log.log('\nSummary of Annotation Matrix Row Sums') + row_sums = x.sum(axis=1).describe() + log.log(_remove_dtype(row_sums)) + + +parser = argparse.ArgumentParser() +parser.add_argument('--out', default='ldsc', type=str, + help='Output filename prefix. If --out is not set, LDSC will use ldsc as the ' + 'defualt output filename prefix.') +# Basic LD Score Estimation Flags' +parser.add_argument('--bfile', default=None, type=str, + help='Prefix for Plink .bed/.bim/.fam file') +parser.add_argument('--l2', default=False, action='store_true', + help='Estimate l2. Compatible with both jackknife and non-jackknife.') +# Filtering / Data Management for LD Score +parser.add_argument('--extract', default=None, type=str, + help='File with SNPs to include in LD Score estimation. ' + 'The file should contain one SNP ID per row.') +parser.add_argument('--keep', default=None, type=str, + help='File with individuals to include in LD Score estimation. ' + 'The file should contain one individual ID per row.') +parser.add_argument('--ld-wind-snps', default=None, type=int, + help='Specify the window size to be used for estimating LD Scores in units of ' + '# of SNPs. You can only specify one --ld-wind-* option.') +parser.add_argument('--ld-wind-kb', default=None, type=float, + help='Specify the window size to be used for estimating LD Scores in units of ' + 'kilobase-pairs (kb). You can only specify one --ld-wind-* option.') +parser.add_argument('--ld-wind-cm', default=None, type=float, + help='Specify the window size to be used for estimating LD Scores in units of ' + 'centiMorgans (cM). You can only specify one --ld-wind-* option.') +parser.add_argument('--print-snps', default=None, type=str, + help='This flag tells LDSC to only print LD Scores for the SNPs listed ' + '(one ID per row) in PRINT_SNPS. The sum r^2 will still include SNPs not in ' + 'PRINT_SNPs. This is useful for reducing the number of LD Scores that have to be ' + 'read into memory when estimating h2 or rg.' ) +# Fancy LD Score Estimation Flags +parser.add_argument('--annot', default=None, type=str, + help='Filename prefix for annotation file for partitioned LD Score estimation. ' + 'LDSC will automatically append .annot or .annot.gz to the filename prefix. ' + 'See docs/file_formats_ld for a definition of the .annot format.') +parser.add_argument('--cts-bin', default=None, type=str, + help='This flag tells LDSC to compute partitioned LD Scores, where the partition ' + 'is defined by cutting one or several continuous variable[s] into bins. ' + 'The argument to this flag should be the name of a single file or a comma-separated ' + 'list of files. The file format is two columns, with SNP IDs in the first column ' + 'and the continuous variable in the second column. ') +parser.add_argument('--cts-breaks', default=None, type=str, + help='Use this flag to specify names for the continuous variables cut into bins ' + 'with --cts-bin. For each continuous variable, specify breaks as a comma-separated ' + 'list of breakpoints, and separate the breakpoints for each variable with an x. ' + 'For example, if binning on MAF and distance to gene (in kb), ' + 'you might set --cts-breaks 0.1,0.25,0.4x10,100,1000 ') +parser.add_argument('--cts-names', default=None, type=str, + help='Use this flag to specify names for the continuous variables cut into bins ' + 'with --cts-bin. The argument to this flag should be a comma-separated list of ' + 'names. For example, if binning on DAF and distance to gene, you might set ' + '--cts-bin DAF,DIST_TO_GENE ') +parser.add_argument('--per-allele', default=False, action='store_true', + help='Setting this flag causes LDSC to compute per-allele LD Scores, ' + 'i.e., \ell_j := \sum_k p_k(1-p_k)r^2_{jk}, where p_k denotes the MAF ' + 'of SNP j. ') +parser.add_argument('--pq-exp', default=None, type=float, + help='Setting this flag causes LDSC to compute LD Scores with the given scale factor, ' + 'i.e., \ell_j := \sum_k (p_k(1-p_k))^a r^2_{jk}, where p_k denotes the MAF ' + 'of SNP j and a is the argument to --pq-exp. ') +parser.add_argument('--no-print-annot', default=False, action='store_true', + help='By defualt, seting --cts-bin or --cts-bin-add causes LDSC to print ' + 'the resulting annot matrix. Setting --no-print-annot tells LDSC not ' + 'to print the annot matrix. ') +parser.add_argument('--maf', default=None, type=float, + help='Minor allele frequency lower bound. Default is MAF > 0.') +# Basic Flags for Working with Variance Components +parser.add_argument('--h2', default=None, type=str, + help='Filename prefix for a .chisq file for one-phenotype LD Score regression. ' + 'LDSC will automatically append .chisq or .chisq.gz to the filename prefix.' + '--h2 requires at minimum also setting the --ref-ld and --w-ld flags.') +parser.add_argument('--rg', default=None, type=str, + help='Comma-separated list of prefixes of .chisq filed for genetic correlation estimation.') +parser.add_argument('--ref-ld', default=None, type=str, + help='Use --ref-ld to tell LDSC which LD Scores to use as the predictors in the LD ' + 'Score regression. ' + 'LDSC will automatically append .l2.ldscore/.l2.ldscore.gz to the filename prefix.') +parser.add_argument('--ref-ld-chr', default=None, type=str, + help='Same as --ref-ld, but will automatically concatenate .l2.ldscore files split ' + 'across 22 chromosomes. LDSC will automatically append .l2.ldscore/.l2.ldscore.gz ' + 'to the filename prefix. If the filename prefix contains the symbol @, LDSC will ' + 'replace the @ symbol with chromosome numbers. Otherwise, LDSC will append chromosome ' + 'numbers to the end of the filename prefix.' + 'Example 1: --ref-ld-chr ld/ will read ld/1.l2.ldscore.gz ... ld/22.l2.ldscore.gz' + 'Example 2: --ref-ld-chr ld/@_kg will read ld/1_kg.l2.ldscore.gz ... ld/22_kg.l2.ldscore.gz') +parser.add_argument('--w-ld', default=None, type=str, + help='Filename prefix for file with LD Scores with sum r^2 taken over SNPs included ' + 'in the regression. LDSC will automatically append .l2.ldscore/.l2.ldscore.gz.') +parser.add_argument('--w-ld-chr', default=None, type=str, + help='Same as --w-ld, but will read files split into 22 chromosomes in the same ' + 'manner as --ref-ld-chr.') +parser.add_argument('--overlap-annot', default=False, action='store_true', + help='This flag informs LDSC that the partitioned LD Scores were generates using an ' + 'annot matrix with overlapping categories (i.e., not all row sums equal 1), ' + 'and prevents LDSC from displaying output that is meaningless with overlapping categories.') +parser.add_argument('--no-intercept', action='store_true', + help = 'If used with --h2, this constrains the LD Score regression intercept to equal ' + '1. If used with --rg, this constrains the LD Score regression intercepts for the h2 ' + 'estimates to be one and the intercept for the genetic covariance estimate to be zero.') +parser.add_argument('--intercept-h2', action='store', default=None, + help = 'Intercepts for constrained-intercept single-trait LD Score regression.') +parser.add_argument('--intercept-gencov', action='store', default=None, + help = 'Intercepts for constrained-intercept cross-trait LD Score regression.' + ' Must have same length as --rg. The first entry is ignored.') +parser.add_argument('--M', default=None, type=str, + help='# of SNPs (if you don\'t want to use the .l2.M files that came with your .l2.ldscore.gz files)') +parser.add_argument('--two-step', default=None, type=float, + help='Test statistic bound for use with the two-step estimator. Not compatible with --no-intercept and --constrain-intercept.') +parser.add_argument('--chisq-max', default=None, type=float, + help='Max chi^2.') +# Flags for both LD Score estimation and h2/gencor estimation +parser.add_argument('--print-cov', default=False, action='store_true', + help='For use with --h2/--rg. This flag tells LDSC to print the ' + 'covaraince matrix of the estimates.') +# frequency (useful for .bin files) +parser.add_argument('--print-delete-vals', default=False, action='store_true', + help='If this flag is set, LDSC will print the block jackknife delete-values (' + 'i.e., the regression coefficeints estimated from the data with a block removed). ' + 'The delete-values are formatted as a matrix with (# of jackknife blocks) rows and ' + '(# of LD Scores) columns.') +# Flags you should almost never use +parser.add_argument('--chunk-size', default=50, type=int, + help='Chunk size for LD Score calculation. Use the default.') +parser.add_argument('--pickle', default=False, action='store_true', + help='Store .l2.ldscore files as pickles instead of gzipped tab-delimited text.') +parser.add_argument('--yes-really', default=False, action='store_true', + help='Yes, I really want to compute whole-chromosome LD Score.') +parser.add_argument('--invert-anyway', default=False, action='store_true', + help="Force LDSC to attempt to invert ill-conditioned matrices.") +parser.add_argument('--n-blocks', default=200, type=int, + help='Number of block jackknife blocks.') +parser.add_argument('--not-M-5-50', default=False, action='store_true', + help='This flag tells LDSC to use the .l2.M file instead of the .l2.M_5_50 file.') +parser.add_argument('--return-silly-things', default=False, action='store_true', + help='Force ldsc to return silly genetic correlation estimates.') +parser.add_argument('--no-check-alleles', default=False, action='store_true', + help='For rg estimation, skip checking whether the alleles match. This check is ' + 'redundant for pairs of chisq files generated using munge_sumstats.py and the ' + 'same argument to the --merge-alleles flag.') +parser.add_argument('--print-coefficients',default=False,action='store_true', + help='when categories are overlapping, print coefficients as well as heritabilities.') +parser.add_argument('--frqfile', type=str, + help='For use with --overlap-annot. Provides allele frequencies to prune to common ' + 'snps if --not-M-5-50 is not set.') +parser.add_argument('--frqfile-chr', type=str, + help='Prefix for --frqfile files split over chromosome.') +# transform to liability scale +parser.add_argument('--samp-prev',default=None, + help='Sample prevalence of binary phenotype (for conversion to liability scale).') +parser.add_argument('--pop-prev',default=None, + help='Population prevalence of binary phenotype (for conversion to liability scale).') if __name__ == '__main__': - parser = argparse.ArgumentParser() - - parser.add_argument('--out', default='ldsc', type=str, - help='Output filename prefix. If --out is not set, LDSC will use ldsc as the ' - 'defualt output filename prefix.') - - # Basic LD Score Estimation Flags' - parser.add_argument('--bfile', default=None, type=str, - help='Prefix for Plink .bed/.bim/.fam file') - - # Filtering / Data Management for LD Score - parser.add_argument('--extract', default=None, type=str, - help='File with SNPs to include in LD Score estimation. ' - 'The file should contain one SNP ID per row.') - parser.add_argument('--keep', default=None, type=str, - help='File with individuals to include in LD Score estimation. ' - 'The file should contain one individual ID per row.') - parser.add_argument('--ld-wind-snps', default=None, type=int, - help='Specify the window size to be used for estimating LD Scores in units of ' - '# of SNPs. You can only specify one --ld-wind-* option.') - parser.add_argument('--ld-wind-kb', default=None, type=float, - help='Specify the window size to be used for estimating LD Scores in units of ' - 'kilobase-pairs (kb). You can only specify one --ld-wind-* option.') - parser.add_argument('--ld-wind-cm', default=None, type=float, - help='Specify the window size to be used for estimating LD Scores in units of ' - 'centiMorgans (cM). You can only specify one --ld-wind-* option.') - parser.add_argument('--print-snps', default=None, type=str, - help='This flag tells LDSC to only print LD Scores for the SNPs listed ' - '(one ID per row) in PRINT_SNPS. The sum r^2 will still include SNPs not in ' - 'PRINT_SNPs. This is useful for reducing the number of LD Scores that have to be ' - 'read into memory when estimating h2 or rg.' ) - - # Output for LD Score - #parser.add_argument('--l1', default=False, action='store_true', - # help='Estimate l1 w.r.t. sample minor allele.') - #parser.add_argument('--l1sq', default=False, action='store_true', - # help='Estimate l1 ^ 2 w.r.t. sample minor allele.') - parser.add_argument('--l2', default=False, action='store_true', - help='Estimate l2. Compatible with both jackknife and non-jackknife.') - #parser.add_argument('--l4', default=False, action='store_true', - # help='Estimate l4. Only compatible with jackknife.') - #parser.add_argument('--se', action='store_true', - # help='Block jackknife SE? (Warning: somewhat slower)') - - # Fancy LD Score Estimation Flags - parser.add_argument('--annot', default=None, type=str, - help='Filename prefix for annotation file for partitioned LD Score estimation. ' - 'LDSC will automatically append .annot or .annot.gz to the filename prefix. ' - 'See docs/file_formats_ld for a definition of the .annot format.') - parser.add_argument('--cts-bin', default=None, type=str, - help='This flag tells LDSC to compute partitioned LD Scores, where the partition ' - 'is defined by cutting one or several continuous variable[s] into bins. ' - 'The argument to this flag should be the name of a single file or a comma-separated ' - 'list of files. The file format is two columns, with SNP IDs in the first column ' - 'and the continuous variable in the second column. ') - parser.add_argument('--cts-bin-add', default=None, type=str, - help='Same as --cts-bin, but tells LDSC to bin additively instead of multiplicatively. ') - parser.add_argument('--cts-breaks', default=None, type=str, - help='Use this flag to specify names for the continuous variables cut into bins ' - 'with --cts-bin. For each continuous variable, specify breaks as a comma-separated ' - 'list of breakpoints, and separate the breakpoints for each variable with an x. ' - 'For example, if binning on MAF and distance to gene (in kb), ' - 'you might set --cts-breaks 0.1,0.25,0.4x10,100,1000 ') - parser.add_argument('--cts-names', default=None, type=str, - help='Use this flag to specify names for the continuous variables cut into bins ' - 'with --cts-bin. The argument to this flag should be a comma-separated list of ' - 'names. For example, if binning on DAF and distance to gene, you might set ' - '--cts-bin DAF,DIST_TO_GENE ' - ) - parser.add_argument('--per-allele', default=False, action='store_true', - help='Setting this flag causes LDSC to compute per-allele LD Scores, ' - 'i.e., \ell_j := \sum_k p_k(1-p_k)r^2_{jk}, where p_k denotes the MAF ' - 'of SNP j. ') - parser.add_argument('--pq-exp', default=None, type=float, - help='Setting this flag causes LDSC to compute LD Scores with the given scale factor, ' - 'i.e., \ell_j := \sum_k (p_k(1-p_k))^a r^2_{jk}, where p_k denotes the MAF ' - 'of SNP j and a is the argument to --pq-exp. ') - parser.add_argument('--maf-exp', default=None, type=float, - help='Setting this flag causes LDSC to compute LD Scores with the given scale factor, ' - 'i.e., \ell_j := \sum_k (p_k^a r^2_{jk}, where p_k denotes the MAF ' - 'of SNP j and a is the argument to --maf-exp. ') - parser.add_argument('--no-print-annot', default=False, action='store_true', - help='By defualt, seting --cts-bin or --cts-bin-add causes LDSC to print ' - 'the resulting annot matrix. Setting --no-print-annot tells LDSC not ' - 'to print the annot matrix. ') - parser.add_argument('--maf', default=None, type=float, - help='Minor allele frequency lower bound. Default is MAF > 0.') - - # Basic Flags for Working with Variance Components - parser.add_argument('--intercept', default=None, type=str, - help='Filename prefix for a .chisq file for one-phenotype LD Score regression. ' - '--intercept performs the same analysis as --h2, but prints output ' - 'focused on the LD Score regression intercept, rather than the h2 estimate. ' - 'LDSC will automatically append .chisq or .chisq.gz to the filename prefix.' - '--intercept requires at minimum also setting the --ref-ld and --w-ld flags.') - parser.add_argument('--h2', default=None, type=str, - help='Filename prefix for a .chisq file for one-phenotype LD Score regression. ' - 'LDSC will automatically append .chisq or .chisq.gz to the filename prefix.' - '--h2 requires at minimum also setting the --ref-ld and --w-ld flags.') - parser.add_argument('--rg', default=None, type=str, - help='Comma-separated list of prefixes of .chisq filed for genetic correlation estimation.') - parser.add_argument('--rg-list', default=None, type=str, - help='File containing a list of prefixes of .chisq files (one per line) for genetic correlation estimation.') - parser.add_argument('--ref-ld', default=None, type=str, - help='Use --ref-ld to tell LDSC which LD Scores to use as the predictors in the LD ' - 'Score regression. ' - 'LDSC will automatically append .l2.ldscore/.l2.ldscore.gz to the filename prefix.') - parser.add_argument('--ref-ld-chr', default=None, type=str, - help='Same as --ref-ld, but will automatically concatenate .l2.ldscore files split ' - 'across 22 chromosomes. LDSC will automatically append .l2.ldscore/.l2.ldscore.gz ' - 'to the filename prefix. If the filename prefix contains the symbol @, LDSC will ' - 'replace the @ symbol with chromosome numbers. Otherwise, LDSC will append chromosome ' - 'numbers to the end of the filename prefix.' - 'Example 1: --ref-ld-chr ld/ will read ld/1.l2.ldscore.gz ... ld/22.l2.ldscore.gz' - 'Example 2: --ref-ld-chr ld/@_kg will read ld/1_kg.l2.ldscore.gz ... ld/22_kg.l2.ldscore.gz') - parser.add_argument('--ref-ld-file', default=None, type=str, - help='File with one line per reference ldscore file, to be concatenated sideways.') - parser.add_argument('--ref-ld-file-chr', default=None, type=str, - help='Same as --ref-ld-file, but will concatenate LD Scores split into 22 ' - 'chromosomes in the same manner as --ref-ld-chr.') - parser.add_argument('--ref-ld-list', default=None, type=str, - help='Comma-separated list of reference ldscore files, to be concatenated sideways.') - parser.add_argument('--ref-ld-list-chr', default=None, type=str, - help='Same as --ref-ld-list, except automatically concatenates LD Score files ' - 'split into 22 chromosomes in the same manner as --ref-ld-chr.') - parser.add_argument('--w-ld', default=None, type=str, - help='Filename prefix for file with LD Scores with sum r^2 taken over SNPs included ' - 'in the regression. LDSC will automatically append .l2.ldscore/.l2.ldscore.gz.') - parser.add_argument('--w-ld-chr', default=None, type=str, - help='Same as --w-ld, but will read files split into 22 chromosomes in the same ' - 'manner as --ref-ld-chr.') - parser.add_argument('--overlap-annot', default=False, action='store_true', - help='This flag informs LDSC that the partitioned LD Scores were generates using an ' - 'annot matrix with overlapping categories (i.e., not all row sums equal 1), ' - 'and prevents LDSC from displaying output that is meaningless with overlapping categories.') - - parser.add_argument('--no-filter-chisq', default=False, action='store_true', - help='Don\'t remove SNPs with large chi-square.') - parser.add_argument('--max-chisq', default=None, type=float, - help='Max chi^2 for SNPs in the regression.') - - parser.add_argument('--no-intercept', action='store_true', - help = 'If used with --h2, this constrains the LD Score regression intercept to equal ' - '1. If used with --rg, this constrains the LD Score regression intercepts for the h2 ' - 'estimates to be one and the intercept for the genetic covariance estimate to be zero.') - parser.add_argument('--constrain-intercept', action='store', default=False, - help = 'If used with --h2, constrain the regression intercept to be a fixed value. ' - 'If used with -rg, constrain the regression intercepts to a comma-separated list ' - 'of three values, where the first value is the intercept of the first h2 regression, ' - 'the second value is the intercept of the second h2 regression, and the third ' - 'value is the intercept of the genetic covaraince regression (i.e., an estimate ' - 'of (# of overlapping samples)*(phenotpyic correlation). ') - parser.add_argument('--non-negative', action='store_true', - help = 'Setting this flag causes LDSC to constrain all of the regression coefficients ' - 'to be non-negative (i.e., to minimize the sum of squared errors subject to the ' - 'constraint that all of the coefficients be positive. Note that the run-time is somewhat higher.') - parser.add_argument('--M', default=None, type=str, - help='# of SNPs (if you don\'t want to use the .l2.M files that came with your .l2.ldscore.gz files)') - parser.add_argument('--M-file', default=None, type=str, - help='Alternate .M file (e.g., if you want to use .M_5_50).') - - # Filtering for sumstats - parser.add_argument('--info-min', default=None, type=float, - help='Minimum INFO score for SNPs included in the regression. If your .chisq files ' - 'do not include an INFO colum, setting this flag will result in an error. We ' - 'recommend throwing out all low-INFO SNPs before making the .chisq file.') - parser.add_argument('--info-max', default=None, type=float, - help='Maximum INFO score for SNPs included in the regression. If your .chisq files ' - 'do not include an INFO colum, setting this flag will result in an error.') - parser.add_argument('--keep-ld', default=None, type=str, - help='Zero-indexed column numbers of LD Scores to keep for LD Score regression.') - # Optional flags for genetic correlation - parser.add_argument('--overlap', default=0, type=int, - help='By defualt LDSC weights the genetic covariance regression in --rg assuming that ' - 'there are no overlapping samples. If there are overlapping samples, the LD Score ' - 'regression standard error will be reduced if the weights take this into account. ' - 'Use --overlap with --rg to tell LDSC the number of overlapping samples. ' - '--overlap must be used with --rho. Since these numbers are only used for ' - 'regression weights, it is OK if they are not precise.') - parser.add_argument('--rho', default=0, type=float, - help='Estimate of the phenotypic correlation among overlapping samples. For use with ' - '--overlap.') - - # Flags for both LD Score estimation and h2/gencor estimation - parser.add_argument('--human-only', default=False, action='store_true', - help='For use with --intercept/--h2/--rg. This flag tells LDSC only to print the ' - 'human-readable .log file and not the machine-readable covaraince matrix of the ' - 'estimates.') - # frequency (useful for .bin files) - parser.add_argument('--print-delete-vals', default=False, action='store_true', - help='If this flag is set, LDSC will print the block jackknife delete-values (' - 'i.e., the regression coefficeints estimated from the data with a block removed). ' - 'The delete-values are formatted as a matrix with (# of jackknife blocks) rows and ' - '(# of LD Scores) columns.') - # Flags you should almost never use - parser.add_argument('--chunk-size', default=50, type=int, - help='Chunk size for LD Score calculation. Use the default.') - parser.add_argument('--pickle', default=False, action='store_true', - help='Store .l2.ldscore files as pickles instead of gzipped tab-delimited text.') - parser.add_argument('--yes-really', default=False, action='store_true', - help='Yes, I really want to compute whole-chromosome LD Score.') - parser.add_argument('--aggregate', action='store_true', - help = 'Use the aggregate estimator.') - parser.add_argument('--invert-anyway', default=False, action='store_true', - help="Force LDSC to attempt to invert ill-conditioned matrices.") - parser.add_argument('--num-blocks', default=200, type=int, - help='Number of block jackknife blocks.') - parser.add_argument('--not-M-5-50', default=False, action='store_true', - help='This flag tells LDSC to use the .l2.M file instead of the .l2.M_5_50 file.') - parser.add_argument('--return-silly-things', default=False, action='store_true', - help='Force ldsc to return silly genetic correlation estimates.') - parser.add_argument('--no-check', default=True, action='store_false', - help='Don\'t check the contents of chisq files. These checks can be slow, and are ' - 'redundant for chisq files generated using sumstats_to_chisq.py.') - parser.add_argument('--no-check-alleles', default=False, action='store_true', - help='For rg estimation, skip checking whether the alleles match. This check is ' - 'redundant for pairs of chisq files generated using sumstats_to_chisq.py with the ' - '--merge-alleles flag.') - parser.add_argument('--slow', default=False, action='store_true', - help='Use a slow (but possibly more numerically stable) jackknife algorithm.') - parser.add_argument('--print-coefficients',default=False,action='store_true', - help='when categories are overlapping, print coefficients as well as heritabilities.') - parser.add_argument('--frqfile', type=str, - help='For use with --overlap-annot. Provides allele frequencies to prune to common ' - 'snps if --not-M-5-50 is not set.') - - - args = parser.parse_args() - - if args.no_check_alleles: - args.no_check = False - - args = parser.parse_args() - defaults = vars(parser.parse_args('')) - opts = vars(args) - non_defaults = [x for x in opts.keys() if opts[x] != defaults[x]] - - header = MASTHEAD - header += "\nOptions: \n" - options = ['--'+x.replace('_','-')+' '+str(opts[x]) for x in non_defaults] - header += '\n'.join(options).replace('True','').replace('False','') - header += '\n' - - if args.constrain_intercept: - args.constrain_intercept = args.constrain_intercept.replace('N','-') - - if args.w_ld: - args.w_ld = args.w_ld - elif args.w_ld_chr: - args.w_ld_chr = args.w_ld_chr - - if args.num_blocks <= 1: - raise ValueError('--num-blocks must be an integer > 1.') - - #if args.freq: - # if (args.bfile is not None) == (args.bin is not None): - # raise ValueError('Must set exactly one of --bin or --bfile for use with --freq') - # - # freq(args, header) - - # LD Score estimation - #elif (args.bin is not None or args.bfile is not None) and (args.l1 or args.l1sq or args.l2 or args.l4): - # if np.sum((args.l1, args.l2, args.l1sq, args.l4)) != 1: - #elif (args.bin is not None or args.bfile is not None): - if args.bfile is not None: - if args.l2 is None: - #raise ValueError('Must specify exactly one of --l1, --l1sq, --l2, --l4 for LD estimation.') - raise ValueError('Must specify --l2 with --bfile.') - #if args.bfile and args.bin: - # raise ValueError('Cannot specify both --bin and --bfile.') - if args.annot is not None and args.extract is not None: - raise ValueError('--annot and --extract are currently incompatible.') - if args.cts_bin is not None and args.extract is not None: - raise ValueError('--cts-bin and --extract are currently incompatible.') - if args.annot is not None and args.cts_bin is not None: - raise ValueError('--annot and --cts-bin are currently incompatible.') - if (args.cts_bin is not None or args.cts_bin_add is not None) != (args.cts_breaks is not None): - raise ValueError('Must set both or neither of --cts-bin and --cts-breaks.') - if args.per_allele and args.pq_exp is not None: - raise ValueError('Cannot set both --per-allele and --pq-exp (--per-allele is equivalent to --pq-exp 1).') - if args.per_allele: - args.pq_exp = 1 - - ldscore(args, header) - - # Summary statistics - elif (args.h2 or - args.rg or - args.intercept or - args.rg_list) and\ - (args.ref_ld or args.ref_ld_chr or args.ref_ld_file or args.ref_ld_file_chr\ - or args.ref_ld_list or args.ref_ld_list_chr) and\ - (args.w_ld or args.w_ld_chr): - - if np.sum(np.array((args.intercept, args.h2, args.rg or args.rg_list)).astype(bool)) > 1: - raise ValueError('Cannot specify more than one of --h2, --rg, --intercept, --rg-list.') - if args.ref_ld and args.ref_ld_chr: - raise ValueError('Cannot specify both --ref-ld and --ref-ld-chr.') - if args.ref_ld_list and args.ref_ld_list_chr: - raise ValueError('Cannot specify both --ref-ld-list and --ref-ld-list-chr.') - if args.ref_ld_file and args.ref_ld_file_chr: - raise ValueError('Cannot specify both --ref-ld-list and --ref-ld-list-chr.') - if args.w_ld and args.w_ld_chr: - raise ValueError('Cannot specify both --w-ld and --w-ld-chr.') - if args.rho or args.overlap: - if not args.rg or args.rg_list: - raise ValueError('--rho and --overlap can only be used with --rg.') - if not (args.rho and args.overlap): - raise ValueError('Must specify either both or neither of --rho and --overlap.') - - if args.rg or args.rg_list: - sumstats.Rg(args, header) - elif args.h2: - sumstats.H2(args, header) - elif args.intercept: - sumstats.Intercept(args, header) - - # bad flags - else: - print header - print 'Error: no analysis selected.' - print 'ldsc.py --help describes all options.' - - -# def freq(args): -# ''' -# Computes and prints reference allele frequencies. Identical to plink --freq. In fact, -# use plink --freq instead with .bed files; it's faster. This is useful for .bin files, -# which are a custom LDSC format. -# -# TODO: the MAF computation is inefficient, because it also filters the genotype matrix -# on MAF. It isn't so slow that it really matters, but fix this eventually. -# -# ''' -# log = logger(args.out+'.log') -# if header: -# log.log(header) -# -# if args.bin: -# snp_file, snp_obj = args.bin+'.bim', ps.PlinkBIMFile -# ind_file, ind_obj = args.bin+'.ind', ps.VcfINDFile -# array_file, array_obj = args.bin+'.bin', ld.VcfBINFile -# elif args.bfile: -# snp_file, snp_obj = args.bfile+'.bim', ps.PlinkBIMFile -# ind_file, ind_obj = args.bfile+'.fam', ps.PlinkFAMFile -# array_file, array_obj = args.bfile+'.bed', ld.PlinkBEDFile -# -# # read bim/snp -# array_snps = snp_obj(snp_file) -# m = len(array_snps.IDList) -# log.log('Read list of {m} SNPs from {f}'.format(m=m, f=snp_file)) -# -# # read fam/ind -# array_indivs = ind_obj(ind_file) -# n = len(array_indivs.IDList) -# log.log('Read list of {n} individuals from {f}'.format(n=n, f=ind_file)) -# -# # read --extract -# if args.extract is not None: -# keep_snps = __filter__(args.extract, 'SNPs', 'include', array_snps) -# else: -# keep_snps = None -# -# # read keep_indivs -# if args.keep: -# keep_indivs = __filter__(args.keep, 'individuals', 'include', array_indivs) -# else: -# keep_indivs = None -# -# # read genotype array -# log.log('Reading genotypes from {fname}'.format(fname=array_file)) -# geno_array = array_obj(array_file, n, array_snps, keep_snps=keep_snps, -# keep_indivs=keep_indivs) -# -# frq_df = array_snps.df.ix[:,['CHR', 'SNP', 'A1', 'A2']] -# frq_array = np.zeros(len(frq_df)) -# frq_array[geno_array.kept_snps] = geno_array.freq -# frq_df['FRQ'] = frq_array -# out_fname = args.out + '.frq' -# log.log('Writing reference allele frequencies to {O}.gz'.format(O=out_fname)) -# frq_df.to_csv(out_fname, sep="\t", header=True, index=False) -# call(['gzip', '-f', out_fname]) + + args = parser.parse_args() + if args.out is None: + raise ValueError('--out is required.') + + log = Logger(args.out+'.log') + try: + defaults = vars(parser.parse_args('')) + opts = vars(args) + non_defaults = [x for x in opts.keys() if opts[x] != defaults[x]] + header = MASTHEAD + header += "\nOptions: \n" + options = ['--'+x.replace('_','-')+' '+str(opts[x]) for x in non_defaults] + header += '\n'.join(options).replace('True','').replace('False','')+'\n' + log.log(header) + log.log('Beginning analysis at {T}'.format(T=time.ctime())) + start_time = time.time() + if args.n_blocks <= 1: + raise ValueError('--n-blocks must be an integer > 1.') + if args.bfile is not None: + if args.l2 is None: + raise ValueError('Must specify --l2 with --bfile.') + if args.annot is not None and args.extract is not None: + raise ValueError('--annot and --extract are currently incompatible.') + if args.cts_bin is not None and args.extract is not None: + raise ValueError('--cts-bin and --extract are currently incompatible.') + if args.annot is not None and args.cts_bin is not None: + raise ValueError('--annot and --cts-bin are currently incompatible.') + if (args.cts_bin is not None) != (args.cts_breaks is not None): + raise ValueError('Must set both or neither of --cts-bin and --cts-breaks.') + if args.per_allele and args.pq_exp is not None: + raise ValueError('Cannot set both --per-allele and --pq-exp (--per-allele is equivalent to --pq-exp 1).') + if args.per_allele: + args.pq_exp = 1 + if not args.overlap_annot or args.not_M_5_50: + if args.frqfile is not None or args.frqfile_chr is not None: + log.log('The frequency file is unnecessary and is being ignored.') + args.frqfile = None + args.frqfile_chr = None + if args.overlap_annot and not args.not_M_5_50: + if not (args.frqfile and args.ref_ld) or (args.frqfile_chr and args.ref_ld_chr): + raise ValueError ('Must set either --frqfile and --ref-ld or --frqfile-chr and --ref-ld-chr') + + + ldscore(args, log) + # summary statistics + elif (args.h2 or args.rg) and (args.ref_ld or args.ref_ld_chr) and (args.w_ld or args.w_ld_chr): + if args.h2 is not None and args.rg is not None: + raise ValueError('Cannot set both --h2 and --rg.') + if args.ref_ld and args.ref_ld_chr: + raise ValueError('Cannot set both --ref-ld and --ref-ld-chr.') + if args.w_ld and args.w_ld_chr: + raise ValueError('Cannot set both --w-ld and --w-ld-chr.') + if (args.samp_prev is not None) != (args.pop_prev is not None): + raise ValueError('Must set both or neither of --samp-prev and --pop-prev.') + + if args.rg: + sumstats.estimate_rg(args, log) + elif args.h2: + sumstats.estimate_h2(args, log) + + # bad flags + else: + print header + print 'Error: no analysis selected.' + print 'ldsc.py -h describes options.' + except Exception: + ex_type, ex, tb = sys.exc_info() + log.log( traceback.format_exc(ex) ) + raise + finally: + log.log('Analysis finished at {T}'.format(T=time.ctime()) ) + time_elapsed = round(time.time()-start_time,2) + log.log('Total time elapsed: {T}'.format(T=sec_to_str(time_elapsed))) diff --git a/ldscore/irwls.py b/ldscore/irwls.py new file mode 100644 index 00000000..d635a2c7 --- /dev/null +++ b/ldscore/irwls.py @@ -0,0 +1,196 @@ +''' +(c) 2015 Brendan Bulik-Sullivan and Hilary Finucane + +Iterativey re-weighted least squares. + +''' +from __future__ import division +import numpy as np +import jackknife as jk + + +class IRWLS(object): + + ''' + Iteratively re-weighted least squares (FLWS). + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + update_func : function + Transforms output of np.linalg.lstsq to new weights. + n_blocks : int + Number of jackknife blocks (for estimating SE via block jackknife). + w : np.matrix with shape (n, 1) + Initial regression weights (default is the identity matrix). These should be on the + inverse CVF scale. + slow : bool + Use slow block jackknife? (Mostly for testing) + + Attributes + ---------- + est : np.matrix with shape (1, p) + IRWLS estimate. + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + delete_values : np.matrix with shape (n_blocks, p) + Jackknife delete values. + + Methods + ------- + wls(x, y, w) : + Weighted Least Squares. + _weight(x, w) : + Weight x by w. + + ''' + + def __init__(self, x, y, update_func, n_blocks, w=None, slow=False, separators=None): + n, p = jk._check_shape(x, y) + if w is None: + w = np.ones_like(y) + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) + + jknife = self.irwls( + x, y, update_func, n_blocks, w, slow=slow, separators=separators) + self.est = jknife.est + self.jknife_se = jknife.jknife_se + self.jknife_est = jknife.jknife_est + self.jknife_var = jknife.jknife_var + self.jknife_cov = jknife.jknife_cov + self.delete_values = jknife.delete_values + self.separators = jknife.separators + + @classmethod + def irwls(cls, x, y, update_func, n_blocks, w, slow=False, separators=None): + ''' + Iteratively re-weighted least squares (IRWLS). + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + update_func: function + Transforms output of np.linalg.lstsq to new weights. + n_blocks : int + Number of jackknife blocks (for estimating SE via block jackknife). + w : np.matrix with shape (n, 1) + Initial regression weights. + slow : bool + Use slow block jackknife? (Mostly for testing) + separators : list or None + Block jackknife block boundaries (optional). + + Returns + ------- + jknife : jk.LstsqJackknifeFast + Block jackknife regression with the final IRWLS weights. + + ''' + (n, p) = x.shape + if y.shape != (n, 1): + raise ValueError( + 'y has shape {S}. y must have shape ({N}, 1).'.format(S=y.shape, N=n)) + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) + + w = np.sqrt(w) + for i in xrange(2): # update this later + new_w = np.sqrt(update_func(cls.wls(x, y, w))) + if new_w.shape != w.shape: + print 'IRWLS update:', new_w.shape, w.shape + raise ValueError('New weights must have same shape.') + else: + w = new_w + + x = cls._weight(x, w) + y = cls._weight(y, w) + if slow: + jknife = jk.LstsqJackknifeSlow( + x, y, n_blocks, separators=separators) + else: + jknife = jk.LstsqJackknifeFast( + x, y, n_blocks, separators=separators) + + return jknife + + @classmethod + def wls(cls, x, y, w): + ''' + Weighted least squares. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + w : np.matrix with shape (n, 1) + Regression weights (1/CVF scale). + + Returns + ------- + coef : list with four elements (coefficients, residuals, rank, singular values) + Output of np.linalg.lstsq + + ''' + (n, p) = x.shape + if y.shape != (n, 1): + raise ValueError( + 'y has shape {S}. y must have shape ({N}, 1).'.format(S=y.shape, N=n)) + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) + + x = cls._weight(x, w) + y = cls._weight(y, w) + coef = np.linalg.lstsq(x, y) + return coef + + @classmethod + def _weight(cls, x, w): + ''' + Weight x by w. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Rows are observations. + w : np.matrix with shape (n, 1) + Regression weights (1 / sqrt(CVF) scale). + + Returns + ------- + x_new : np.matrix with shape (n, p) + x_new[i,j] = x[i,j] * w'[i], where w' is w normalized to have sum 1. + + Raises + ------ + ValueError : + If any element of w is <= 0 (negative weights are not meaningful in WLS). + + ''' + if np.any(w <= 0): + raise ValueError('Weights must be > 0') + (n, p) = x.shape + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape (n, 1).'.format(S=w.shape)) + + w = w / float(np.sum(w)) + x_new = np.multiply(x, w) + return x_new diff --git a/ldscore/jackknife.py b/ldscore/jackknife.py index fc8aa08c..d9d0cba5 100644 --- a/ldscore/jackknife.py +++ b/ldscore/jackknife.py @@ -1,1012 +1,514 @@ ''' (c) 2014 Brendan Bulik-Sullivan and Hilary Finucane -This module contains basic functions for estimating - 1. heritability / partitioned heritability - 2. genetic covariance - 3. genetic correlation - 4. block jackknife standard errors (hence the module name) for all of the above. - -Numpy does this annoying thing where it treats an array of shape (M, ) very differently -from an array of shape (M, 1). In order to deal with univariate LD Score regression -and partitioned LD Score regression with the same code, everything in this module deals -with numpy matrices. - -Weird bugs may result if you pass numpy arrays or pandas dataframes without first -converting to a matrix with the correct shape. - -Terminology note -- the intended use case for this module is an LD Score regression with -n_snps SNPs included in the regression and n_annot (partitioned) LD Scores (which means -that the number of parameters estimated in the regression will be n_annot + 1, since we -also estimate an intercept term). +Fast block jackknives. + +Everything in this module deals with 2D numpy arrays. 1D data are represented as arrays +with dimension (N, 1) or (1, N), to avoid bugs arising from numpy treating (N, ) as +a fundamentally different shape from (N, 1). The convention in this module is for the +first dimension to represent # of data points (or # of blocks in a block jackknife, since +a block is like a datapoint), and for the second dimension to represent the dimensionality +of the data. ''' from __future__ import division import numpy as np -from scipy.stats import norm -from scipy.stats import chi2 -#import statsmodels.api as sm from scipy.optimize import nnls +np.seterr(divide='raise', invalid='raise') + + +def _check_shape(x, y): + '''Check that x and y have the correct shapes (for regression jackknives).''' + if len(x.shape) != 2 or len(y.shape) != 2: + raise ValueError('x and y must be 2D arrays.') + if x.shape[0] != y.shape[0]: + raise ValueError( + 'Number of datapoints in x != number of datapoints in y.') + if y.shape[1] != 1: + raise ValueError('y must have shape (n_snp, 1)') + n, p = x.shape + if p > n: + raise ValueError('More dimensions than datapoints.') + + return (n, p) + +def _check_shape_block(xty_block_values, xtx_block_values): + '''Check that xty_block_values and xtx_block_values have correct shapes.''' + if xtx_block_values.shape[0:2] != xty_block_values.shape: + raise ValueError( + 'Shape of xty_block_values must equal shape of first two dimensions of xty_block_values.') + if len(xtx_block_values.shape) < 3: + raise ValueError('xtx_block_values must be a 3D array.') + if xtx_block_values.shape[1] != xtx_block_values.shape[2]: + raise ValueError( + 'Last two axes of xtx_block_values must have same dimension.') -def kill_brackets(x): - '''Get rid of annoying brackets in numpy arrayss''' - x = x.replace('[[ ','') - x = x.replace(' ]]','') - x = x.replace('[[ ','') - x = x.replace(' ]]','') - x = x.replace('[[','') - x = x.replace(']]','') - x = x.replace('[ ','') - x = x.replace(' ]','') - x = x.replace('[ ','') - x = x.replace(' ]','') - x = x.replace('[','') - x = x.replace(']','') - return(x) - -def _weight(x, w): - - ''' - Re-weights x by multiplying by w. - - Parameters - ---------- - x : np.matrix with shape (n_row, n_col) - Rows are observations. - w : np.matrix with shape (n_row, 1) - Regression weights. - - Returns - ------- - - x_new : np.matrix with shape (n_row, n_col) - x_new[i,j] = x[i,j] * w[i] - - ''' - if np.any(w <= 0): - raise ValueError('Weights must be > 0') - - w = np.sqrt(w/ float(np.sum(w))) - x_new = np.multiply(x, w) - return x_new - - -def _append_intercept(x): - - ''' - Appends an intercept term to the design matrix for a linear regression. - - Parameters - ---------- - x : np.matrix with shape (n_row, n_col) - Design matrix. Columns are predictors; rows are observations. - - Returns - ------- - - x_new : np.matrix with shape (n_row, n_col+1) - Design matrix with intercept term appended. - - ''' - - n_row = x.shape[0] - int = np.matrix(np.ones(n_row)).reshape((n_row,1)) - x_new = np.concatenate((x, int), axis=1) - return x_new - -def _gencov_weights(ld, w_ld, N1, N2, No, M, h1, h2, rho_g, rho): - - ''' - Computes appropriate regression weights to correct for heteroskedasticity in the - bivariate LDScore regression under and infinitesimal model. These regression weights are - approximately equal to the reciprocal of the conditional variance function - 1 / var(betahat1*betahat2 | LD Score) - - Parameters - ---------- - ld : np.matrix with shape (n_snp, 1) - LD Scores (non-partitioned) - w_ld : np.matrix with shape (n_snp, 1) - LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included - in the regression. - M : int > 0 - Number of SNPs used for estimating LD Score (need not equal number of SNPs included in - the regression). - N1, N2 : np.matrix of ints > 0 with shape (n_snp, 1) - Number of individuals sampled for each SNP for each study. - No : int - Number of overlapping individuals. - h1, h2 : float in [0,1] - Heritability estimates for each study. - rhog : float in [0,1] - Genetic covariance estimate. - rho : float in [0,1] - Phenotypic correlation estimate. - - Returns - ------- - w : np.matrix with shape (n_snp, 1) - Regression weights. Approx equal to reciprocal of conditional variance function. - - ''' - - h1 = max(h1,0) - h2=max(h2,0) - h1 = min(h1,1) - h2=min(h2,1) - rho_g = min(rho_g,1) - rho_g = max(rho_g, -1) - ld = np.fmax(ld, 1.0) - w_ld = np.fmax(w_ld, 1.0) - # prevent integer division bugs with np.divide - N1 = N1.astype(float); N2 = N2.astype(float); No = float(No) - a = h1*ld / M + np.divide(1.0, N1) - b = h2*ld / M + np.divide(1.0, N2) - c = rho_g*ld / M + np.divide(No*rho, np.multiply(N1,N2)) - het_w = np.divide(1.0, np.multiply(a, b) + 2*np.square(c)) - oc_w = np.divide(1.0, w_ld) - # the factor of 3 is for debugging -- for degenerate rg (same sumstats twice) - # the 3 makes the h2 weights equal to the gencov weights - w = 3*np.multiply(het_w, oc_w) - return w - -def _hsq_weights(ld, w_ld, N, M, hsq): - - ''' - Computes appropriate regression weights to correct for heteroskedasticity in the LD - Score regression under an infinitesimal model. These regression weights are - approximately equal to the reciprocal of the conditional variance function - 1 / var(chi^2 | LD Score) - - Parameters - ---------- - ld : np.matrix with shape (n_snp, 1) - LD Scores (non-partitioned). - w_ld : np.matrix with shape (n_snp, 1) - LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included - in the regression. - N : np.matrix of ints > 0 with shape (n_snp, 1) - Number of individuals sampled for each SNP. - M : int > 0 - Number of SNPs used for estimating LD Score (need not equal number of SNPs included in - the regression). - hsq : float in [0,1] - Heritability estimate. - - Returns - ------- - w : np.matrix with shape (n_snp, 1) - Regression weights. Approx equal to reciprocal of conditional variance function. - - ''' - hsq = max(hsq,0); hsq = min(hsq,1) - ld = np.fmax(ld, 1.0) - w_ld = np.fmax(w_ld, 1.0) - c = hsq * N / M - het_w = np.divide(1.0, np.square(1.0+np.multiply(c, ld))) - oc_w = np.divide(1.0, w_ld) - w = np.multiply(het_w, oc_w) - return w - - -def obs_to_liab(h2_obs, P, K): - - ''' - Converts heritability on the observed scale in an ascertained sample to heritability - on the liability scale in the population. - - Parameters - ---------- - h2_obs : float - Heritability on the observed scale in an ascertained sample. - P : float in [0,1] - Prevalence of the phenotype in the sample. - K : float in [0,1] - Prevalence of the phenotype in the population. - - Returns - ------- - h2_liab : float - Heritability of liability in the population and standard error. - - ''' - - if K <= 0 or K >= 1: - raise ValueError('K must be in the range (0,1)') - if P <= 0 or P >= 1: - raise ValueError('P must be in the range (0,1)') - - thresh = norm.isf(K) - z = norm.pdf(thresh) - conversion_factor = K**2 * (1-K)**2 / (P * (1-P) * z**2) - h2_liab = h2_obs * conversion_factor - return h2_liab - - -class Hsq(object): - ''' - The conflict between python and genetics capitalization conventions (capitalize - objects, but additive heritability is lowercase) makes me sad :-( - - Class for estimating heritability / partitioned heritability from summary statistics. - - Parameters - ---------- - chisq : np.matrix with shape (n_snp, 1) - Chi-square statistics. - ld : np.matrix with shape (n_snp, n_annot) - LD Scores. - w_ld : np.matrix with shape (n_snp, 1) - LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included - in the regression. - N : np.matrix of ints > 0 with shape (n_snp, 1) - Number of individuals sampled for each SNP. - M : np.matrix of ints with shape (1, n_annot) > 0 - Number of SNPs used for estimating LD Score (need not equal number of SNPs included in - the regression). - num_blocks : int, default = 1000 - Number of block jackknife blocks. - intercept : none or float - If none, fits LD Score regression w/ intercept. If float, constrains the intercept - to equal the given float. - - Attributes - ---------- - N : int - Sample size. In a case/control study, this should be total sample size: number of - cases + number of controls. NOT some measure of effective sample size that accounts - for case/control ratio. The case-control ratio comes into play when converting from - observed to liability scale (in the function obs_to_liab). - M : np.matrix of ints with shape (1, n_annot) - Total number of SNPs per category in the reference panel used for estimating LD Score. - M_tot : int - Total number of SNPs in the reference panel used for estimating LD Score. - n_annot : int - Number of partitions. - n_snp : int - Number of SNPs included in the regression. - mean_chisq : float - Mean chi-square. - w_mean_chisq : float - Weighted mean chi-square (with the same weights as the regression). - lambda_gc : float - Devlin and Roeder lambda GC (median chi-square / 0.4549). - autocor : float - Lag-1 autocorrelation between block jackknife pseudoerrors. If much above zero, the - block jackknife standard error will be unreliable. This can be solved by using a - larger block size. - hsq_cov : np.matrix with shape (n_annot, n_annot) - Block jackknife estimate of variance-covariance matrix of partitioned h2 estimates. - cat_hsq : np.matrix with shape (1, n_annot) - Partitioned heritability estimates. - cat_hsq_se : np.matrix with shape (1, n_annot) - Standard errors of partitioned heritability estimates. - intercept : float - LD Score regression intercept. - intercept_se : float - Standard error of LD Score regression intercept. - tot_hsq : float - Total h2g estimate. - tot_hsq_se : float - Block jackknife estimate of standard error of the total h2g estimate. - prop_hsq : np.matrix with shape (1, n_annot) - Proportion of h2 per annotation. - M_prop : np.matrix with shape (1, n_annot) - Proportion of SNPs (in reference panel) per annotation. - enrichment : np.matrix with shape (1, n_annot) - Per category enrichment of h2 relative to all SNPs. Precisely, - (h2 per SNP in category) / (h2 per SNP overall). - _jknife : LstsqJackknife - Jackknife object. - - Methods - ------- - _aggregate(y, x, M_tot) : - Aggregate estimator. - summary(ref_ld_colnames): - Returns a summary of the LD Score regression. - summary_intercept(): - Returns a summary of the LD Score regression focused on the intercept. - - ''' - def __init__(self, chisq, ref_ld, w_ld, N, M, num_blocks=200, non_negative=False, - intercept=None, slow=False): - - self.N = N - self.M = M - self.M_tot = float(np.sum(M)) - self.n_annot = ref_ld.shape[1] - self.n_snp = ref_ld.shape[0] - self.mean_chisq = np.mean(chisq) - self.constrain_intercept = intercept - # median and matrix don't play nice? - self.lambda_gc = np.median(np.asarray(chisq)) / 0.4549 - ref_ld_tot = np.sum(ref_ld, axis=1) - # dividing by mean N keeps the condition number under control - Nbar = np.mean(N) - self.Nbar = Nbar - x = np.multiply(N, ref_ld) / Nbar - if self.constrain_intercept is None: - x = _append_intercept(x) - chisq_m_int = chisq - 1 - else: - chisq_m_int = chisq - self.constrain_intercept - - agg_hsq = self._aggregate(chisq_m_int, np.multiply(N, ref_ld_tot), self.M_tot) - weights = _hsq_weights(ref_ld_tot, w_ld, N, self.M_tot, agg_hsq) - if np.all(weights == 0): - raise ValueError('Something is wrong, all regression weights are zero.') - - x = _weight(x, weights) - y = _weight(chisq_m_int, weights) - self.w_mean_chisq = np.average(chisq, weights=weights) - if non_negative: - self._jknife = LstsqJackknifeSlow(x, y, num_blocks, nn=True) - elif not slow: - self._jknife = LstsqJackknifeFast(x, y, num_blocks) - else: - self._jknife = LstsqJackknifeSlow(x, y, num_blocks) - - self.coef = self._jknife.est[0,0:self.n_annot] / Nbar - self.coef_cov = self._jknife.jknife_cov[0:self.n_annot,0:self.n_annot] / (Nbar**2) - self.coef_se = np.sqrt(np.diag(self.coef_cov)) - self.cat_hsq = np.multiply(self.M, self.coef) - self.cat_hsq_cov = np.multiply(np.dot(self.M.T,self.M), self.coef_cov) - self.cat_hsq_se = np.sqrt(np.diag(self.cat_hsq_cov)) - self.tot_hsq = np.sum(self.cat_hsq) - self.tot_hsq_cov = np.sum(self.cat_hsq_cov) - self.tot_hsq_se = np.sqrt(self.tot_hsq_cov) - numer_delete_vals = np.multiply(self.M,self._jknife.delete_values[:,0:self.n_annot]) / Nbar - denom_delete_vals = np.sum(numer_delete_vals,axis=1)*np.ones(self.n_annot) - prop_hsq_est = self.cat_hsq/self.tot_hsq - self.prop_hsq_j = RatioJackknife(prop_hsq_est,numer_delete_vals,denom_delete_vals) - self.prop_hsq = self.prop_hsq_j.est - self.prop_hsq_se = self.prop_hsq_j.jknife_se - self.prop_hsq_cov = self.prop_hsq_j.jknife_cov - - if intercept is None: - self.intercept = self._jknife.est[0,self.n_annot] + 1 - self.intercept_se = self._jknife.jknife_se[0,self.n_annot] - if self.mean_chisq > 1: - self.ratio_se = self.intercept_se / (self.mean_chisq - 1) - self.ratio = (self.intercept - 1) / (self.mean_chisq - 1) - else: - self.ratio = float('nan') - self.ratio_se = float('nan') - - self.M_prop = self.M / self.M_tot - self.enrichment = np.divide(self.cat_hsq, self.M) / (self.tot_hsq/self.M_tot) - - def _aggregate(self, y, x, M_tot): - '''Aggregate estimator. For use in regression weights.''' - numerator = np.mean(y) - denominator = np.mean(x) / M_tot - agg = numerator / denominator - return agg - - def summary(self, ref_ld_colnames, overlap=False, outfile = None): - '''Print information about LD Score Regression''' - out = [] - out.append('Total observed scale h2: '+str(np.matrix(self.tot_hsq))+\ - ' ('+str(np.matrix(self.tot_hsq_se))+')') - if self.n_annot > 1: - if not overlap: - out.append( 'Categories: '+' '.join(ref_ld_colnames)) - out.append( 'Observed scale h2: '+ str(np.matrix(self.cat_hsq))) - out.append( 'Observed scale h2 SE: '+str(np.matrix(self.cat_hsq_se))) - out.append( 'Proportion of SNPs: '+str(np.matrix(self.M_prop))) - out.append( 'Proportion of h2g: ' +str(np.matrix(self.prop_hsq))) - out.append( 'Enrichment: '+str(np.matrix(self.enrichment))) - out.append( 'Coefficients: '+str(self.coef)) - out.append( 'Coefficient SE: '+str(self.coef_se)) - else: - out.append( 'Partitioned heritabilities and enrichments printed to {}.results'.format(outfile)) - out.append( 'Lambda GC: '+ str(np.matrix(self.lambda_gc))) - out.append( 'Mean Chi^2: '+ str(np.matrix(self.mean_chisq))) - if self.constrain_intercept is not None: - out.append( 'Intercept: constrained to {C}'.format(C=np.matrix(self.constrain_intercept))) - else: - out.append( 'Intercept: '+ str(np.matrix(self.intercept))+\ - ' ('+str(np.matrix(self.intercept_se))+')') - - out = '\n'.join(out) - return kill_brackets(out) - - def summary_intercept(self): - '''Print information about LD Score regression intercept.''' - - out = [] - out.append( 'Observed scale h2: '+str(np.matrix(self.tot_hsq))+' ('+\ - str(np.matrix(self.tot_hsq_se))+')') - out.append( 'Lambda GC: '+ str(np.matrix(self.lambda_gc))) - out.append( 'Mean Chi^2: '+ str(np.matrix(self.mean_chisq))) - out.append( 'Weighted Mean Chi^2: '+ str(np.matrix(self.w_mean_chisq))) - out.append( 'Intercept: '+ str(np.matrix(self.intercept))+\ - ' ('+str(np.matrix(self.intercept_se))+')') - - if self.mean_chisq > 1: - out.append( 'Ratio: '+str(np.matrix(self.ratio))+\ - ' ('+str(np.matrix(self.ratio_se))+')') - else: - out.append( 'Ratio: NA (mean chi^2 < 1)' ) - - out = '\n'.join(out) - return kill_brackets(out) - - -class Gencov(object): - - ''' - Class for estimating genetic covariance / partitioned genetic covariance from summary - statistics. Inherits from Hsq, but only for _aggregate. Note: the efficiency of the - estimate will be improved if first you estimate heritability for each trait then - feed these values into hsq1 and hsq2. This is only used for the regression weights. - - Could probably refactor so as to reuse more code from Hsq, but (a) the amount of - duplicated code is small and (b) although the procedure for estimating genetic - covariance and h2 is now very similar, there is no guarantee that it will stay this - way. - - Parameters - ---------- - bhat1, bhat2 : np.matrix with shape (n_snp, 1) - (Signed) effect-size estimates for each study. In a case control study, bhat should be - the signed square root of chi-square, where the sign is + if OR > 1 and - otherwise. - ld : np.matrix with shape (n_snp, n_annot) - LD Scores. - w_ld : np.matrix with shape (n_snp, 1) - LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included - in the regression. - N1, N2 : np.matrix of ints > 0 with shape (n_snp, 1) - Number of individuals sampled for each SNP for each study. - M : int > 0 - Number of SNPs used for estimating LD Score (need not equal number of SNPs included in - the regression). - hsq1, hsq2 : float - Heritability estimates for each study (used in regression weights). - N_overlap : int, default 0. - Number of overlapping samples. - rho : float in [-1,1] - Estimate of total phenotypic correlation between trait 1 and trait 2. Only used for - regression weights, and then only when N_overlap > 0. - num_blocks : int, default = 1000 - Number of block jackknife blocks. - intercept : none or float - If none, fits LD Score regression w/ intercept. If float, constrains the intercept - to equal the given float. - - Attributes - ---------- - N1, N2 : int - Sample sizes. In a case/control study, this should be total sample size: number of - cases + number of controls. NOT some measure of effective sample size that accounts - for case/control ratio. The case-control ratio comes into play when converting from - observed to liability scale (in the function obs_to_liab). - gencov_cov : np.matrix with shape (n_annot, n_annot) - Block jackknife estimate of variance-covariance matrix of the partitioned h2 estimates. - cat_gencov : np.matrix with shape (1, n_annot) - Partitioned heritability estimates. - cat_gencov_se : np.matrix with shape (1, n_annot) - Standard errors of partitioned heritability estimates. - intercept : float - LD Score regression intercept. NB this is not on the same scale as the intercept from - the regression chisq ~ LD Score. The intercept from the genetic covariance regression - is on the same scale as N_overlap / (N1*N2). - intercept_se : float - Standard error of LD Score regression intercept. - tot_gencov : float - Total h2g estimate. - tot_gencov_se : float - Block jackknife estimate of standard error of the total h2g estimate. - prop_gencov : np.matrix with shape (1, n_annot) - Proportion of genetic covariance per annotation. - M_prop : np.matrix with shape (1, n_annot) - Proportion of SNPs (in reference panel) per annotation. - enrichment : np.matrix with shape (1, n_annot) - Per category enrichment of h2 relative to all SNPs. Precisely, - (gencov per SNP in category) / (gencov per SNP overall). - _jknife : LstsqJackknife - Jackknife object. - - ''' - def __init__(self, bhat1, bhat2, ref_ld, w_ld, N1, N2, M, hsq1, hsq2, N_overlap=None, - rho=None, num_blocks=200, intercept=None, slow=False): - - self.N1 = N1 - self.N2 = N2 - self.N_overlap = N_overlap if N_overlap is not None else 0 - self.M = M - self.M_tot = np.sum(M) - self.n_annot = ref_ld.shape[1] - self.n_snp = ref_ld.shape[0] - self.constrain_intercept = intercept - - ref_ld_tot = np.sum(ref_ld, axis=1) - n1n2 = float(np.dot(self.N1.T, self.N2))/self.n_snp - y = np.multiply(bhat1, bhat2) - if intercept is None: - x = _append_intercept(ref_ld) - else: - x = ref_ld - # input the intercept multiplied by N1*N2 for ease of use - # internally work with betahat rather than Z score - y = y - intercept/n1n2 - rho = 1 - N_overlap = intercept - - agg_gencov = self._aggregate(y, ref_ld_tot, self.M_tot, rho, N_overlap) - weights = _gencov_weights(ref_ld_tot, w_ld, N1, N2, N_overlap, self.M_tot, hsq1, hsq2, - agg_gencov, rho) - if np.all(weights == 0): - raise ValueError('Something is wrong, all regression weights are zero.') - - x = _weight(x, weights) - y = _weight(y, weights) - - if not slow: - self._jknife = LstsqJackknifeFast(x, y, num_blocks) - else: - self._jknife = LstsqJackknifeSlow(x, y, num_blocks) - - no_intercept_cov = self._jknife.jknife_cov[0:self.n_annot,0:self.n_annot] - self.gencov_cov = np.multiply(np.dot(self.M.T,self.M), no_intercept_cov) - self.coef = self._jknife.est[0,0:self.n_annot] - self.coef_se = np.sqrt(np.diag(no_intercept_cov)) - self.cat_gencov = np.multiply(self.M, self._jknife.est[0,0:self.n_annot]) - self.cat_gencov_se = np.multiply(self.M, self._jknife.jknife_se[0,0:self.n_annot]) - self.tot_gencov = np.sum(self.cat_gencov) - self.tot_gencov_se = np.sqrt(np.sum(M*no_intercept_cov*self.M.T)) - self.prop_gencov = self.cat_gencov / self.tot_gencov - self.M_prop = self.M / self.M_tot - self.enrichment = np.divide(self.cat_gencov, self.M) / (self.tot_gencov/self.M_tot) - self.Z = self.tot_gencov / self.tot_gencov_se - self.P_val = chi2.sf(self.Z**2, 1, loc=0, scale=1) - if intercept is None: - self.intercept = self._jknife.est[0,self.n_annot]*n1n2 - self.intercept_se = self._jknife.jknife_se[0,self.n_annot]*n1n2 - - def _aggregate(self, y, x, M_tot, rho=None, N_overlap=None): - '''Aggregate estimator. For use in regression weights.''' - numerator = np.mean(y) - denominator = np.mean(x) / M_tot - agg = numerator / denominator - return agg - - def summary(self, ref_ld_colnames, overlap=False): - '''Print output of jk.Gencov object''' - out = [] - out.append('Total observed scale gencov: '+str(np.matrix(self.tot_gencov))+' ('+\ - str(np.matrix(self.tot_gencov_se))+')') - out.append('Z-score: '+str(np.matrix(self.Z))) - out.append('P: '+str(np.matrix(self.P_val))) - - if self.n_annot > 1: - out.append( 'Categories: '+ str(' '.join(ref_ld_colnames))) - if not overlap: - out.append( 'Observed scale gencov: '+str(np.matrix(self.cat_gencov))) - out.append( 'Observed scale gencov SE: '+str(np.matrix(self.cat_gencov_se))) - out.append( 'Proportion of SNPs: '+str(np.matrix(self.M_prop))) - out.append( 'Proportion of gencov: ' +str(np.matrix(self.prop_gencov))) - out.append( 'Enrichment: '+str(np.matrix(self.enrichment))) - - if self.constrain_intercept is not None: - out.append( 'Intercept: constrained to {C}'.format(C=np.matrix(self.constrain_intercept))) - else: - out.append( 'Intercept: '+ str(np.matrix(self.intercept))+\ - ' ('+str(np.matrix(self.intercept_se))+')') - - out = '\n'.join(out) - return kill_brackets(out) - - -class Gencor(object): - - ''' - Class for estimating genetic correlation from summary statistics. Implemented as a ratio - estimator with block jackknife bias correction (the block jackknife allows for - estimation of reasonably good standard errors from dependent data and decreases the - bias in a ratio estimate from O(1/N) to O(1/N^2), where N = number of data points). - - Parameters - ---------- - bhat1, bhat2 : np.matrix with shape (n_snp, 1) - (Signed) effect-size estimates for each study. In a case control study, bhat should be - the signed square root of chi-square, where the sign is + if OR > 1 and - otherwise. - ld : np.matrix with shape (n_snp, n_annot) - LD Scores. - w_ld : np.matrix with shape (n_snp, 1) - LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included - in the regression. - N1, N2 : np.matrix of ints > 0 with shape (n_snp, 1) - Number of individuals sampled for each SNP for each study. - M : int > 0 - Number of SNPs used for estimating LD Score (need not equal number of SNPs included in - the regression). - hsq1, hsq2 : float - Heritability estimates for each study (used in regression weights). - N_overlap : int, default 0. - Number of overlapping samples. - rho : float in [-1,1] - Estimate of total phenotypic correlation between trait 1 and trait 2. Only used for - regression weights, and then only when N_overlap > 0. - num_blocks : int, default = 1000 - Number of block jackknife blocks. - intercepts : list with length 3 - Intercepts for constrained LD Score regression. If None, then do not constrain - intercept. - - Attributes - ---------- - N1, N2 : int - Sample sizes. In a case/control study, this should be total sample size: number of - cases + number of controls. NOT some measure of effective sample size that accounts - for case/control ratio. The case-control ratio comes into play when converting from - observed to liability scale (in the function obs_to_liab). - M : np.matrix of ints with shape (1, n_annot) - Total number of SNPs per category in the reference panel used for estimating LD Score. - M_tot : int - Total number of SNPs in the reference panel used for estimating LD Score. - n_annot : int - Number of partitions. - n_snp : int - Number of SNPs included in the regression. - hsq1, hsq2 : Hsq - Heritability estimates for traits 1 and 2, respectively. - gencov : Gencov - Genetic covariance estimate. - autocor : float - Lag-1 autocorrelation between ratio block jackknife pseudoerrors. If much above zero, - the block jackknife standard error will be unreliable. This can be solved by using a - larger block size. - tot_gencor : float - Total genetic correlation. - tot_gencor_se : float - Genetic correlation standard error. - _gencor : RatioJackknife - Jackknife used for estimating genetic correlation. - - ''' - def __init__(self, bhat1, bhat2, ref_ld, w_ld, N1, N2, M, intercepts, - N_overlap=None, rho=None, num_blocks=200, return_silly_things=False, first_hsq=None, - slow=False): - - self.N1 = N1 - self.N2 = N2 - self.N_overlap = N_overlap if N_overlap is not None else 0 - self.rho = rho if rho is not None else 0 - self.M = M - self.M_tot = np.sum(M) - self.n_annot = ref_ld.shape[1] - self.n_snp = ref_ld.shape[0] - self.intercepts = intercepts - self.huge_se_flag = False - self.negative_hsq_flag = False - self.out_of_bounds_flag = False - self.tiny_hsq_flag = False - self.return_silly_things = return_silly_things - chisq1 = np.multiply(N1, np.square(bhat1)) - chisq2 = np.multiply(N2, np.square(bhat2)) - - if first_hsq is None: - self.hsq1 = Hsq(chisq1, ref_ld, w_ld, N1, M, num_blocks=num_blocks, - non_negative=False, intercept=intercepts[0], slow=slow) - else: - self.hsq1 = first_hsq - - self.hsq2 = Hsq(chisq2, ref_ld, w_ld, N2, M, num_blocks=num_blocks, - non_negative=False, intercept=intercepts[1], slow=slow) - self.gencov = Gencov(bhat1, bhat2, ref_ld, w_ld, N1, N2, M, self.hsq1.tot_hsq, - self.hsq2.tot_hsq, N_overlap=self.N_overlap, rho=self.rho, num_blocks=num_blocks, - intercept=intercepts[2], slow=slow) - - if (self.hsq1.tot_hsq <= 0 or self.hsq2.tot_hsq <= 0): - self.negative_hsq_flag = True - - # total genetic correlation - self.tot_gencor_biased = self.gencov.tot_gencov /\ - np.sqrt(self.hsq1.tot_hsq * self.hsq2.tot_hsq) - numer_delete_values = self.cat_to_tot(self.gencov._jknife.delete_values[:,0:self.n_annot], self.M) - hsq1_delete_values = self.cat_to_tot(self.hsq1._jknife.delete_values[:,0:self.n_annot], self.M)\ - / self.hsq1.Nbar - hsq2_delete_values = self.cat_to_tot(self.hsq2._jknife.delete_values[:,0:self.n_annot], self.M)\ - / self.hsq2.Nbar - denom_delete_values = np.sqrt(np.multiply(hsq1_delete_values, hsq2_delete_values)) - self.gencor = RatioJackknife(self.tot_gencor_biased, numer_delete_values, denom_delete_values) - self.tot_gencor = float(self.gencor.jknife_est) - if (self.tot_gencor > 1.2 or self.tot_gencor < -1.2): - self.out_of_bounds_flag = True - elif np.isnan(self.tot_gencor): - self.tiny_hsq_flag = True - - self.tot_gencor_se = float(self.gencor.jknife_se) - if self.tot_gencor_se > 0.25: - self.huge_se_flag = True - - self.Z = self.tot_gencor / self.tot_gencor_se - self.P_val = chi2.sf(self.Z**2, 1, loc=0, scale=1) - - def cat_to_tot(self, x, M): - '''Converts per-category pseudovalues to total pseudovalues.''' - return np.dot(x, M.T) - - def summary(self): - '''Reusable code for printing output of jk.Gencor object''' - out = [] - - if self.negative_hsq_flag and not self.return_silly_things: - out.append('Genetic Correlation: nan (nan) (heritability estimate < 0) ') - out.append('Z-score: nan (nan) (heritability estimate < 0)') - out.append('P: nan (nan) (heritability estimate < 0)') - out.append('WARNING: One of the h2 estimates was < 0. Consult the documentation.') - out = '\n'.join(out) - - elif self.tiny_hsq_flag and not self.return_silly_things: - out.append('Genetic Correlation: nan (nan) (heritability close to 0) ') - out.append('Z-score: nan (nan) (heritability close to 0)') - out.append('P: nan (nan) (heritability close to 0)') - out.append('WARNING: one of the h2\'s was < 0 in one of the jackknife blocks. Consult the documentation.') - out = '\n'.join(out) - - elif self.huge_se_flag and not self.return_silly_things: - warn_msg = ' WARNING: asymptotic P-values may not be valid when SE(rg) is very high.' - out.append('Genetic Correlation: '+str(np.matrix(self.tot_gencor))+' ('+\ - str(np.matrix(self.tot_gencor_se))+')') - out.append('Z-score: '+str(np.matrix(self.Z))) - out.append('P: '+str(np.matrix(self.P_val))+warn_msg) - out = '\n'.join(out) - - elif self.out_of_bounds_flag and not self.return_silly_things: - out.append('Genetic Correlation: nan (nan) (rg out of bounds) ') - out.append('Z-score: nan (nan) (rg out of bounds)') - out.append('P: nan (nan) (rg out of bounds)') - out.append('WARNING: rg was out of bounds. Consult the documentation.') - out = '\n'.join(out) - - else: - out.append('Genetic Correlation: '+str(np.matrix(self.tot_gencor))+' ('+\ - str(np.matrix(self.tot_gencor_se))+')') - out.append('Z-score: '+str(np.matrix(self.Z))) - out.append('P: '+str(np.matrix(self.P_val))) - if self.return_silly_things and \ - (self.huge_se_flag or self.negative_hsq_flag or self.out_of_bounds_flag or self.tiny_hsq_flag): - out.append('WARNING: returning silly results because you asked for them.') - out = '\n'.join(out) - - return kill_brackets(out) + return xtx_block_values.shape[0:2] class Jackknife(object): - - def __init__(self, x, y, num_blocks, separators=None, *args, **kwargs): - if len(x.shape) <= 1: - x = np.atleast_2d(x).T - if len(y.shape) <= 1: - y = np.atleast_2d(y).T - - self.N = y.shape[0] - if self.N != x.shape[0]: - raise ValueError('Number of data points in y != number of data points in x.') - - self.num_blocks = num_blocks - self.separators = separators - self.output_dim = x.shape[1] - if self.num_blocks > self.N: - msg = 'Number of jackknife blocks ({N1}) > number of SNPs ({N2}). ' - msg += 'Reduce the number of jackknife blocks with the --num-blocks flag.' - raise ValueError(msg.format(N1=num_blocks, N2=N)) - - self.__init_specific__(x, y, *args, **kwargs) - - def __jknife__(self, pseudovalues, num_blocks): - '''Converts pseudovalues to jackknife estimates''' - jknife_est = np.atleast_2d(np.mean(pseudovalues, axis=0) ) - jknife_var = np.atleast_2d(np.var(pseudovalues, axis=0) / (num_blocks - 1) ) - jknife_se = np.atleast_2d(np.std(pseudovalues, axis=0) / np.sqrt(num_blocks - 1)) - jknife_cov = np.atleast_2d(np.cov(pseudovalues.T) / (num_blocks)) - return (jknife_est, jknife_var, jknife_se, jknife_cov) - - def __delete_values_to_pseudovals__(self, delete_values, est): - '''Converts block values to pseudovalues''' - pseudovalues = np.matrix(np.zeros((self.num_blocks, self.output_dim))) - for j in xrange(0,self.num_blocks): - pseudovalues[j,...] = self.num_blocks*est - (self.num_blocks-1)*delete_values[j,...] - - return pseudovalues - - def __get_separators__(self): - '''Define block boundaries''' - if self.separators is None: - N = self.N - block_size = int(N/self.num_blocks) - separators = np.arange(0,N,block_size) - remainder = N - separators[-1] - - if len(separators) == self.num_blocks: - separators = np.hstack([separators,N]) - else: - for r in range(remainder): - separators[-(r+1)] += remainder - r - else: - separators = self.separators - - return separators - + + ''' + Base class for jackknife objects. Input involves x,y, so this base class is tailored + for statistics computed from independent and dependent variables (e.g., regressions). + The __delete_vals_to_pseudovalues__ and __jknife__ methods will still be useful for other + sorts of statistics, but the __init__ method will need to be overriden. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + *args, **kwargs : + Arguments for inheriting jackknives. + + Attributes + ---------- + n_blocks : int + Number of jackknife blocks + p : int + Dimensionality of the independent varianble + N : int + Number of datapoints (equal to x.shape[0]) + + Methods + ------- + jknife(pseudovalues): + Computes jackknife estimate and variance from the jackknife pseudovalues. + delete_vals_to_pseudovalues(delete_vals, est): + Converts delete values and the whole-data estimate to pseudovalues. + get_separators(): + Returns (approximately) evenly-spaced jackknife block boundaries. + ''' + + def __init__(self, x, y, n_blocks=None, separators=None): + self.N, self.p = _check_shape(x, y) + if separators is not None: + if max(separators) != self.N: + raise ValueError( + 'Max(separators) must be equal to number of data points.') + if min(separators) != 0: + raise ValueError('Max(separators) must be equal to 0.') + self.separators = sorted(separators) + self.n_blocks = len(separators) - 1 + elif n_blocks is not None: + self.n_blocks = n_blocks + self.separators = self.get_separators(self.N, self.n_blocks) + else: + raise ValueError('Must specify either n_blocks are separators.') + + if self.n_blocks > self.N: + raise ValueError('More blocks than data points.') + + @classmethod + def jknife(cls, pseudovalues): + ''' + Converts pseudovalues to jackknife estimate and variance. + + Parameters + ---------- + pseudovalues : np.matrix pf floats with shape (n_blocks, p) + + Returns + ------- + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + + ''' + n_blocks = pseudovalues.shape[0] + jknife_cov = np.atleast_2d(np.cov(pseudovalues.T, ddof=1) / n_blocks) + jknife_var = np.atleast_2d(np.diag(jknife_cov)) + jknife_se = np.atleast_2d(np.sqrt(jknife_var)) + jknife_est = np.atleast_2d(np.mean(pseudovalues, axis=0)) + return (jknife_est, jknife_var, jknife_se, jknife_cov) + + @classmethod + def delete_values_to_pseudovalues(cls, delete_values, est): + ''' + Converts whole-data estimate and delete values to pseudovalues. + + Parameters + ---------- + delete_values : np.matrix with shape (n_blocks, p) + Delete values. + est : np.matrix with shape (1, p): + Whole-data estimate. + + Returns + ------- + pseudovalues : np.matrix with shape (n_blocks, p) + Psuedovalues. + + Raises + ------ + ValueError : + If est.shape != (1, delete_values.shape[1]) + + ''' + n_blocks, p = delete_values.shape + if est.shape != (1, p): + raise ValueError( + 'Different number of parameters in delete_values than in est.') + + return n_blocks * est - (n_blocks - 1) * delete_values + + @classmethod + def get_separators(cls, N, n_blocks): + '''Define evenly-spaced block boundaries.''' + return np.floor(np.linspace(0, N, n_blocks + 1)).astype(int) + class LstsqJackknifeSlow(Jackknife): - def __init_specific__(self, x, y, nn=False): - if nn: - self.reg_func = lambda x,y: nnls(x, y)[0] - else: - self.reg_func = lambda x,y: np.linalg.lstsq(x, y)[0] - self.est = np.matrix(self.reg_func(x,np.array(y).T[0])) - self.delete_values = self.__compute_delete_values__(x, y, self.num_blocks) - self.pseudovalues = self.__delete_values_to_pseudovals__(self.delete_values,self.est) - (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ - self.__jknife__(self.pseudovalues, self.num_blocks) - - def __compute_delete_values__(self, x, y, num_blocks): - N = self.N - separators = self.__get_separators__() - delete_values = np.matrix(np.zeros((self.num_blocks, self.output_dim))) - for i in range(self.num_blocks): - # s = block start SNP index; e = block end SNP index - s = separators[i] - e = separators[i+1] - x_delete = np.vstack([x[0:s,...],x[e:,...]]) - y_delete = np.array(np.vstack([y[0:s,...],y[e:,...]])).T[0] - delete_values[i,...] = self.reg_func(x_delete,y_delete) - - return delete_values - - + ''' + Slow linear-regression block jackknife. This class computes delete values directly, + rather than forming delete values from block values. Useful for testing and for + non-negative least squares (which as far as I am aware does not admit a fast block + jackknife algorithm). + + Inherits from Jackknife class. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + nn: bool + Non-negative least-squares? + + Attributes + ---------- + est : np.matrix with shape (1, p) + FWLS estimate. + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + delete_vals : np.matrix with shape (n_blocks, p) + Jackknife delete values. + + Methods + ------- + delete_values(x, y, func, s): + Compute delete values of func(x, y) the slow way, with blocks defined by s. + + ''' + + def __init__(self, x, y, n_blocks=None, nn=False, separators=None): + Jackknife.__init__(self, x, y, n_blocks, separators) + if nn: # non-negative least squares + func = lambda x, y: np.atleast_2d(nnls(x, np.array(y).T[0])[0]) + else: + func = lambda x, y: np.atleast_2d( + np.linalg.lstsq(x, np.array(y).T[0])[0]) + + self.est = func(x, y) + self.delete_values = self.delete_values(x, y, func, self.separators) + self.pseudovalues = self.delete_values_to_pseudovalues( + self.delete_values, self.est) + (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ + self.jknife(self.pseudovalues) + + @classmethod + def delete_values(cls, x, y, func, s): + ''' + Compute delete values by deleting one block at a time. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + func : function (n, p) , (n, 1) --> (1, p) + Function of x and y to be jackknived. + s : list of ints + Block separators. + + Returns + ------- + delete_values : np.matrix with shape (n_blocks, p) + Delete block values (with n_blocks blocks defined by parameter s). + + Raises + ------ + ValueError : + If x.shape[0] does not equal y.shape[0] or x and y are not 2D. + + ''' + _check_shape(x, y) + d = [func(np.vstack([x[0:s[i], ...], x[s[i + 1]:, ...]]), np.vstack([y[0:s[i], ...], y[s[i + 1]:, ...]])) + for i in xrange(len(s) - 1)] + + return np.concatenate(d, axis=0) + + class LstsqJackknifeFast(Jackknife): - ''' - Fast least-squares block jackknife. - - Less numerically stable than the other option, so probably don't use for multivariate - regression. Fine for univariate regression. - - ''' - def __init_specific__(self, x, y): - self.block_vals = self.__compute_block_vals__(x, y, self.num_blocks) - self.est = self.__block_vals_to_est__(self.block_vals) - (self.pseudovalues, self.delete_values) =\ - self.__block_vals_to_pseudovals__(self.block_vals, self.est) - (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ - self.__jknife__(self.pseudovalues, self.num_blocks) - - def __compute_block_vals__(self, x, y, num_blocks): - N = self.N - separators = self.__get_separators__() - xty_block_vals = []; xtx_block_vals = [] - for i in range(len(separators)-1): - # s = block start SNP index; e = block end SNP index - s = separators[i] - e = separators[i+1] - xty = np.dot( x[s:e,...].T, y[s:e,...] ) - xtx = np.dot( x[s:e,...].T, x[s:e,...] ) - xty_block_vals.append(xty) - xtx_block_vals.append(xtx) - - block_vals = (xty_block_vals, xtx_block_vals) - return block_vals - - def __block_vals_to_pseudovals__(self, block_vals, est): - '''Converts block values to pseudovalues''' - pseudovalues = np.matrix(np.zeros((self.num_blocks, self.output_dim))) - delete_values = np.matrix(np.zeros((self.num_blocks, self.output_dim))) - xty_blocks = block_vals[0] - xtx_blocks = block_vals[1] - xty_tot = np.sum(xty_blocks, axis=0) - xtx_tot = np.sum(xtx_blocks, axis=0) - try: - for j in xrange(0,self.num_blocks): - delete_xty_j = xty_tot - xty_blocks[j] - delete_xtx_inv_j = np.linalg.inv(xtx_tot - xtx_blocks[j]) - delete_value_j = np.dot(delete_xtx_inv_j, delete_xty_j).T - pseudovalues[j,...] = self.num_blocks*est - (self.num_blocks-1)*delete_value_j - delete_values[j,...] = delete_value_j - except np.linalg.linalg.LinAlgError as e: - msg = 'Singular design matrix in at least one delete-k jackknife block. ' - msg += 'Check that you have not passed highly correlated partitioned LD Scores. ' - msg += 'Sometimes changing the block size can help. ' - raise np.linalg.linalg.LinAlgError(msg, e) - - return (pseudovalues, delete_values) - - def __block_vals_to_est__(self, block_vals): - '''Converts block values to the total (non-jackknife) estimate''' - xty_blocks = block_vals[0] - xtx_blocks = block_vals[1] - xty = np.sum(xty_blocks, axis=0) - try: - xtx_inv = np.linalg.inv(np.sum(xtx_blocks, axis=0)) - except np.linalg.linalg.LinAlgError as e: - msg = "Singular design matrix in at least one delete-k jackknife block. " - msg += 'Check that you have not passed highly correlated partitioned LD Scores.' - raise np.linalg.linalg.LinAlgError(msg, e) - - return np.matrix(np.dot(xtx_inv, xty).T) - - -class JackknifeAggregate(LstsqJackknifeSlow): - - def __init_specific__(self, x, y, annot_matrix): - x_small = np.dot(annot_matrix.T,x) - y_small = np.dot(annot_matrix.T,y) - self.est = np.dot(np.linalg.inv(x_small),y_small).T - self.delete_values = self.__compute_delete_values__(x,y,annot_matrix,num_blocks) - self.pseudovalues = self.__delete_values_to_pseudovals__(self.delete_values,self.est) - (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ - self.__jknife__(self.pseudovalues, self.num_blocks) - - def __compute_delete_values__(self, x, y, annot_matrix, num_blocks): - N = self.N - separators = self.__get_separators__() - delete_values = np.matrix(np.zeros((self.num_blocks, self.output_dim))) - for i in range(num_blocks): - # s = block start SNP index; e = block end SNP index - s = separators[i] - e = separators[i+1] - annot_delete = np.vstack([annot_matrix[0:s,...],annot_matrix[e:,...]]) - x_delete = np.vstack([x[0:s,...],x[e:,...]]) - x_small_delete = np.dot(annot_delete.T,x_delete) - y_delete = np.vstack([y[0:s,...],y[e:,...]]) - y_small_delete = np.dot(annot_delete.T,y_delete) - delete_values[i,...] = np.dot(np.linalg.inv(x_small_delete),y_small_delete).T - - return delete_values + ''' + Fast block jackknife for linear regression. + + Inherits from Jackknife class. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + + Attributes + ---------- + est : np.matrix with shape (1, p) + FWLS estimate. + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + delete_vals : np.matrix with shape (n_blocks, p) + Jackknife delete values. + + Methods + ------- + block_values(x, y, n_blocks) : + Computes block values for the regression y~x. + block_values_to_est(block_values) : + Computes whole-data estimate from block values. + block_values_to_pseudovalues(block_values, est) : + Computes pseudovalues and delete values in a single pass over the block values. + + ''' + + def __init__(self, x, y, n_blocks=None, separators=None): + Jackknife.__init__(self, x, y, n_blocks, separators) + xty, xtx = self.block_values(x, y, self.separators) + self.est = self.block_values_to_est(xty, xtx) + self.delete_values = self.block_values_to_delete_values(xty, xtx) + self.pseudovalues = self.delete_values_to_pseudovalues( + self.delete_values, self.est) + (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ + self.jknife(self.pseudovalues) + + @classmethod + def block_values(cls, x, y, s): + ''' + Compute block values. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + s : list of ints + Block separators. + + Returns + ------- + xty_block_values : np.matrix with shape (n_blocks, p) + Block values of X^T Y. + xtx_block_values : 3d np array with shape (n_blocks, p, p) + Block values of X^T X. + + Raises + ------ + ValueError : + If x.shape[0] does not equal y.shape[0] or x and y are not 2D. + + ''' + n, p = _check_shape(x, y) + n_blocks = len(s) - 1 + xtx_block_values = np.zeros((n_blocks, p, p)) + xty_block_values = np.zeros((n_blocks, p)) + for i in xrange(n_blocks): + xty_block_values[i, ...] = np.dot( + x[s[i]:s[i + 1], ...].T, y[s[i]:s[i + 1], ...]).reshape((1, p)) + xtx_block_values[i, ...] = np.dot( + x[s[i]:s[i + 1], ...].T, x[s[i]:s[i + 1], ...]) + + return (xty_block_values, xtx_block_values) + + @classmethod + def block_values_to_est(cls, xty_block_values, xtx_block_values): + ''' + Converts block values to the whole-data linear regression estimate. + + Parameters + ---------- + xty_block_values : np.matrix with shape (n_blocks, p) + Block values of X^T Y. + xtx_block_values : 3D np.array with shape (n_blocks, p, p) + Block values of X^T X + + Returns + ------- + est : np.matrix with shape (1, p) + Whole data estimate. + + Raises + ------ + LinAlgError : + If design matrix is singular. + ValueError : + If the last two dimensions of xtx_block_values are not equal or if the first two + dimensions of xtx_block_values do not equal the shape of xty_block_values. + + ''' + n_blocks, p = _check_shape_block(xty_block_values, xtx_block_values) + xty = np.sum(xty_block_values, axis=0) + xtx = np.sum(xtx_block_values, axis=0) + return np.linalg.solve(xtx, xty).reshape((1, p)) + + @classmethod + def block_values_to_delete_values(cls, xty_block_values, xtx_block_values): + ''' + Converts block values to delete values. + + Parameters + ---------- + xty_block_values : np.matrix with shape (n_blocks, p) + Block values of X^T Y. + xtx_block_values : 3D np.array with shape (n_blocks, p, p) + Block values of X^T X + est : np.matrix with shape (1, p) + Whole data estimate + + Returns + ------- + delete_values : np.matrix with shape (n_blocks, p) + Delete Values. + + Raises + ------ + LinAlgError : + If delete design matrix is singular. + ValueError : + If the last two dimensions of xtx_block_values are not equal or if the first two + dimensions of xtx_block_values do not equal the shape of xty_block_values. + + ''' + n_blocks, p = _check_shape_block(xty_block_values, xtx_block_values) + delete_values = np.zeros((n_blocks, p)) + xty_tot = np.sum(xty_block_values, axis=0) + xtx_tot = np.sum(xtx_block_values, axis=0) + for j in xrange(n_blocks): + delete_xty = xty_tot - xty_block_values[j] + delete_xtx = xtx_tot - xtx_block_values[j] + delete_values[j, ...] = np.linalg.solve( + delete_xtx, delete_xty).reshape((1, p)) + + return delete_values class RatioJackknife(Jackknife): - ''' - Block jackknife class for a ratio estimator (for genetic correlation estimation). - - Inherits from LstsqJackknife. - - Parameters - ---------- - est : float or np.array with shape (1, n_annot) - (Biased) ratio estimate (e.g., if we are estimate a = b / c, est should be \ - \hat{a} = \hat{b} / \hat{c}. - numer_delete_values : np.matrix with shape (n_blocks, n_annot) - Delete-k values for the numerator. - denom_delete_values: np.matrix with shape (n_blocks, n_annot) - Delete-k values for the denominator. - - Warning - ------- - If any of the delete-k block values for a category is zero, will return nan for - jknife_est and inf for jknife_var and jknife_se for that category. jknife_cov will - have the expected dimension, but the row and column corresponding to covariance with - the category with a zero delete-k block value will be nan. - - ''' - - def __init__(self, est, numer_delete_values, denom_delete_values): - if numer_delete_values.shape != denom_delete_values.shape: - raise ValueError('numer_delete_values.shape != denom_delete_values.shape.') - - self.est = est - self.numer_delete_values = numer_delete_values - self.denom_delete_values = denom_delete_values - self.num_blocks = numer_delete_values.shape[0] - self.output_dim = numer_delete_values.shape[1] - self.pseudovalues = self.__delete_values_to_pseudovals__(self.est,\ - self.denom_delete_values, self.numer_delete_values) - (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ - self.__jknife__(self.pseudovalues, self.num_blocks) - - def __delete_values_to_pseudovals__(self, est, denom, numer): - '''Converts delete-k values to pseudovalues.''' - pseudovalues = np.matrix(np.zeros((self.num_blocks, self.output_dim))) - for j in xrange(0,self.num_blocks): - pseudovalues[j,...] = self.num_blocks*est - (self.num_blocks - 1)*\ - numer[j,...]/denom[j,...] - - return pseudovalues + ''' + Block jackknife ratio estimate. + + Jackknife. + + Parameters + ---------- + est : float or np.array with shape (1, p) + Whole data ratio estimate + numer_delete_values : np.matrix with shape (n_blocks, p) + Delete values for the numerator. + denom_delete_values: np.matrix with shape (n_blocks, p) + Delete values for the denominator. + + Methods + ------- + delete_vals_to_pseudovalues(est, denom, num): + Converts denominator/ numerator delete values and the whole-data estimate to + pseudovalues. + + Raises + ------ + FloatingPointError : + If any entry of denom_delete_values is zero. + + Note that it is possible for the denominator to cross zero (i.e., be both positive + and negative) and still have a finite ratio estimate and SE, for example if the + numerator is fixed to 0 and the denominator is either -1 or 1. If the denominator + is noisily close to zero, then it is unlikely that the denominator will yield zero + exactly (and therefore yield an inf or nan), but delete values will be of the form + (numerator / close to zero) and -(numerator / close to zero), i.e., (big) and -(big), + and so the jackknife will (correctly) yield huge SE. + + ''' + + def __init__(self, est, numer_delete_values, denom_delete_values): + if numer_delete_values.shape != denom_delete_values.shape: + raise ValueError( + 'numer_delete_values.shape != denom_delete_values.shape.') + if len(numer_delete_values.shape) != 2: + raise ValueError('Delete values must be matrices.') + if len(est.shape) != 2 or est.shape[0] != 1 or est.shape[1] != numer_delete_values.shape[1]: + raise ValueError( + 'Shape of est does not match shape of delete values.') + + self.n_blocks = numer_delete_values.shape[0] + self.est = est + self.pseudovalues = self.delete_values_to_pseudovalues(self.est, + denom_delete_values, numer_delete_values) + (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ + self.jknife(self.pseudovalues) + + @classmethod + def delete_values_to_pseudovalues(cls, est, denom, numer): + ''' + Converts delete values to pseudovalues. + + Parameters + ---------- + est : np.matrix with shape (1, p) + Whole-data ratio estimate. + denom : np.matrix with shape (n_blocks, p) + Denominator delete values. + numer : np.matrix with shape (n_blocks, p) + Numerator delete values. + + Returns + ------- + pseudovalues : + Ratio Jackknife Pseudovalues. + + Raises + ------ + ValueError : + If numer.shape != denom.shape. + + ''' + n_blocks, p = denom.shape + pseudovalues = np.zeros((n_blocks, p)) + for j in xrange(0, n_blocks): + pseudovalues[j, ...] = n_blocks * est - \ + (n_blocks - 1) * numer[j, ...] / denom[j, ...] + + return pseudovalues diff --git a/ldscore/ldscore.py b/ldscore/ldscore.py index 9c94c81a..5e8a5065 100644 --- a/ldscore/ldscore.py +++ b/ldscore/ldscore.py @@ -1,842 +1,414 @@ from __future__ import division import numpy as np import bitarray as ba -#import progressbar as pb -import pandas as pd def getBlockLefts(coords, max_dist): - ''' - Converts coordinates + max block length to the a list of coordinates of the leftmost - SNPs to be included in blocks. - - Parameters - ---------- - coords : array - Array of coordinates. Must be sorted. - max_dist : float - Maximum distance between SNPs included in the same window. - - Returns - ------- - block_left : 1D np.ndarray with same length as block_left - block_left[j] := min{k | dist(j, k) < max_dist}. - - ''' - M = len(coords) - j = 0 - block_left = np.zeros(M) - for i in xrange(M): - while j < M and abs(coords[j] - coords[i]) > max_dist: - j += 1 - - block_left[i] = j - - return block_left - - + ''' + Converts coordinates + max block length to the a list of coordinates of the leftmost + SNPs to be included in blocks. + + Parameters + ---------- + coords : array + Array of coordinates. Must be sorted. + max_dist : float + Maximum distance between SNPs included in the same window. + + Returns + ------- + block_left : 1D np.ndarray with same length as block_left + block_left[j] := min{k | dist(j, k) < max_dist}. + + ''' + M = len(coords) + j = 0 + block_left = np.zeros(M) + for i in xrange(M): + while j < M and abs(coords[j] - coords[i]) > max_dist: + j += 1 + + block_left[i] = j + + return block_left + + def block_left_to_right(block_left): - ''' - Converts block lefts to block rights. - - Parameters - ---------- - block_left : array - Array of block lefts. - - Returns - ------- - block_right : 1D np.ndarray with same length as block_left - block_right[j] := max {k | block_left[k] <= j} - - ''' - M = len(block_left) - j = 0 - block_right = np.zeros(M) - for i in xrange(M): - while j < M and block_left[j] <= i: - j += 1 - - block_right[i] = j - - return block_right - - + ''' + Converts block lefts to block rights. + + Parameters + ---------- + block_left : array + Array of block lefts. + + Returns + ------- + block_right : 1D np.ndarray with same length as block_left + block_right[j] := max {k | block_left[k] <= j} + + ''' + M = len(block_left) + j = 0 + block_right = np.zeros(M) + for i in xrange(M): + while j < M and block_left[j] <= i: + j += 1 + + block_right[i] = j + + return block_right + + class __GenotypeArrayInMemory__(object): - ''' - Parent class for various classes containing interfaces for files with genotype - matrices, e.g., plink .bed files, etc - ''' - def __init__(self, fname, n, snp_list, keep_snps=None, keep_indivs=None, mafMin=None): - self.m = len(snp_list.IDList) - self.n = n - self.keep_snps = keep_snps - self.keep_indivs = keep_indivs - self.df = np.array(snp_list.df[['CHR','SNP','BP','CM']]) - self.colnames = ['CHR','SNP','BP','CM'] - self.mafMin = mafMin if mafMin is not None else 0 - self._currentSNP = 0 - (self.nru, self.geno) = self.__read__(fname, self.m, n) - # filter individuals - if keep_indivs is not None: - keep_indivs = np.array(keep_indivs, dtype='int') - if np.any(keep_indivs > self.n): - raise ValueError('keep_indivs indices out of bounds') - - (self.geno, self.m, self.n) = self.__filter_indivs__(self.geno, keep_indivs, self.m, - self.n) - - if self.n > 0: - print 'After filtering, {n} individuals remain'.format(n=self.n) - else: - raise ValueError('After filtering, no individuals remain') - - # filter SNPs - if keep_snps is not None: - keep_snps = np.array(keep_snps, dtype='int') - if np.any(keep_snps > self.m): # if keep_snps is None, this returns False - raise ValueError('keep_snps indices out of bounds') - - (self.geno, self.m, self.n, self.kept_snps, self.freq) = self.__filter_snps_maf__( - self.geno, self.m, self.n, self.mafMin, keep_snps) - - if self.m > 0: - print 'After filtering, {m} SNPs remain'.format(m=self.m) - else: - raise ValueError('After filtering, no SNPs remain') - - self.df = self.df[self.kept_snps,:] - self.maf = np.minimum(self.freq, np.ones(self.m)-self.freq) - self.sqrtpq = np.sqrt(self.freq*(np.ones(self.m)-self.freq)) - self.df = np.c_[self.df, self.maf] - self.colnames.append('MAF') - - def __read__(self,fname, m, n): - raise NotImplementedError - - def __filter_indivs__(geno, keep_indivs, m, n): - raise NotImplementedError - - def __filter_maf_(geno, m, n, maf): - raise NotImplementedError - ''' - def __ldscore_pbar__(self,m): - widgets = ['Computed LD for ', pb.Counter(), ' / {m} SNPs ('.format(m=m), - pb.Timer(), ')'] - pbar = pb.ProgressBar(widgets=widgets,maxval=m).start() - return pbar - - def __filter_indivs_pbar__(self, n): - widgets = ['Filtered ', pb.Counter(), ' / {n} individuals ('.format(n=n), - pb.Timer(), ')'] - pbar = pb.ProgressBar(widgets=widgets,maxval=n).start() - return pbar - - def __filter_snps_pbar__(self, m): - widgets = ['Computed MAF for ', pb.Counter(), ' / {m} SNPs ('.format(m=m), - pb.Timer(), ')'] - pbar = pb.ProgressBar(widgets=widgets,maxval=m).start() - return pbar - ''' - - ### L1 w.r.t. minor allele - def l1VarBlocks(self, block_left, c, annot=None): - ''' - Estimates - - L1hat(j) := sum_k r_jk - - for j = 1,..., M, where the sign of r_jk reflects the correlation between the - (in-sample) minor alleles at j and k. - - ''' - snp_getter = lambda n: self.nextSNPs(n, minorRef=True) - func = lambda x: x - l1hat = self.__corSumVarBlocks__(block_left,c,func,snp_getter,annot) - return l1hat - - def l1BlockJackknife(self, block_left, c, annot=None, jN=10): - func = lambda x: x - snp_getter = lambda n: self.nextSNPs(n, minorRef=True) - return self.__corSumBlockJackknife__(block_left, c, func, snp_getter, annot, jN) - - - ### L1 ^ 2 (unbiased) w.r.t. minor allele - def l1sqVarBlocks(self, block_left, c, annot=None): - ''' - For each SNP j = 1, ... , M computes an approximately unbiased estimate of L1(j)^2 - - L1hat(j)^2 := ((N-1) / N) ( sum_k rhat_jk ) ^ 2 - ( sum_k L1hat(k) ) / N, - - where the sums are taken over all SNPs k within 1cM of j. This would be an unbiased - estimator if genotypes were multivariate normal. - - ''' - block_right = block_left_to_right(block_left) - l1hat = self.l1VarBlocks(block_left, c, annot) - l1sqhat = self.__l1_to_l1sqhat_(l1hat, block_left, block_right) - return l1sqhat - - def __l1_to_l1sqhat_(self, l1hat, block_left, block_right): - l1sqhat = np.square(l1hat)*(self.n - 1) / self.n - for k in xrange(self.m): - l1sqhat[block_left[k]:block_right[k],...] -= l1hat[k] / self.n - - return l1sqhat - - def l1sqBlockJackknife(self, block_left, c, annot=None, jN=10): - func = lambda x: x - func2 = lambda x,y : np.square(np.dot(x,y)) - snp_getter = lambda n: self.nextSNPs(n, minorRef=True) - return self.__corSumBlockJackknife__(block_left, c, func, snp_getter, annot, jN, func2) - - - ### L2 - def ldScoreVarBlocks(self, block_left, c, annot=None): - ''' - Computes an unbiased estimate of L2(j) for j=1,..,M - - ''' - func = lambda x: self.__l2_unbiased__(x,self.n) - snp_getter = self.nextSNPs - return self.__corSumVarBlocks__(block_left, c, func, snp_getter, annot) - - def ldScoreBlockJackknife(self, block_left, c, annot=None, jN=10): - func = lambda x: np.square(x) - snp_getter = self.nextSNPs - return self.__corSumBlockJackknife__(block_left, c, func, snp_getter, annot, jN) - - def __l2_unbiased__(self, x, n): - denom = n-2 if n>2 else n # allow n<2 for testing purposes - sq = np.square(x) - return sq - (1-sq) / denom - - ### L4 - def l4VarBlocks(self, block_left, c, annot=None, jN=10): - ''' - Computes an estimate of L4 that is unbiased for MVN genotypes. - ''' - func = lambda x: self.__l4_unbiased__(x,self.n) - snp_getter = self.nextSNPs - return self.__corSumVarBlocks__(block_left, c, func, snp_getter, annot) - - def __l4_unbiased__(self, x, n): - raise NotImplementedError - - def l4BlockJackknife(self, block_left, c, annot=None, jN=10): - func = lambda x: np.square(np.square((x))) - snp_getter = self.nextSNPs - return self.__corSumBlockJackknife__(block_left, c, func, snp_getter, annot, jN) - - - # general methods for calculating sums of Pearson correlation coefficients - def __corSumVarBlocks__(self, block_left, c, func, snp_getter, annot=None): - ''' - Parameters - ---------- - block_left : np.ndarray with shape (M, ) - block_left[i] = index of leftmost SNP included in LD Score of SNP i. - if c > 1, then only entries that are multiples of c are examined, and it is - assumed that block_left[a*c+i] = block_left[a*c], except at - the beginning of the chromosome where the 0th SNP is included in the window. - - c : int - Chunk size. - func : function - Function to be applied to the genotype correlation matrix. Before dotting with - annot. Examples: for biased L2, np.square. For biased L4, - lambda x: np.square(np.square(x)). For L1, lambda x: x. - snp_getter : function(int) - The method to be used to get the next SNPs (normalized genotypes? Normalized - genotypes with the minor allele as reference allele? etc) - annot: numpy array with shape (m,n_a) - SNP annotations. - - Returns - ------- - cor_sum : np.ndarray with shape (M, num_annots) - Estimates. - - ''' - m, n = self.m, self.n - block_sizes = np.array(np.arange(m) - block_left) - block_sizes = np.ceil(block_sizes / c)*c - if not np.any(annot): - annot = np.ones((m,1)) - else: - annot_m = annot.shape[0] - if annot_m != self.m: - raise ValueError('Incorrect number of SNPs in annot') - - n_a = annot.shape[1] # number of annotations - cor_sum = np.zeros((m,n_a)) - # b = index of first SNP for which SNP 0 is not included in LD Score - b = np.nonzero(block_left > 0) - if np.any(b): - b = b[0][0] - else: - b = m - b = int(np.ceil(b/c)*c) # round up to a multiple of c - if b > m: - c = 1; b = m - l_A = 0; nsq = n**2 # l_A := index of leftmost SNP in matrix A - - A = snp_getter(b) - rfuncAB = np.zeros((b,c)) - rfuncBB = np.zeros((c,c)) - # chunk inside of block - #pbar = self.__ldscore_pbar__(m) - - for l_B in xrange(0,b,c): # l_B := index of leftmost SNP in matrix B - #pbar.update(l_B) - B = A[:,l_B:l_B+c] - np.dot(A.T, B / n, out=rfuncAB) - rfuncAB = func(rfuncAB) - cor_sum[l_A:l_A+b,:] += np.dot(rfuncAB,annot[l_B:l_B+c,:]) - - # chunk to right of block - b0 = b - md = int(c*np.floor(m/c)) - end = md + 1 if md !=m else md - for l_B in xrange(b0,end,c): - #pbar.update(l_B) - - # check if the annot matrix is all zeros for this block + chunk - # this happens w/ sparse categories (i.e., pathways) - - # update the block - old_b = b - b = block_sizes[l_B] - if l_B > b0 and b > 0: - # block_size can't increase more than c - # block_size can't be less than c unless it is zero - # both of these things make sense - A = np.hstack((A[:,old_b-b+c:old_b],B)) - l_A += old_b-b+c - elif l_B == b0 and b > 0: - A = A[:,b0-b:b0] - l_A = b0-b - elif b == 0: # no SNPs to left in window, e.g., after a sequence gap - A = np.array(()).reshape((n,0)) - l_A = l_B - if l_B == md: - c = m - md - rfuncAB = np.zeros((b,c)) - rfuncBB = np.zeros((c,c)) - if b != old_b: - rfuncAB = np.zeros((b,c)) - - B = snp_getter(c) - - p1 = np.all(annot[l_A:l_A+b,:] == 0) - p2 = np.all(annot[l_B:l_B+c,:] == 0) - if p1 and p2: - continue - - np.dot(A.T, B / n, out=rfuncAB) - rfuncAB = func(rfuncAB) - cor_sum[l_A:l_A+b,:] += np.dot(rfuncAB, annot[l_B:l_B+c,:]) - cor_sum[l_B:l_B+c,:] += np.dot(annot[l_A:l_A+b,:].T,rfuncAB).T - np.dot(B.T, B / n, out=rfuncBB) - rfuncBB = func(rfuncBB) - cor_sum[l_B:l_B+c,:] += np.dot(rfuncBB, annot[l_B:l_B+c,:]) - - #pbar.finish() - return cor_sum - -# def __corSumBlockJackknife__(self, block_left, c, func1, snp_getter, annot=None, jN=10, -# func2=np.dot): -# ''' -# Estimates LD Score and standard error using a block jackknife. -# -# Parameters -# ---------- -# -# Returns -# ------- -# -# Notes -# ----- -# -# ''' -# -# M, N = self.m, self.n; -# if jN > N: -# raise ValueError('jN must be <= N') -# jSize = int(np.ceil(N/jN)) -# last_jSize = N - (jN-1) *jSize -# def jsize(i): -# if i < jN: return jSize -# elif i == jN: return last_jSize -# else: raise ValueError -# -# block_sizes = np.array(np.arange(M) - block_left) -# block_sizes = np.ceil(block_sizes / c)*c -# if not np.any(annot): -# N_A = 1 # number of annotations -# annot = np.ones((M,N_A)) -# else: -# N_A = annot.shape[1] -# annot_m = annot.shape[0] -# if annot_m != self.m: -# raise ValueError('Incorrect number of SNPs in annot') -# -# # b = index of first SNP for which SNP 0 is not included in LD Score -# b = np.nonzero(block_left > 0) -# if np.any(b): -# b = b[0][0] -# else: -# b = M -# b = int(np.ceil(b/c)*c) # round up to a multiple of c -# if b > M: -# c = 1; b = M -# l_A = 0; nsq = N**2 # l_A := index of leftmost SNP in matrix A -# A = snp_getter(b) # read first b0 SNPs into the block -# rAB = np.zeros((b,c,jN)) -# rBB = np.zeros((c,c,jN)) -# MD = int(c*np.floor(M/c)) -# # num SNPs x num annotations x num jknife blocks + 1 -# # last index in 3rd axis is LD Score with nothing deleted -# cor_sum = np.zeros((M,N_A,jN+1)) -# # chunk inside of block -# pbar = self.__ldscore_pbar__(M) -# for l_B in xrange(0,b,c): # l_B := index of leftmost SNP in matrix B -# pbar.update(l_B) -# B = A[:,l_B:l_B+c] -# for ji in xrange(0, jN): -# i = ji*jSize -# jA = A[i:i+jsize(ji),:] -# jB = B[i:i+jsize(ji),:] -# rAB[:,:,ji] = np.dot(jA.T, jB) / jsize(ji) -# for j in xrange(0,jN+1): -# ii = np.arange(jN) != j -# jRfuncAB = func1(np.mean(rAB[:,:,ii], axis=2)) -# cor_sum[l_A:l_A+b,:,j] += func2(jRfuncAB, annot[l_B:l_B+c,:]) -# -# # chunk to right of block -# b0 = b -# MD = int(c*np.floor(M/c)) -# end = MD + 1 if MD !=M else MD -# for l_B in xrange(b0, end, c): -# pbar.update(l_B) -# old_b = b -# b = block_sizes[l_B] -# -# if l_B > b0 and b > 0: -# A = np.hstack((A[:,old_b-b+c:old_b],B)) # block_size can't increase more than c -# l_A += old_b-b+c -# elif l_B == b0 and b > 0: -# A = A[:,b0-b:b0] -# l_A = b0-b -# elif b == 0: # no SNPs to left in window, e.g., after a sequence gap -# A = np.array(()).reshape((N,0)) -# l_A = l_B -# -# if l_B == MD: -# c = M - MD -# #rAB = np.zeros((b,c,jN)) -# #rBB = np.zeros((c,c,jN)) -# #if b != old_b: -# #rAB = np.zeros((b,c,jN)) -# rAB = []; rBB = [] -# -# B = snp_getter(c) -# -# # compute block values -# for ji in xrange(0, jN): -# i = ji*jSize -# jA = A[i:i+jsize(ji),:] -# jB = B[i:i+jsize(ji),:] -# rAB.append(np.dot(jA.T, jB) / jsize(ji)) -# rBB.append(np.dot(jB.T, jB) / jsize(ji)) -# #rAB[:,:,ji] = np.dot(jA.T, jB) / jsize(ji) -# #rBB[:,:,ji] = np.dot(jB.T,jB) / jsize(ji) -# -# rAB.append(np.zeros(rAB[1].shape)) -# rBB.append(np.zeros(rBB[1].shape)) -# sum_rAB = np.sum(rAB, axis=0) -# sum_rBB = np.sum(rBB, axis=0) -# -# # compute delete-k values -# for j in xrange(0,jN+1): -# #ii = np.arange(jN) != j -# #jRfuncAB = func1(np.mean(rAB[:,:,ii], axis=2)) -# #jRfuncBB = func1(np.mean(rBB[:,:,ii], axis=2)) -# denom = jN if j != jN+1 else jN+1 -# jRfuncAB = func1( (sum_rAB - rAB[j]) / denom ) -# jRfuncBB = func1( (sum_rBB - rBB[j]) / denom ) -# cor_sum[l_A:l_A+b,:,j] += func2(jRfuncAB, annot[l_B:l_B+c,:]) -# cor_sum[l_B:l_B+c,:,j] += func2(annot[l_A:l_A+b,:].T,jRfuncAB).T -# cor_sum[l_B:l_B+c,:,j] += func2(jRfuncBB, annot[l_B:l_B+c,:]) -# -# # compute jackknife SE and jackknife estimate -# cor_sum_se = np.sqrt(jN-1)*np.std(cor_sum[:,:,0:jN],axis=2) -# cor_sum_jn = jN*cor_sum[:,:,jN] - (jN-1)*np.mean(cor_sum[:,:,0:jN], axis=2) -# pbar.finish() -# print -# return (cor_sum_jn, cor_sum_se) + ''' + Parent class for various classes containing interfaces for files with genotype + matrices, e.g., plink .bed files, etc + ''' + def __init__(self, fname, n, snp_list, keep_snps=None, keep_indivs=None, mafMin=None): + self.m = len(snp_list.IDList) + self.n = n + self.keep_snps = keep_snps + self.keep_indivs = keep_indivs + self.df = np.array(snp_list.df[['CHR', 'SNP', 'BP', 'CM']]) + self.colnames = ['CHR', 'SNP', 'BP', 'CM'] + self.mafMin = mafMin if mafMin is not None else 0 + self._currentSNP = 0 + (self.nru, self.geno) = self.__read__(fname, self.m, n) + # filter individuals + if keep_indivs is not None: + keep_indivs = np.array(keep_indivs, dtype='int') + if np.any(keep_indivs > self.n): + raise ValueError('keep_indivs indices out of bounds') + + (self.geno, self.m, self.n) = self.__filter_indivs__(self.geno, keep_indivs, self.m, + self.n) + + if self.n > 0: + print 'After filtering, {n} individuals remain'.format(n=self.n) + else: + raise ValueError('After filtering, no individuals remain') + + # filter SNPs + if keep_snps is not None: + keep_snps = np.array(keep_snps, dtype='int') + if np.any(keep_snps > self.m): # if keep_snps is None, this returns False + raise ValueError('keep_snps indices out of bounds') + + (self.geno, self.m, self.n, self.kept_snps, self.freq) = self.__filter_snps_maf__( + self.geno, self.m, self.n, self.mafMin, keep_snps) + + if self.m > 0: + print 'After filtering, {m} SNPs remain'.format(m=self.m) + else: + raise ValueError('After filtering, no SNPs remain') + + self.df = self.df[self.kept_snps, :] + self.maf = np.minimum(self.freq, np.ones(self.m)-self.freq) + self.sqrtpq = np.sqrt(self.freq*(np.ones(self.m)-self.freq)) + self.df = np.c_[self.df, self.maf] + self.colnames.append('MAF') + + def __read__(self, fname, m, n): + raise NotImplementedError + + def __filter_indivs__(geno, keep_indivs, m, n): + raise NotImplementedError + + def __filter_maf_(geno, m, n, maf): + raise NotImplementedError + + def ldScoreVarBlocks(self, block_left, c, annot=None): + '''Computes an unbiased estimate of L2(j) for j=1,..,M.''' + func = lambda x: self.__l2_unbiased__(x, self.n) + snp_getter = self.nextSNPs + return self.__corSumVarBlocks__(block_left, c, func, snp_getter, annot) + + def ldScoreBlockJackknife(self, block_left, c, annot=None, jN=10): + func = lambda x: np.square(x) + snp_getter = self.nextSNPs + return self.__corSumBlockJackknife__(block_left, c, func, snp_getter, annot, jN) + + def __l2_unbiased__(self, x, n): + denom = n-2 if n > 2 else n # allow n<2 for testing purposes + sq = np.square(x) + return sq - (1-sq) / denom + + # general methods for calculating sums of Pearson correlation coefficients + def __corSumVarBlocks__(self, block_left, c, func, snp_getter, annot=None): + ''' + Parameters + ---------- + block_left : np.ndarray with shape (M, ) + block_left[i] = index of leftmost SNP included in LD Score of SNP i. + if c > 1, then only entries that are multiples of c are examined, and it is + assumed that block_left[a*c+i] = block_left[a*c], except at + the beginning of the chromosome where the 0th SNP is included in the window. + + c : int + Chunk size. + func : function + Function to be applied to the genotype correlation matrix. Before dotting with + annot. Examples: for biased L2, np.square. For biased L4, + lambda x: np.square(np.square(x)). For L1, lambda x: x. + snp_getter : function(int) + The method to be used to get the next SNPs (normalized genotypes? Normalized + genotypes with the minor allele as reference allele? etc) + annot: numpy array with shape (m,n_a) + SNP annotations. + + Returns + ------- + cor_sum : np.ndarray with shape (M, num_annots) + Estimates. + + ''' + m, n = self.m, self.n + block_sizes = np.array(np.arange(m) - block_left) + block_sizes = np.ceil(block_sizes / c)*c + if not np.any(annot): + annot = np.ones((m, 1)) + else: + annot_m = annot.shape[0] + if annot_m != self.m: + raise ValueError('Incorrect number of SNPs in annot') + + n_a = annot.shape[1] # number of annotations + cor_sum = np.zeros((m, n_a)) + # b = index of first SNP for which SNP 0 is not included in LD Score + b = np.nonzero(block_left > 0) + if np.any(b): + b = b[0][0] + else: + b = m + b = int(np.ceil(b/c)*c) # round up to a multiple of c + if b > m: + c = 1 + b = m + l_A = 0 # l_A := index of leftmost SNP in matrix A + A = snp_getter(b) + rfuncAB = np.zeros((b, c)) + rfuncBB = np.zeros((c, c)) + # chunk inside of block + for l_B in xrange(0, b, c): # l_B := index of leftmost SNP in matrix B + B = A[:, l_B:l_B+c] + np.dot(A.T, B / n, out=rfuncAB) + rfuncAB = func(rfuncAB) + cor_sum[l_A:l_A+b, :] += np.dot(rfuncAB, annot[l_B:l_B+c, :]) + # chunk to right of block + b0 = b + md = int(c*np.floor(m/c)) + end = md + 1 if md != m else md + for l_B in xrange(b0, end, c): + # check if the annot matrix is all zeros for this block + chunk + # this happens w/ sparse categories (i.e., pathways) + # update the block + old_b = b + b = block_sizes[l_B] + if l_B > b0 and b > 0: + # block_size can't increase more than c + # block_size can't be less than c unless it is zero + # both of these things make sense + A = np.hstack((A[:, old_b-b+c:old_b], B)) + l_A += old_b-b+c + elif l_B == b0 and b > 0: + A = A[:, b0-b:b0] + l_A = b0-b + elif b == 0: # no SNPs to left in window, e.g., after a sequence gap + A = np.array(()).reshape((n, 0)) + l_A = l_B + if l_B == md: + c = m - md + rfuncAB = np.zeros((b, c)) + rfuncBB = np.zeros((c, c)) + if b != old_b: + rfuncAB = np.zeros((b, c)) + + B = snp_getter(c) + p1 = np.all(annot[l_A:l_A+b, :] == 0) + p2 = np.all(annot[l_B:l_B+c, :] == 0) + if p1 and p2: + continue + + np.dot(A.T, B / n, out=rfuncAB) + rfuncAB = func(rfuncAB) + cor_sum[l_A:l_A+b, :] += np.dot(rfuncAB, annot[l_B:l_B+c, :]) + cor_sum[l_B:l_B+c, :] += np.dot(annot[l_A:l_A+b, :].T, rfuncAB).T + np.dot(B.T, B / n, out=rfuncBB) + rfuncBB = func(rfuncBB) + cor_sum[l_B:l_B+c, :] += np.dot(rfuncBB, annot[l_B:l_B+c, :]) + + return cor_sum class PlinkBEDFile(__GenotypeArrayInMemory__): - ''' - Interface for Plink .bed format - ''' - def __init__(self, fname, n, snp_list, keep_snps=None, keep_indivs=None, mafMin=None): - self._bedcode = { - 2: ba.bitarray('11'), - 9: ba.bitarray('10'), - 1: ba.bitarray('01'), - 0:ba.bitarray('00') - } - - __GenotypeArrayInMemory__.__init__(self, fname, n, snp_list, keep_snps=keep_snps, - keep_indivs=keep_indivs, mafMin=mafMin) - - def __read__(self, fname, m, n): - if not fname.endswith('.bed'): - raise ValueError('.bed filename must end in .bed') - - fh = open(fname,'rb') - magicNumber = ba.bitarray(endian="little") - magicNumber.fromfile(fh, 2) - bedMode = ba.bitarray(endian="little") - bedMode.fromfile(fh, 1) - ### TODO this is ugly - e = (4 - n % 4) if n % 4 != 0 else 0 - nru = n + e - self.nru = nru - # check magic number - if magicNumber != ba.bitarray('0011011011011000'): - raise IOError("Magic number from Plink .bed file not recognized") - - if bedMode != ba.bitarray('10000000'): - raise IOError("Plink .bed file must be in default SNP-major mode") - - # check file length - self.geno = ba.bitarray(endian="little") - self.geno.fromfile(fh) - self.__test_length__(self.geno,self.m,self.nru) - return (self.nru, self.geno) - - def __test_length__(self, geno, m, nru): - exp_len = 2*m*nru - real_len = len(geno) - if real_len != exp_len: - s = "Plink .bed file has {n1} bits, expected {n2}" - raise IOError(s.format(n1=real_len, n2=exp_len)) - - def __filter_indivs__(self, geno, keep_indivs, m, n): - n_new = len(keep_indivs) - e = (4 - n_new % 4) if n_new % 4 != 0 else 0 - nru_new = n_new + e - nru = self.nru - z = ba.bitarray(m*2*nru_new, endian="little") - #pbar = self.__filter_indivs_pbar__(len(keep_indivs)) - for e, i in enumerate(keep_indivs): - #pbar.update(e) - z[2*e::2*nru_new] = geno[2*i::2*nru] - z[2*e+1::2*nru_new] = geno[2*i+1::2*nru] - - #pbar.finish() - self.nru = nru_new - return (z, m, n_new) - - def __filter_snps_maf__(self, geno, m, n, mafMin, keep_snps): - ''' - Credit to Chris Chang and the Plink2 developers for this algorithm - Modified from plink_filter.c - https://github.com/chrchang/plink-ng/blob/master/plink_filter.c - - Genotypes are read forwards (since we are cheating and using endian="little") - - A := (genotype) & 1010... - B := (genotype) & 0101... - C := (A >> 1) & B - - Then - - a := A.count() = missing ct + hom major ct - b := B.count() = het ct + hom major ct - c := C.count() = hom major ct - - Which implies that - - missing ct = a - c - # of indivs with nonmissing genotype = n - a + c - major allele ct = b + c - major allele frequency = (b+c)/(2*(n-a+c)) - het ct + missing ct = a + b - 2*c - - Why does bitarray not have >> ???? - - ''' - - nru = self.nru - m_poly = 0 - y = ba.bitarray() - if keep_snps is None: keep_snps = xrange(m) - kept_snps = []; freq = [] - #pbar = self.__filter_snps_pbar__(len(keep_snps)) - for e,j in enumerate(keep_snps): - #pbar.update(e) - z = geno[2*nru*j:2*nru*(j+1)] - A = z[0::2]; a = A.count() - B = z[1::2]; b = B.count() - c = (A&B).count() - major_ct = b + c # number of copies of the major allele - n_nomiss = n - a + c # number of individuals with nonmissing genotypes - f = major_ct / (2*n_nomiss) if n_nomiss > 0 else 0 - het_miss_ct = a+b-2*c # remove SNPs that are only either het or missing - if np.minimum(f,1-f) > mafMin and het_miss_ct < n: - freq.append(f) - y += z - m_poly += 1 - kept_snps.append(j) - - #pbar.finish() - return (y, m_poly, n, kept_snps, freq) - - def nextSNPs(self, b, minorRef=None): - ''' - Unpacks the binary array of genotypes and returns an n x b matrix of floats of - normalized genotypes for the next b SNPs, where n := number of samples. - - Parameters - ---------- - b : int - Number of SNPs to return. - minorRef: bool, default None - Should we flip reference alleles so that the minor allele is the reference? - (This is useful for computing l1 w.r.t. minor allele). - - Returns - ------- - X : np.array with dtype float64 with shape (n, b), where n := number of samples - Matrix of genotypes normalized to mean zero and variance one. If minorRef is - not None, then the minor allele will be the positive allele (i.e., two copies - of the minor allele --> a positive number). - - ''' - - try: - b = int(b) - if b <= 0: - raise ValueError("b must be > 0") - except TypeError: - raise TypeError("b must be an integer") - - if self._currentSNP + b > self.m: - s = '{b} SNPs requested, {k} SNPs remain' - raise ValueError(s.format(b=b, k=(self.m-self._currentSNP))) - - c = self._currentSNP - n = self.n - nru = self.nru - slice = self.geno[2*c*nru:2*(c+b)*nru] - X = np.array(slice.decode(self._bedcode), dtype="float64").reshape((b,nru)).T - X = X[0:n,:] - Y = np.zeros(X.shape) - for j in xrange(0,b): - newsnp = X[:,j] - ii = newsnp != 9 - avg = np.mean(newsnp[ii]) - newsnp[np.logical_not(ii)] = avg - denom = np.std(newsnp) - if denom == 0: - denom = 1 - - if minorRef is not None and self.freq[self._currentSNP + j] > 0.5: - denom = denom*-1 - - Y[:,j] = (newsnp - avg)/denom - - self._currentSNP += b - return Y - - -class VcfBINFile(__GenotypeArrayInMemory__): - ''' - Interface for genotypes stored as a python bitarray. - - Parameters - ---------- - Inherits __init__ from parent class. - - Methods - ------- - __read__(fname, m, n) - Reads genotypes from file. - __filter_indivs__(geno, keep_indivs, n) - Filters individuals. - __filter_snps_maf__(geno, m, mafMin, keep_snps) - Filters SNPs and removes low-MAF SNPs. - nextSNPs(b, minorRef) - Returns an (n, b) array of genotypes at the next b SNPs. - - Attributes - ---------- - Same as parent class. - - ''' - def __read__(self, fname, m, n): - if not fname.endswith('.bin'): - raise ValueError('.bin filename must end in .bin') - - n, m = self.n, self.m - geno = ba.bitarray(endian="big") - magicNumber = ba.bitarray(endian="big") - fh = open(fname,'rb') - magicNumber.fromfile(fh, 4) - ldsc = ba.bitarray() - ldsc.fromstring('ldsc') - if magicNumber != ldsc: - raise IOError("Magic number from .bin file not recognized") - # check file length - geno.fromfile(fh) - exp_len = m*n + ((8-m*n) % 8) - real_len = len(geno) - if real_len != exp_len: - s = "Binary .bin file has {n1} bits, expected {n2}" - raise IOError(s.format(n1=real_len, n2=exp_len)) - - return (n, geno) - - def __filter_indivs__(self, geno, keep_indivs, m, n): - ''' - Filters the genotype matrix to retain only those individuals in keep_indivs. - - Parameters - ---------- - geno : ba.bitarray - Genotype array before filtering. - keep_indivs : np.array of ints - Array of indices of individuals to retain. - n : int - Number of individuals before filtering. - - Returns - ------- - z : ba.bitarray - Filtered genotype matrix. - n_new : int - Number of individuals retained after filtering. - - ''' - - n_new = len(keep_indivs) - z = ba.bitarray(m*n_new, endian="big") - #pbar = self.__filter_indivs_pbar__(len(keep_indivs)) - for e, i in enumerate(keep_indivs): - #pbar.update(e) - z[e::n_new] = geno[i:m*n:n] - - #pbar.finish() - return (z, m, n_new) - - def __filter_snps_maf__(self, geno, m, n, mafMin, keep_snps): - ''' - Removes low-MAF SNPs + SNPs not in keep_snps from the gentotype matrix. - - A SNP is retained if the SNP index is included in keep_snps AND the SNP has MAF - above mafMin. - - Parameters - ---------- - geno : ba.bitarray - Genotype array. - m : int - Number of SNPs. - mafMin : float in [0, 0.5) - Minimum minor allele frequency required for inclusion. - keep_snps : np.array of ints - Indices of SNPs to keep. - - Returns - ------- - y[0:m_poly*n] : ba.bitarary - Bitarray with genotypes at SNPs that passed filters. - m_poly : int - Number of SNPs that passed filters. - kept_snps : np.array of ints - Indices of SNPs that passed filters. - freq : np.array of floats - Genotype frequencies (NB: not minor allele frequency, frequency of the - allele coded 1). - - ''' - - y = ba.bitarray(m*n, endian="big") - m_poly = 0; freq = [] - if keep_snps is None: keep_snps = xrange(m) - kept_snps = [] - #pbar = self.__filter_snps_pbar__(len(keep_snps)) - for e,j in enumerate(keep_snps): - #pbar.update(e) - z = geno[j*n:(j+1)*n] - c = z.count() - if c < n and np.minimum(c/n,1-c/n) > mafMin: - y[m_poly*n:(m_poly+1)*n] = z - m_poly += 1 - freq.append(c/n) - kept_snps.append(j) - - #pbar.finish() - return (y[0:m_poly*n], m_poly, n, kept_snps, freq) - - def nextSNPs(self, b, minorRef=None): - ''' - Unpacks the binary array of genotypes and returns an n x b matrix of floats of - normalized genotypes for the next b SNPs, where n := number of samples. - - Parameters - ---------- - b : int - Number of SNPs to return. - minorRef: bool, default None - Should we flip reference alleles so that the minor allele is the reference? - (This is useful for computing l1 w.r.t. minor allele). - - Returns - ------- - X : np.array with dtype float64 with shape (n, b), where n := number of samples - Matrix of genotypes normalized to mean zero and variance one. If minorRef is - not None, then the minor allele will be the positive allele (i.e., two copies - of the minor allele --> a positive number). - - ''' - - b = int(b) - if b <= 0: - raise ValueError("b must be > 0") - - if self._currentSNP + b > self.m: - s = '{b} SNPs requested, {k} SNPs remain' - raise ValueError(s.format(b=b, k=(self.m-self._currentSNP))) - - b = min(self.m - self._currentSNP, b) - n = self.n - c = self._currentSNP - X = np.array( self.geno[c*n:(c+b)*n].tolist(), dtype="float64").reshape((b,n)).T - if minorRef is not None: - flip_ref = np.ones(b) - flip_ref[np.nonzero(np.array(self.freq[c:(c+b)]) > 0.5)] = -1 - denom = self.sqrtpq[c:c+b] * flip_ref - X = (X - self.freq[c:c+b] ) / denom - else: - X = (X - self.freq[c:c+b] ) / self.sqrtpq[c:c+b] - - self._currentSNP += b - return X - - -class AnnotFile(): - - def __init__(self, fname): - self._read(fname) - - def _read(self, fname): - dtype_dict = {0:"str", 1:"float64", 2:"string"} - self.annot = pd.read_csv(fname, header=0, delim_whitespace=True, dtype=dtype_dict) - numAnnots = self.annot.shape[1] - 3 - self.annotIDs = self.annot['SNP'] - self.annotMatrix = np.array(self.annot.ix[:,4:], dtype="float64") \ No newline at end of file + ''' + Interface for Plink .bed format + ''' + def __init__(self, fname, n, snp_list, keep_snps=None, keep_indivs=None, mafMin=None): + self._bedcode = { + 2: ba.bitarray('11'), + 9: ba.bitarray('10'), + 1: ba.bitarray('01'), + 0: ba.bitarray('00') + } + + __GenotypeArrayInMemory__.__init__(self, fname, n, snp_list, keep_snps=keep_snps, + keep_indivs=keep_indivs, mafMin=mafMin) + + def __read__(self, fname, m, n): + if not fname.endswith('.bed'): + raise ValueError('.bed filename must end in .bed') + + fh = open(fname, 'rb') + magicNumber = ba.bitarray(endian="little") + magicNumber.fromfile(fh, 2) + bedMode = ba.bitarray(endian="little") + bedMode.fromfile(fh, 1) + e = (4 - n % 4) if n % 4 != 0 else 0 + nru = n + e + self.nru = nru + # check magic number + if magicNumber != ba.bitarray('0011011011011000'): + raise IOError("Magic number from Plink .bed file not recognized") + + if bedMode != ba.bitarray('10000000'): + raise IOError("Plink .bed file must be in default SNP-major mode") + + # check file length + self.geno = ba.bitarray(endian="little") + self.geno.fromfile(fh) + self.__test_length__(self.geno, self.m, self.nru) + return (self.nru, self.geno) + + def __test_length__(self, geno, m, nru): + exp_len = 2*m*nru + real_len = len(geno) + if real_len != exp_len: + s = "Plink .bed file has {n1} bits, expected {n2}" + raise IOError(s.format(n1=real_len, n2=exp_len)) + + def __filter_indivs__(self, geno, keep_indivs, m, n): + n_new = len(keep_indivs) + e = (4 - n_new % 4) if n_new % 4 != 0 else 0 + nru_new = n_new + e + nru = self.nru + z = ba.bitarray(m*2*nru_new, endian="little") + for e, i in enumerate(keep_indivs): + z[2*e::2*nru_new] = geno[2*i::2*nru] + z[2*e+1::2*nru_new] = geno[2*i+1::2*nru] + + self.nru = nru_new + return (z, m, n_new) + + def __filter_snps_maf__(self, geno, m, n, mafMin, keep_snps): + ''' + Credit to Chris Chang and the Plink2 developers for this algorithm + Modified from plink_filter.c + https://github.com/chrchang/plink-ng/blob/master/plink_filter.c + + Genotypes are read forwards (since we are cheating and using endian="little") + + A := (genotype) & 1010... + B := (genotype) & 0101... + C := (A >> 1) & B + + Then + + a := A.count() = missing ct + hom major ct + b := B.count() = het ct + hom major ct + c := C.count() = hom major ct + + Which implies that + + missing ct = a - c + # of indivs with nonmissing genotype = n - a + c + major allele ct = b + c + major allele frequency = (b+c)/(2*(n-a+c)) + het ct + missing ct = a + b - 2*c + + Why does bitarray not have >> ???? + + ''' + nru = self.nru + m_poly = 0 + y = ba.bitarray() + if keep_snps is None: + keep_snps = xrange(m) + kept_snps = [] + freq = [] + for e, j in enumerate(keep_snps): + z = geno[2*nru*j:2*nru*(j+1)] + A = z[0::2] + a = A.count() + B = z[1::2] + b = B.count() + c = (A & B).count() + major_ct = b + c # number of copies of the major allele + n_nomiss = n - a + c # number of individuals with nonmissing genotypes + f = major_ct / (2*n_nomiss) if n_nomiss > 0 else 0 + het_miss_ct = a+b-2*c # remove SNPs that are only either het or missing + if np.minimum(f, 1-f) > mafMin and het_miss_ct < n: + freq.append(f) + y += z + m_poly += 1 + kept_snps.append(j) + + return (y, m_poly, n, kept_snps, freq) + + def nextSNPs(self, b, minorRef=None): + ''' + Unpacks the binary array of genotypes and returns an n x b matrix of floats of + normalized genotypes for the next b SNPs, where n := number of samples. + + Parameters + ---------- + b : int + Number of SNPs to return. + minorRef: bool, default None + Should we flip reference alleles so that the minor allele is the reference? + (This is useful for computing l1 w.r.t. minor allele). + + Returns + ------- + X : np.array with dtype float64 with shape (n, b), where n := number of samples + Matrix of genotypes normalized to mean zero and variance one. If minorRef is + not None, then the minor allele will be the positive allele (i.e., two copies + of the minor allele --> a positive number). + + ''' + + try: + b = int(b) + if b <= 0: + raise ValueError("b must be > 0") + except TypeError: + raise TypeError("b must be an integer") + + if self._currentSNP + b > self.m: + s = '{b} SNPs requested, {k} SNPs remain' + raise ValueError(s.format(b=b, k=(self.m-self._currentSNP))) + + c = self._currentSNP + n = self.n + nru = self.nru + slice = self.geno[2*c*nru:2*(c+b)*nru] + X = np.array(slice.decode(self._bedcode), dtype="float64").reshape((b, nru)).T + X = X[0:n, :] + Y = np.zeros(X.shape) + for j in xrange(0, b): + newsnp = X[:, j] + ii = newsnp != 9 + avg = np.mean(newsnp[ii]) + newsnp[np.logical_not(ii)] = avg + denom = np.std(newsnp) + if denom == 0: + denom = 1 + + if minorRef is not None and self.freq[self._currentSNP + j] > 0.5: + denom = denom*-1 + + Y[:, j] = (newsnp - avg) / denom + + self._currentSNP += b + return Y diff --git a/ldscore/parse.py b/ldscore/parse.py index 45cd2186..07c9bdd6 100644 --- a/ldscore/parse.py +++ b/ldscore/parse.py @@ -1,687 +1,279 @@ ''' (c) 2014 Brendan Bulik-Sullivan and Hilary Finucane -This module contains functions for parsing various ldsc-defined file formats +This module contains functions for parsing various ldsc-defined file formats. ''' from __future__ import division import numpy as np import pandas as pd -from scipy.special import chdtri -import gzip, bz2 import os +def series_eq(x, y): + '''Compare series, return False if lengths not equal.''' + return len(x) == len(y) and (x == y).all() + + +def read_csv(fh, **kwargs): + return pd.read_csv(fh, delim_whitespace=True, na_values='.', **kwargs) + + +def sub_chr(s, chr): + '''Substitute chr for @, else append chr to the end of str.''' + if '@' not in s: + s += '@' + + return s.replace('@', str(chr)) + + +def which_compression(fh): + '''Given a file prefix, figure out what sort of compression to use.''' + if os.access(fh + '.bz2', 4): + suffix = '.bz2' + compression = 'bz2' + elif os.access(fh + '.gz', 4): + suffix = '.gz' + compression = 'gzip' + elif os.access(fh, 4): + suffix = '' + compression = None + else: + raise IOError('Could not open {F}[./gz/bz2]'.format(F=fh)) + + return suffix, compression + + def get_compression(fh): - '''Which sort of compression should we use with pd.read_csv?''' - if fh.endswith('gz'): - compression='gzip' - elif fh.endswith('bz2'): - compression='bz2' - elif fh.endswith('pickle'): - compression='pickle' - else: - compression=None - return compression - - -def filter_df(df, colname, pred): - ''' - Filters df down to those rows where pred applied to colname returns True - - Parameters - ---------- - df : pd.DataFrame - Data frame to filter. - colname : string - Name of a column in df. - pred : function - Function that takes one argument (the data type of colname) and returns True or False. - - Returns - ------- - df2 : pd.DataFrame - Filtered version of df. - - ''' - if colname in df.columns: - df2 = df[pred( df[colname] )] - else: - raise ValueError('Cannot find a column named {C}.'.format(C=colname)) - - return df2 - - -# input checking functions - -def check_dir(dir): - ''' - Checks that directions of effect are sensible. Nonsense values should have been caught - already by coercion to int. - - ''' - c1 = dir != 1 - c2 = dir != -1 - if np.any(np.logical_and(c1, c2)): - raise ValueError('DIR entry not equal to +/- 1.') - - -def check_rsid(rsids): - ''' - Checks that rs numbers are sensible. - - ''' - # check for rsid = . - #if np.any(rsids == '.'): - # raise ValueError('Some SNP identifiers are set to . (a dot).') - - # check for duplicate rsids - #if np.any(rsids.duplicated('SNP')): - # raise ValueError('Duplicated SNP identifiers.') - - -def check_pvalue(P): - ''' - Checks that P values are sensible. Nonsense values should have been caught already by - coercion to float. - - ''' - # check for missing - if np.any(np.isnan(P)): - raise ValueError('Missing P-values') - - # check for P outside of the range (0,1] - if np.max(P) > 1: - raise ValueError('P-values cannot be > 1.') - if np.min(P) <= 0: - raise ValueError('P values cannot be <= 0') - - -def check_chisq(chisq): - ''' - Checks that chi-square statistics are sensible. Nonsense values should have been caught - already by coercion to float. - - ''' - if np.any(np.isnan(chisq)): - raise ValueError('Missing chi-square statistics.') - - # check for chisq outside of the range [0,Inf) - if np.max(chisq) == float('inf'): - raise ValueError('Infinite chi-square statistics.') - if np.min(chisq) < 0: - raise ValueError('Negative chi-square statistics') - - -def check_maf(maf): - ''' - Checks that MAFs are sensible. Nonsense values should have been caught already by - coercion to float. - - ''' - if np.any(np.isnan(maf)): - raise ValueError('Missing values in MAF.') - - # check for MAF outside of the range (0,1) - if np.max(maf) >= 1: - raise ValueError('MAF >= 1.') - if np.min(maf) <= 0: - raise ValueError('MAF <= 0.') - - -def check_N(N): - ''' - Checks that sample sizes are sensible. Nonsense values should have been caught already - by coercion to int. - - ''' - if np.min(N) < 0: - raise ValueError('Negative N.') - - -def cut_cts(vec, breaks): - '''Cuts vectors for --cts-bin.''' - - max_cts = np.max(vec) - min_cts = np.min(vec) - cut_breaks = list(breaks) - name_breaks = list(cut_breaks) - if np.all(cut_breaks >= max_cts) or np.all(cut_breaks <= min_cts): - raise ValueError('All breaks lie outside the range of the cts variable.') - - if np.all(cut_breaks <= max_cts): - name_breaks.append(max_cts) - cut_breaks.append(max_cts+1) - - if np.all(cut_breaks >= min_cts): - name_breaks.append(min_cts) - cut_breaks.append(min_cts-1) - - name_breaks.sort() - cut_breaks.sort() - n_breaks = len(cut_breaks) - # so that col names are consistent across chromosomes with different max vals - name_breaks[0] = 'min' - name_breaks[-1] = 'max' - name_breaks = [str(x) for x in name_breaks] - labs = [name_breaks[i]+'_'+name_breaks[i+1] for i in xrange(n_breaks-1)] - cut_vec = pd.Series(pd.cut(vec, bins=cut_breaks, labels=labs)) - return cut_vec - - -# parsers + '''Which sort of compression should we use with read_csv?''' + if fh.endswith('gz'): + compression = 'gzip' + elif fh.endswith('bz2'): + compression = 'bz2' + else: + compression = None + + return compression + def read_cts(fh, match_snps): - '''Reads files for --cts-bin.''' - comp = get_compression(fh) - cts = pd.read_csv(fh, header=None, delim_whitespace=True, compression=comp) - cts.rename( columns={0: 'SNP', 1: 'ANNOT'}, inplace=True) - check_rsid(cts.SNP) - snp = cts.SNP.values - if len(snp) != len(match_snps) or np.any(snp != match_snps): - msg = 'All --cts-bin files must contain the same SNPs in the same ' - msg += 'order as the .bim file.' - raise ValueError(msg) - - return cts.ANNOT.values - - -def chisq(fh, require_alleles=False, keep_na=False, check=True): - - ''' - Parses .chisq files. See docs/file_formats_sumstats.txt - - ''' - dtype_dict = { -# 'CHR': str, - 'SNP': str, -# 'CM': float, -# 'BP': int, - 'P': float, - 'CHISQ': float, - 'N': float, - 'MAF': float, - 'INFO': float, - 'INC_ALLELE': str, - 'DEC_ALLELE': str - } - - compression = get_compression(fh) - if compression != 'pickle': - if compression == 'gzip': - openfunc = gzip.open - elif compression == 'bz2': - raise NotImplementedError('bzipped chisq files not currently supported.') - else: - openfunc = open - - colnames = openfunc(fh,'rb').readline().split() - if require_alleles: - usecols = ['SNP','P','CHISQ','N','MAF','INFO','INC_ALLELE','DEC_ALLELE'] - else: - usecols = ['SNP','P','CHISQ','N','MAF','INFO'] - - usecols = [x for x in usecols if x in colnames] - try: - x = pd.read_csv(fh, header=0, delim_whitespace=True, usecols=usecols, - dtype=dtype_dict, compression=compression) - except AttributeError as e: - raise AttributeError('Improperly formatted chisq file: '+str(e.args)) - - else: - x = pd.read_pickle(fh).reset_index(drop=True) - - if 'P' in x.columns: - ii = x.P.notnull() - check_pvalue(x.loc[ii,'P']) - x.loc[ii,'P'] = chdtri(1, x.loc[ii,'P']) - x.rename(columns={'P': 'CHISQ'}, inplace=True) - - if check: - ''' - Check that all the columns make sense. These checks are somewhat slow, and are - completely redundant for chisq files generated using sumstats_to_chisq.py. - ''' - try: - check_N(x['N']) - except KeyError as e: - raise KeyError('No column named N in .chisq: '+str(e.args)) - - x['N'] = x['N'].astype(float) - - try: - check_rsid(x.SNP) - except KeyError as e: - raise KeyError('No column named SNP in .chisq: '+str(e.args)) - - ii = x.N.notnull() - if 'MAF' in x.columns: - maf = x.loc[ii, 'MAF'] - check_maf(maf) - x.loc[ii,'MAF'] = np.fmin(maf, 1-maf) - elif 'CHISQ' in x.columns: - check_chisq(x.loc[ii,'CHISQ']) - else: - raise ValueError('.chisq file must have a column labeled either P or CHISQ.') - - if require_alleles: - ''' - Checks that only matter for rg estimation, where the alleles need to be aligned to - the same ref allele. These checks are redundant for chisq files generated using - sumstats_to_chisq.py and --merge-alleles. - - ''' - if not ('INC_ALLELE' in x.columns and 'DEC_ALLELE' in x.columns): - msg = '.chisq file must have an INC_ALLELE and DEC_ALLELE column for rg estimation.' - raise ValueError(msg) - - x.INC_ALLELE[ii] = x.INC_ALLELE[ii].apply(lambda y: y.upper()) - y = x.INC_ALLELE[ii].unique() - p = ( (y == 'A') | (y == 'C') | (y == 'T') | (y == 'G')).all() - if not p: - raise ValueError('INC_ALLELE column must contain only A/C/T/G. Do you have indels?') - - x.DEC_ALLELE[ii] = x.DEC_ALLELE[ii].apply(lambda y: y.upper()) - y = x.DEC_ALLELE[ii].unique() - p = ( (y == 'A') | (y == 'C') | (y == 'T') | (y == 'G')).all() - if not p: - raise ValueError('DEC_ALLELE column must contain only A/C/T/G. Do you have indels?') - - if np.any(x.INC_ALLELE[ii] == x.DEC_ALLELE[ii]): - raise ValueError('INC_ALLELE cannot equal DEC_ALLELE.') - - if not keep_na: - x = x[ii] - - return x - - -def ldscore_fromfile(fh, num=None): - '''Sideways concatenation of a list of LD Scores from a file.''' - f = open(fh,'r') - lines = [x.rstrip('\n') for x in f.readlines()] - return ldscore_fromlist(lines, num) - - -def annot_fromfile(fh, num=None, frqfile=None): - '''Sideways concatenation of a list of annot matrices from a file.''' - f = open(fh,'r') - lines = [x.rstrip('\n') for x in f.readlines()] - return annot(lines, num, frqfile) + '''Reads files for --cts-bin.''' + compression = get_compression(fh) + cts = read_csv(fh, compression=compression, header=None, names=['SNP', 'ANNOT']) + if not series_eq(cts.SNP, match_snps): + raise ValueError('--cts-bin and the .bim file must have identical SNP columns.') + + return cts.ANNOT.values + + +def sumstats(fh, alleles=False, dropna=True): + '''Parses .sumstats files. See docs/file_formats_sumstats.txt.''' + dtype_dict = {'SNP': str, 'Z': float, 'N': float, 'A1': str, 'A2': str} + compression = get_compression(fh) + usecols = ['SNP', 'Z', 'N'] + if alleles: + usecols += ['A1', 'A2'] + + try: + x = read_csv(fh, usecols=usecols, dtype=dtype_dict, compression=compression) + except (AttributeError, ValueError) as e: + raise ValueError('Improperly formatted sumstats file: ' + str(e.args)) + + if dropna: + x = x.dropna(how='any') + + return x def ldscore_fromlist(flist, num=None): - '''Sideways concatenation of a list of LD Score files.''' - ldscore_array = [] - for i,fh in enumerate(flist): - fh = fh.rstrip('\n') - y = ldscore(fh, num) - if i > 0: - if not np.all(y.SNP == ldscore_array[0].SNP): - msg = 'All files in --ref-ld-list or --ref-ld-file must contain the same SNPs ' - msg += 'in the same order.' - raise ValueError(msg) - - y = y.ix[:,1:len(y.columns)] # remove SNP column - - # force unique column names - new_col_dict = {c: c+'_'+str(i) for c in y.columns if c != 'SNP'} - y.rename(columns=new_col_dict, inplace=True) - ldscore_array.append(y) - - x = pd.concat(ldscore_array, axis=1) - return x + '''Sideways concatenation of a list of LD Score files.''' + ldscore_array = [] + for i, fh in enumerate(flist): + y = ldscore(fh, num) + if i > 0: + if not series_eq(y.SNP, ldscore_array[0].SNP): + raise ValueError('LD Scores for concatenation must have identical SNP columns.') + else: # keep SNP column from only the first file + y = y.drop(['SNP'], axis=1) + new_col_dict = {c: c + '_' + str(i) for c in y.columns if c != 'SNP'} + y.rename(columns=new_col_dict, inplace=True) + ldscore_array.append(y) -def which_compression(fh): - '''Given a file prefix, figure out what sort of compression to use''' - if os.access(fh+'.pickle', 4): - suffix = '.pickle' - compression = 'pickle' - elif os.access(fh+'.bz2', 4): - suffix = '.bz2' - compression = 'bz2' - elif os.access(fh+'.gz', 4): - suffix = '.gz' - compression = 'gzip' - elif os.access(fh, 4): - compression = None - suffix = '' - else: - msg = 'Could not open {F}[.pickle/gz/bz2]' - raise IOError(msg.format(F=fh)) - - return [suffix, compression] + return pd.concat(ldscore_array, axis=1) def l2_parser(fh, compression): - '''For parsing LD Score files''' - if compression == "gzip" or compression == 'bz2' or compression == None: - x = pd.read_csv(fh, header=0, delim_whitespace=True, compression=compression) - elif compression == 'pickle': - x = pd.read_pickle(fh).reset_index(drop=True) - - return x.drop(['MAF','CM'], axis=1) + '''Parse LD Score files''' + x = read_csv(fh, header=0, compression=compression) + if 'MAF' in x.columns and 'CM' in x.columns: # for backwards compatibility w/ v<1.0.0 + x = x.drop(['MAF', 'CM'], axis=1) + return x def annot_parser(fh, compression, frqfile_full=None, compression_frq=None): - '''For parsing LD Score files''' - if compression == "gzip" or compression == 'bz2' or compression == None: - x = pd.read_csv(fh, header=0, delim_whitespace=True, compression=compression) - elif compression == 'pickle': - x = pd.read_pickle(fh) - df_annot = x.drop(['CHR','BP','CM'], axis=1) + '''Parse annot files''' + df_annot = read_csv(fh, header=0, compression=compression).drop(['CHR', 'BP', 'CM'], axis=1) + df_annot.iloc[:, 1:] = df_annot.iloc[:, 1:].astype(float) + if frqfile_full is not None: + df_frq = frq_parser(frqfile_full, compression_frq) + df_annot = df_annot[(.95 > df_frq.FRQ) & (df_frq.FRQ > 0.05)] + return df_annot - for name in df_annot.columns[1:]: - df_annot[name] = df_annot[name].astype(float) - - if frqfile_full is not None: - df_frq = frq_parser(frqfile_full, compression_frq) - frq = np.array(df_frq['FRQ']) - df_annot = df_annot[frq > 0.05] - df_annot = df_annot[frq < 0.95] - return df_annot +def frq_parser(fh, compression): + '''Parse frequency files.''' + df = read_csv(fh, header=0, compression=compression) + if 'MAF' in df.columns: + df.rename(columns={'MAF': 'FRQ'}, inplace=True) + return df[['SNP', 'FRQ']] -def frq_parser(fh, compression): - '''For parsing frequency files''' - if compression == "gzip" or compression == 'bz2' or compression == None: - x = pd.read_csv(fh, header=0, delim_whitespace=True, compression=compression) - elif compression == 'pickle': - x = pd.read_pickle(fh) - return x - - def ldscore(fh, num=None): - ''' - Parses .l2.ldscore files. See docs/file_formats_ld.txt - - If num is not None, parses .l2.ldscore files split across [num] chromosomes (e.g., the - output of parallelizing ldsc.py --l2 across chromosomes). - - ''' - - if num is not None: - '''22 files, one for each chromosome''' - suffix = '.l2.ldscore' - if '@' in fh: - first_fh = fh.replace('@', '1')+suffix - else: - first_fh = fh+'1'+suffix - - [s, compression] = which_compression(first_fh) - suffix += s - if '@' in fh: - chr_ld = [l2_parser(fh.replace('@',str(i))+suffix, compression) for i in xrange(1,num+1)] - else: - chr_ld = [l2_parser(fh + str(i) + suffix, compression) for i in xrange(1,num+1)] - - # automatically sorted by chromosome - x = pd.concat(chr_ld) - x = x.drop(['CHR','BP'], axis=1) - - else: - '''Just one file''' - suffix = '.l2.ldscore' - [s, compression] = which_compression(fh+suffix) - suffix += s - x = l2_parser(fh+suffix, compression) - # may need to sort - x = x.sort(['CHR','BP']) - x = x.drop(['CHR','BP'], axis=1) - - x = x.drop_duplicates(subset='SNP') - x = x[x.SNP != '.'] - #check_rsid(x.index) - #for col in x.columns[1:]: - # x[col] = x[col].astype(float) - - return x - -def annot(fh_list, num=None, frqfile=None): + '''Parse .l2.ldscore files, split across num chromosomes. See docs/file_formats_ld.txt.''' + suffix = '.l2.ldscore' + if num is not None: # num files, e.g., one per chromosome + first_fh = sub_chr(fh, 1) + suffix + s, compression = which_compression(first_fh) + chr_ld = [l2_parser(sub_chr(fh, i) + suffix + s, compression) for i in xrange(1, num + 1)] + x = pd.concat(chr_ld) # automatically sorted by chromosome + else: # just one file + s, compression = which_compression(fh + suffix) + x = l2_parser(fh + suffix + s, compression) + + x = x.sort(['CHR', 'BP']) # SEs will be wrong unless sorted + x = x.drop(['CHR', 'BP'], axis=1).drop_duplicates(subset='SNP') + return x + - ''' - Parses .annot files and returns an overlap matrix. See docs/file_formats_ld.txt - - If num is not None, parses .annot files split across [num] chromosomes (e.g., the - output of parallelizing ldsc.py --l2 across chromosomes). - - ''' - - if num is not None: - '''22 files, one for each chromosome''' - annot_suffix = ['.annot' for fh in fh_list] - annot_compression = [] - for (i,fh) in enumerate(fh_list): - if '@' in fh: - first_fh = fh.replace('@', '1')+annot_suffix[i] - else: - first_fh = fh+'1'+annot_suffix[i] - [annot_s,annot_comp_single] = which_compression(first_fh) - annot_suffix[i] += annot_s - annot_compression.append(annot_comp_single) - - if frqfile is not None: - frq_suffix = '.frq' - if '@' in frqfile: - first_frqfile = frqfile.replace('@', '1')+frq_suffix - else: - first_frqfile = frqfile+'1'+frq_suffix - [frq_s, frq_compression] = which_compression(first_frqfile) - frq_suffix += frq_s - - y = [] - M_tot = 0 - for chr in xrange(1,num+1): - - if '@' in fh_list[0]: - if frqfile is not None: - df_annot_chr_list = [annot_parser(fh.replace('@',str(chr))+annot_suffix[i], annot_compression[i], - frqfile.replace('@',str(chr))+frq_suffix,frq_compression) for (i,fh) in enumerate(fh_list)] - else: - df_annot_chr_list = [annot_parser(fh.replace('@',str(chr))+annot_suffix[i], annot_compression[i]) - for (i,fh) in enumerate(fh_list)] - else: - if frqfile is not None: - df_annot_chr_list = [annot_parser(fh + str(chr) + annot_suffix[i], annot_compression[i], - frqfile+str(chr)+frq_suffix, frq_compression) for (i,fh) in enumerate(fh_list)] - else: - df_annot_chr_list = [annot_parser(fh + str(chr) + annot_suffix[i], annot_compression[i]) - for (i,fh) in enumerate(fh_list)] - annot_matrix_chr_list = [np.matrix(df_annot_chr.ix[:,1:]) for df_annot_chr in df_annot_chr_list] - annot_matrix_chr = np.hstack(annot_matrix_chr_list) - y.append(np.dot(annot_matrix_chr.T, annot_matrix_chr)) - M_tot += len(df_annot_chr_list[0]) - del df_annot_chr - del annot_matrix_chr - - x = sum(y) - - else: - - '''Just one file''' - annot_suffix = ['.annot' for fh in fh_list] - annot_compression = [] - for (i,fh) in enumerate(fh_list): - [annot_s,annot_comp_single] = which_compression(fh+annot_suffix[i]) - annot_suffix[i] += annot_s - annot_compression.append(annot_comp_single) - - if frqfile is not None: - frq_suffix = '.frq' - [frq_s, frq_compression] = which_compression(frqfile+frq_suffix) - frq_suffix += frq_s - - df_annot_list = [annot_parser(fh + annot_suffix[i], annot_compression[i], - frqfile+frq_suffix, frq_compression) for (i,fh) in enumerate(fh_list)] - - else: - df_annot_list = [annot_parser(fh + annot_suffix[i], annot_compression[i]) - for (i,fh) in enumerate(fh_list)] - - annot_matrix_list = [np.matrix(y.ix[:,1:]) for y in df_annot_list] - annot_matrix = np.hstack(annot_matrix_list) - - x = np.dot(annot_matrix.T,annot_matrix) - M_tot = len(df_annot_list[0]) - - return [x, M_tot] - - -def M(fh, num=None, N=2, common=None): - ''' - Parses .l{N}.M files. See docs/file_formats_ld.txt. - - If num is not none, parses .l2.M files split across [num] chromosomes (e.g., the output - of parallelizing ldsc.py --l2 across chromosomes). - - ''' - parsefunc = lambda y : [float(z) for z in open(y, 'r').readline().split()] - if common: - suffix = '.l'+str(N)+'.M_5_50' - else: - suffix = '.l'+str(N)+'.M' - if num is not None: - if '@' in fh: - x = np.sum([parsefunc(fh.replace('@',str(i))+suffix) for i in xrange(1,num+1)], axis=0) - else: - x = np.sum([parsefunc(fh+str(i)+suffix) for i in xrange(1,num+1)], axis=0) - else: - x = parsefunc(fh + suffix) - - return x - - -def M_fromfile(flist, num=None, N=2, common = None): - f = open(flist,'r') - flist = [x.rstrip('\n') for x in f.readlines()] - return M_fromlist (flist, num, N, common) - - -def M_fromlist(flist, num=None, N=2, common = None): - M_annot = np.hstack([M(fh.rstrip('\n'), num, N, common) for fh in flist]) - return M_annot +def M(fh, num=None, N=2, common=False): + '''Parses .l{N}.M files, split across num chromosomes. See docs/file_formats_ld.txt.''' + parsefunc = lambda y: [float(z) for z in open(y, 'r').readline().split()] + suffix = '.l' + str(N) + '.M' + if common: + suffix += '_5_50' + + if num is not None: + x = np.sum([parsefunc(sub_chr(fh, i) + suffix) for i in xrange(1, num + 1)], axis=0) + else: + x = parsefunc(fh + suffix) + + return np.array(x).reshape((1, len(x))) + + +def M_fromlist(flist, num=None, N=2, common=False): + '''Read a list of .M* files and concatenate sideways.''' + return np.hstack([M(fh, num, N, common) for fh in flist]) + + +def annot(fh_list, num=None, frqfile=None): + ''' + Parses .annot files and returns an overlap matrix. See docs/file_formats_ld.txt. + If num is not None, parses .annot files split across [num] chromosomes (e.g., the + output of parallelizing ldsc.py --l2 across chromosomes). + + ''' + annot_suffix = ['.annot' for fh in fh_list] + annot_compression = [] + if num is not None: # 22 files, one for each chromosome + for i, fh in enumerate(fh_list): + first_fh = sub_chr(fh, 1) + annot_suffix[i] + annot_s, annot_comp_single = which_compression(first_fh) + annot_suffix[i] += annot_s + annot_compression.append(annot_comp_single) + + if frqfile is not None: + frq_suffix = '.frq' + first_frqfile = sub_chr(frqfile, 1) + frq_suffix + frq_s, frq_compression = which_compression(first_frqfile) + frq_suffix += frq_s + + y = [] + M_tot = 0 + for chr in xrange(1, num + 1): + if frqfile is not None: + df_annot_chr_list = [annot_parser(sub_chr(fh, chr) + annot_suffix[i], annot_compression[i], + sub_chr(frqfile, chr) + frq_suffix, frq_compression) + for i, fh in enumerate(fh_list)] + else: + df_annot_chr_list = [annot_parser(sub_chr(fh, chr) + annot_suffix[i], annot_compression[i]) + for i, fh in enumerate(fh_list)] + + annot_matrix_chr_list = [np.matrix(df_annot_chr.ix[:, 1:]) for df_annot_chr in df_annot_chr_list] + annot_matrix_chr = np.hstack(annot_matrix_chr_list) + y.append(np.dot(annot_matrix_chr.T, annot_matrix_chr)) + M_tot += len(df_annot_chr_list[0]) + + x = sum(y) + else: # just one file + for i, fh in enumerate(fh_list): + annot_s, annot_comp_single = which_compression(fh + annot_suffix[i]) + annot_suffix[i] += annot_s + annot_compression.append(annot_comp_single) + + if frqfile is not None: + frq_suffix = '.frq' + frq_s, frq_compression = which_compression(frqfile + frq_suffix) + frq_suffix += frq_s + + df_annot_list = [annot_parser(fh + annot_suffix[i], annot_compression[i], + frqfile + frq_suffix, frq_compression) for i, fh in enumerate(fh_list)] + + else: + df_annot_list = [annot_parser(fh + annot_suffix[i], annot_compression[i]) + for i, fh in enumerate(fh_list)] + + annot_matrix_list = [np.matrix(y.ix[:, 1:]) for y in df_annot_list] + annot_matrix = np.hstack(annot_matrix_list) + x = np.dot(annot_matrix.T, annot_matrix) + M_tot = len(df_annot_list[0]) + + return x, M_tot def __ID_List_Factory__(colnames, keepcol, fname_end, header=None, usecols=None): - - class IDContainer(object): - - def __init__(self, fname): - self.__usecols__ = usecols - self.__colnames__ = colnames - self.__keepcol__ = keepcol - self.__fname_end__ = fname_end - self.__header__ = header - self.__read__(fname) - - if self.__colnames__: - if 'SNP' in self.__colnames__: - check_rsid(self.df['SNP']) - - self.n = len(self.IDList) - - def __read__(self, fname): - end = self.__fname_end__ - if end and not fname.endswith(end): - raise ValueError('{f} filename must end in {f}'.format(f=end)) - - comp = get_compression(fname) - self.df = pd.read_csv(fname, header=self.__header__, usecols=self.__usecols__, - delim_whitespace=True, compression=comp) - #if np.any(self.df.duplicated(self.df.columns[self.__keepcol__])): - # raise ValueError('Duplicate Entries in Filter File') - - if self.__colnames__: - self.df.columns = self.__colnames__ - - self.IDList = self.df.iloc[:,[self.__keepcol__]].astype('object') - - def loj(self, externalDf): - ''' - Returns indices of those elements of self.IDList that appear in exernalDf - ''' - r = externalDf.columns[0] - l = self.IDList.columns[0] - merge_df = externalDf.iloc[:,[0]] - merge_df['keep'] = True - z = pd.merge(self.IDList, merge_df, how='left',left_on=l, right_on=r, - sort=False) - ii = z['keep'] == True - return np.nonzero(ii)[0] - - return IDContainer - - -PlinkBIMFile = __ID_List_Factory__(['CHR', 'SNP','CM','BP','A1','A2'],1,'.bim',usecols=[0,1,2,3,4,5]) -VcfSNPFile = __ID_List_Factory__(['CHR','BP','SNP','CM'],2,'.snp',usecols=[0,1,2,3]) -PlinkFAMFile = __ID_List_Factory__(['IID'],0,'.fam',usecols=[1]) -VcfINDFile = __ID_List_Factory__(['IID'],0,'.ind',usecols=[0]) -FilterFile = __ID_List_Factory__(['ID'],0,None,usecols=[0]) -AnnotFile = __ID_List_Factory__(None,2,None,header=0,usecols=None) - - - -# def l1(fh, num=None): -# ''' -# Parses .l1.ldscore files. See docs/file_formats_ld.txt -# -# If num is not None, parses .l1.ldscore files split across [num] chromosomes (e.g., the -# output of parallelizing ldsc.py --l1 across chromosomes). -# -# ''' -# -# parsefunc = lambda y, compression : pd.read_csv(y, header=0, delim_whitespace=True, -# compression=compression).drop(['CHR','BP','CM','MAF'], axis=1) -# -# if num is not None: -# try: -# suffix = '.l1.ldscore.gz' -# if '@' in fh: -# full_fh = fh.replace('@', '1') + suffix -# else: -# full_fh = fh + '1' + suffix -# -# x = open(full_fh, 'rb') -# x.close() -# compression = 'gzip' -# except IOError: -# suffix = '.l1.ldscore' -# -# if '@' in fh: -# full_fh = fh.replace('@', '1') + suffix -# else: -# full_fh = fh + '1' + suffix -# x = open(full_fh, 'rb') -# x.close() -# compression = None -# -# if '@' in fh: -# chr_ld = [parsefunc(fh.replace('@',str(i))+suffix, compression) for i in xrange(1,num+1)] -# else: -# chr_ld = [parsefunc(fh + str(i) + suffix, compression) for i in xrange(1,num+1)] -# -# x = pd.concat(chr_ld) -# -# else: -# try: -# full_fh = fh + '.l1.ldscore.gz' -# open(full_fh, 'rb') -# compression = 'gzip' -# except IOError: -# full_fh = fh + '.l1.ldscore' -# open(full_fh, 'rb') -# compression = None -# -# x = parsefunc(full_fh, compression) -# -# ii = x['SNP'] != '.' -# x = x[ii] -# check_rsid(x['SNP']) -# for col in x.columns[1:]: -# x[col] = x[col].astype(float) -# -# return x + class IDContainer(object): + + def __init__(self, fname): + self.__usecols__ = usecols + self.__colnames__ = colnames + self.__keepcol__ = keepcol + self.__fname_end__ = fname_end + self.__header__ = header + self.__read__(fname) + self.n = len(self.IDList) + + def __read__(self, fname): + end = self.__fname_end__ + if end and not fname.endswith(end): + raise ValueError('{f} filename must end in {f}'.format(f=end)) + + comp = get_compression(fname) + self.df = pd.read_csv(fname, header=self.__header__, usecols=self.__usecols__, + delim_whitespace=True, compression=comp) + + if self.__colnames__: + self.df.columns = self.__colnames__ + + self.IDList = self.df.iloc[:, [self.__keepcol__]].astype('object') + + def loj(self, externalDf): + '''Returns indices of those elements of self.IDList that appear in exernalDf.''' + r = externalDf.columns[0] + l = self.IDList.columns[0] + merge_df = externalDf.iloc[:, [0]] + merge_df['keep'] = True + z = pd.merge(self.IDList, merge_df, how='left', left_on=l, right_on=r, + sort=False) + ii = z['keep'] == True + return np.nonzero(ii)[0] + + return IDContainer + + +PlinkBIMFile = __ID_List_Factory__(['CHR', 'SNP', 'CM', 'BP', 'A1', 'A2'], 1, '.bim', usecols=[0, 1, 2, 3, 4, 5]) +PlinkFAMFile = __ID_List_Factory__(['IID'], 0, '.fam', usecols=[1]) +FilterFile = __ID_List_Factory__(['ID'], 0, None, usecols=[0]) +AnnotFile = __ID_List_Factory__(None, 2, None, header=0, usecols=None) diff --git a/ldscore/regressions.py b/ldscore/regressions.py new file mode 100644 index 00000000..db77ee4d --- /dev/null +++ b/ldscore/regressions.py @@ -0,0 +1,714 @@ +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +Estimators of heritability and genetic correlation. + +Shape convention is (n_snp, n_annot) for all classes. +Last column = intercept. + +''' +from __future__ import division +import numpy as np +import pandas as pd +from scipy.stats import norm, chi2 +import jackknife as jk +from irwls import IRWLS +from collections import namedtuple +np.seterr(divide='raise', invalid='raise') + +s = lambda x: remove_brackets(str(np.matrix(x))) + + +def update_separators(s, ii): + '''s are separators with ii masked. Returns unmasked separators.''' + maplist = np.arange(len(ii) + 1)[np.squeeze(ii)] + mask_to_unmask = lambda i: maplist[i] + t = np.apply_along_axis(mask_to_unmask, 0, s[1:-1]) + t = np.hstack(((0), t, (len(ii)))) + return t + + +def p_z_norm(est, se): + '''Convert estimate and se to Z-score and P-value.''' + try: + Z = est / se + except (FloatingPointError, ZeroDivisionError): + Z = float('inf') + + P = chi2.sf(Z ** 2, 1, loc=0, scale=1) # 0 if Z=inf + return P, Z + + +def remove_brackets(x): + '''Get rid of brackets and trailing whitespace in numpy arrays.''' + return x.replace('[', '').replace(']', '').strip() + + +def append_intercept(x): + ''' + Appends an intercept term to the design matrix for a linear regression. + + Parameters + ---------- + x : np.matrix with shape (n_row, n_col) + Design matrix. Columns are predictors; rows are observations. + + Returns + ------- + x_new : np.matrix with shape (n_row, n_col+1) + Design matrix with intercept term appended. + + ''' + n_row = x.shape[0] + intercept = np.ones((n_row, 1)) + x_new = np.concatenate((x, intercept), axis=1) + return x_new + + +def remove_intercept(x): + '''Removes the last column.''' + n_col = x.shape[1] + return x[:, 0:n_col - 1] + + +def gencov_obs_to_liab(gencov_obs, P1, P2, K1, K2): + ''' + Converts genetic covariance on the observed scale in an ascertained sample to genetic + covariance on the liability scale in the population + + Parameters + ---------- + gencov_obs : float + Genetic covariance on the observed scale in an ascertained sample. + P1, P2 : float in (0,1) + Prevalences of phenotypes 1,2 in the sample. + K1, K2 : float in (0,1) + Prevalences of phenotypes 1,2 in the population. + + Returns + ------- + gencov_liab : float + Genetic covariance between liabilities in the population. + + Note: if a trait is a QT, set P = K = None. + + ''' + c1 = 1 + c2 = 1 + if P1 is not None and K1 is not None: + c1 = np.sqrt(h2_obs_to_liab(1, P1, K1)) + if P2 is not None and K2 is not None: + c2 = np.sqrt(h2_obs_to_liab(1, P2, K2)) + + return gencov_obs * c1 * c2 + + +def h2_obs_to_liab(h2_obs, P, K): + ''' + Converts heritability on the observed scale in an ascertained sample to heritability + on the liability scale in the population. + + Parameters + ---------- + h2_obs : float + Heritability on the observed scale in an ascertained sample. + P : float in (0,1) + Prevalence of the phenotype in the sample. + K : float in (0,1) + Prevalence of the phenotype in the population. + + Returns + ------- + h2_liab : float + Heritability of liability in the population. + + ''' + if np.isnan(P) and np.isnan(K): + return h2_obs + if K <= 0 or K >= 1: + raise ValueError('K must be in the range (0,1)') + if P <= 0 or P >= 1: + raise ValueError('P must be in the range (0,1)') + + thresh = norm.isf(K) + conversion_factor = K ** 2 * \ + (1 - K) ** 2 / (P * (1 - P) * norm.pdf(thresh) ** 2) + return h2_obs * conversion_factor + + +class LD_Score_Regression(object): + + def __init__(self, y, x, w, N, M, n_blocks, intercept=None, slow=False, step1_ii=None, old_weights=False): + for i in [y, x, w, M, N]: + try: + if len(i.shape) != 2: + raise TypeError('Arguments must be 2D arrays.') + except AttributeError: + raise TypeError('Arguments must be arrays.') + + n_snp, self.n_annot = x.shape + if any(i.shape != (n_snp, 1) for i in [y, w, N]): + raise ValueError( + 'N, weights and response (z1z2 or chisq) must have shape (n_snp, 1).') + if M.shape != (1, self.n_annot): + raise ValueError('M must have shape (1, n_annot).') + + M_tot = float(np.sum(M)) + x_tot = np.sum(x, axis=1).reshape((n_snp, 1)) + self.constrain_intercept = intercept is not None + self.intercept = intercept + tot_agg = self.aggregate(y, x_tot, N, M_tot, intercept) + initial_w = self._update_weights( + x_tot, w, N, M_tot, tot_agg, intercept) + Nbar = np.mean(N) # keep condition number low + x = np.multiply(N, x) / Nbar + if not self.constrain_intercept: + x, x_tot = append_intercept(x), append_intercept(x_tot) + yp = y + else: + yp = y - intercept + self.intercept_se = 'NA' + + self.twostep_filtered = None + if step1_ii is not None and self.constrain_intercept: + raise ValueError( + 'twostep is not compatible with constrain_intercept.') + elif step1_ii is not None and self.n_annot > 1: + raise ValueError( + 'twostep not compatible with partitioned LD Score yet.') + elif step1_ii is not None: + n1 = np.sum(step1_ii) + self.twostep_filtered = n_snp - n1 + x1 = x[np.squeeze(step1_ii), :] + yp1, w1, N1, initial_w1 = map( + lambda a: a[step1_ii].reshape((n1, 1)), (yp, w, N, initial_w)) + update_func1 = lambda a: self._update_func( + a, x1, w1, N1, M_tot, Nbar, ii=step1_ii) + step1_jknife = IRWLS( + x1, yp1, update_func1, n_blocks, slow=slow, w=initial_w1) + step1_int, _ = self._intercept(step1_jknife) + yp = yp - step1_int + x = remove_intercept(x) + x_tot = remove_intercept(x_tot) + update_func2 = lambda a: self._update_func( + a, x_tot, w, N, M_tot, Nbar, step1_int) + s = update_separators(step1_jknife.separators, step1_ii) + step2_jknife = IRWLS( + x, yp, update_func2, n_blocks, slow=slow, w=initial_w, separators=s) + c = np.sum(np.multiply(initial_w, x)) / \ + np.sum(np.multiply(initial_w, np.square(x))) + jknife = self._combine_twostep_jknives( + step1_jknife, step2_jknife, M_tot, c, Nbar) + elif old_weights: + initial_w = np.sqrt(initial_w) + x = IRWLS._weight(x, initial_w) + y = IRWLS._weight(y, initial_w) + jknife = jk.LstsqJackknifeFast(x, y, n_blocks) + else: + update_func = lambda a: self._update_func( + a, x_tot, w, N, M_tot, Nbar, intercept) + jknife = IRWLS( + x, yp, update_func, n_blocks, slow=slow, w=initial_w) + + self.coef, self.coef_cov, self.coef_se = self._coef(jknife, Nbar) + self.cat, self.cat_cov, self.cat_se =\ + self._cat(jknife, M, Nbar, self.coef, self.coef_cov) + + self.tot, self.tot_cov, self.tot_se = self._tot(self.cat, self.cat_cov) + self.prop, self.prop_cov, self.prop_se =\ + self._prop(jknife, M, Nbar, self.cat, self.tot) + + self.enrichment, self.M_prop = self._enrichment( + M, M_tot, self.cat, self.tot) + if not self.constrain_intercept: + self.intercept, self.intercept_se = self._intercept(jknife) + + self.jknife = jknife + self.tot_delete_values = self._delete_vals_tot(jknife, Nbar, M) + if not self.constrain_intercept: + self.intercept_delete_values = jknife.delete_values[ + :, self.n_annot] + + self.M = M + + @classmethod + def aggregate(cls, y, x, N, M, intercept=None): + if intercept is None: + intercept = cls.__null_intercept__ + + num = M * (np.mean(y) - intercept) + denom = np.mean(np.multiply(x, N)) + return num / denom + + def _update_func(self, x, ref_ld_tot, w_ld, N, M, Nbar, intercept=None, ii=None): + raise NotImplementedError + + def _delete_vals_tot(self, jknife, Nbar, M): + '''Get delete values for total h2 or gencov.''' + n_annot = self.n_annot + tot_delete_vals = jknife.delete_values[ + :, 0:n_annot] # shape (n_blocks, n_annot) + # shape (n_blocks, 1) + tot_delete_vals = np.dot(tot_delete_vals, M.T) / Nbar + return tot_delete_vals + + def _coef(self, jknife, Nbar): + '''Get coefficient estimates + cov from the jackknife.''' + n_annot = self.n_annot + coef = jknife.est[0, 0:n_annot] / Nbar + coef_cov = jknife.jknife_cov[0:n_annot, 0:n_annot] / Nbar ** 2 + coef_se = np.sqrt(np.diag(coef_cov)) + return coef, coef_cov, coef_se + + def _cat(self, jknife, M, Nbar, coef, coef_cov): + '''Convert coefficients to per-category h2 or gencov.''' + cat = np.multiply(M, coef) + cat_cov = np.multiply(np.dot(M.T, M), coef_cov) + cat_se = np.sqrt(np.diag(cat_cov)) + return cat, cat_cov, cat_se + + def _tot(self, cat, cat_cov): + '''Convert per-category h2 to total h2 or gencov.''' + tot = np.sum(cat) + tot_cov = np.sum(cat_cov) + tot_se = np.sqrt(tot_cov) + return tot, tot_cov, tot_se + + def _prop(self, jknife, M, Nbar, cat, tot): + '''Convert total h2 and per-category h2 to per-category proportion h2 or gencov.''' + n_annot = self.n_annot + n_blocks = jknife.delete_values.shape[0] + numer_delete_vals = np.multiply( + M, jknife.delete_values[:, 0:n_annot]) / Nbar # (n_blocks, n_annot) + denom_delete_vals = np.sum( + numer_delete_vals, axis=1).reshape((n_blocks, 1)) + denom_delete_vals = np.dot(denom_delete_vals, np.ones((1, n_annot))) + prop = jk.RatioJackknife( + cat / tot, numer_delete_vals, denom_delete_vals) + return prop.est, prop.jknife_cov, prop.jknife_se + + def _enrichment(self, M, M_tot, cat, tot): + '''Compute proportion of SNPs per-category enrichment for h2 or gencov.''' + M_prop = M / M_tot + enrichment = np.divide(cat, M) / (tot / M_tot) + return enrichment, M_prop + + def _intercept(self, jknife): + '''Extract intercept and intercept SE from block jackknife.''' + n_annot = self.n_annot + intercept = jknife.est[0, n_annot] + intercept_se = jknife.jknife_se[0, n_annot] + return intercept, intercept_se + + def _combine_twostep_jknives(self, step1_jknife, step2_jknife, M_tot, c, Nbar=1): + '''Combine free intercept and constrained intercept jackknives for --two-step.''' + n_blocks, n_annot = step1_jknife.delete_values.shape + n_annot -= 1 + if n_annot > 2: + raise ValueError( + 'twostep not yet implemented for partitioned LD Score.') + + step1_int, _ = self._intercept(step1_jknife) + est = np.hstack( + (step2_jknife.est, np.array(step1_int).reshape((1, 1)))) + delete_values = np.zeros((n_blocks, n_annot + 1)) + delete_values[:, n_annot] = step1_jknife.delete_values[:, n_annot] + delete_values[:, 0:n_annot] = step2_jknife.delete_values -\ + c * (step1_jknife.delete_values[:, n_annot] - + step1_int).reshape((n_blocks, n_annot)) # check this + pseudovalues = jk.Jackknife.delete_values_to_pseudovalues( + delete_values, est) + jknife_est, jknife_var, jknife_se, jknife_cov = jk.Jackknife.jknife( + pseudovalues) + jknife = namedtuple('jknife', + ['est', 'jknife_se', 'jknife_est', 'jknife_var', 'jknife_cov', 'delete_values']) + return jknife(est, jknife_se, jknife_est, jknife_var, jknife_cov, delete_values) + + +class Hsq(LD_Score_Regression): + + __null_intercept__ = 1 + + def __init__(self, y, x, w, N, M, n_blocks=200, intercept=None, slow=False, twostep=None, old_weights=False): + step1_ii = None + if twostep is not None: + step1_ii = y < twostep + + LD_Score_Regression.__init__(self, y, x, w, N, M, n_blocks, intercept=intercept, + slow=slow, step1_ii=step1_ii, old_weights=old_weights) + self.mean_chisq, self.lambda_gc = self._summarize_chisq(y) + if not self.constrain_intercept: + self.ratio, self.ratio_se = self._ratio( + self.intercept, self.intercept_se, self.mean_chisq) + + def _update_func(self, x, ref_ld_tot, w_ld, N, M, Nbar, intercept=None, ii=None): + ''' + Update function for IRWLS + + x is the output of np.linalg.lstsq. + x[0] is the regression coefficients + x[0].shape is (# of dimensions, 1) + the last element of x[0] is the intercept. + + intercept is None --> free intercept + intercept is not None --> constrained intercept + ''' + hsq = M * x[0][0] / Nbar + if intercept is None: + intercept = max(x[0][1]) # divide by zero error if intercept < 0 + else: + if ref_ld_tot.shape[1] > 1: + raise ValueError( + 'Design matrix has intercept column for constrained intercept regression!') + + ld = ref_ld_tot[:, 0].reshape(w_ld.shape) # remove intercept + w = self.weights(ld, w_ld, N, M, hsq, intercept, ii) + return w + + def _summarize_chisq(self, chisq): + '''Compute mean chi^2 and lambda_GC.''' + mean_chisq = np.mean(chisq) + # median and matrix don't play nice + lambda_gc = np.median(np.asarray(chisq)) / 0.4549 + return mean_chisq, lambda_gc + + def _ratio(self, intercept, intercept_se, mean_chisq): + '''Compute ratio (intercept - 1) / (mean chi^2 -1 ).''' + if mean_chisq > 1: + ratio_se = intercept_se / (mean_chisq - 1) + ratio = (intercept - 1) / (mean_chisq - 1) + else: + ratio = 'NA' + ratio_se = 'NA' + + return ratio, ratio_se + + def _overlap_output(self, category_names, overlap_matrix, M_annot, M_tot, print_coefficients): + '''LD Score regression summary for overlapping categories.''' + for i in range(self.n_annot): + overlap_matrix[i, :] = overlap_matrix[i, :] / M_annot + + prop_hsq_overlap = np.dot( + overlap_matrix, self.prop.T).reshape((1, self.n_annot)) + prop_hsq_overlap_var = np.diag( + np.dot(np.dot(overlap_matrix, self.prop_cov), overlap_matrix.T)) + prop_hsq_overlap_se = np.sqrt( + np.maximum(0, prop_hsq_overlap_var)).reshape((1, self.n_annot)) + one_d_convert = lambda x: np.array(x).reshape(np.prod(x.shape)) + prop_M_overlap = M_annot / M_tot + enrichment = prop_hsq_overlap / prop_M_overlap + enrichment_se = prop_hsq_overlap_se / prop_M_overlap + df = pd.DataFrame({ + 'Category': category_names, + 'Prop._SNPs': one_d_convert(prop_M_overlap), + 'Prop._h2': one_d_convert(prop_hsq_overlap), + 'Prop._h2_std_error': one_d_convert(prop_hsq_overlap_se), + 'Enrichment': one_d_convert(enrichment), + 'Enrichment_std_error': one_d_convert(enrichment_se), + 'Coefficient': one_d_convert(self.coef), + 'Coefficient_std_error': self.coef_se, + 'Coefficient_z-score': one_d_convert(self.coef) / one_d_convert(self.coef_se) + }) + df = df[np.logical_not(df['Prop._SNPs'] > .9999)] + df['Enrichment_p'] = chi2.sf( + one_d_convert((df.Enrichment - 1) / df.Enrichment_std_error) ** 2, 1) + if print_coefficients: + df = df[['Category', 'Prop._SNPs', 'Prop._h2', 'Prop._h2_std_error', 'Enrichment', + 'Enrichment_std_error', 'Enrichment_p', 'Coefficient', 'Coefficient_std_error', + 'Coefficient_z-score']] + else: + df = df[['Category', 'Prop._SNPs', 'Prop._h2', 'Prop._h2_std_error', 'Enrichment', + 'Enrichment_std_error', 'Enrichment_p']] + return df + + def summary(self, ref_ld_colnames=None, P=None, K=None): + '''Print summary of the LD Score Regression.''' + if P is not None and K is not None: + T = 'Liability' + c = h2_obs_to_liab(1, P, K) + else: + T = 'Observed' + c = 1 + + out = ['Total ' + T + ' scale h2: ' + + s(c * self.tot) + ' (' + s(c * self.tot_se) + ')'] + if self.n_annot > 1: + if ref_ld_colnames is None: + ref_ld_colnames = ['CAT_' + str(i) + for i in xrange(self.n_annot)] + + out.append('Categories: ' + ' '.join(ref_ld_colnames)) + out.append(T + ' scale h2: ' + s(c * self.cat)) + out.append(T + ' scale h2 SE: ' + s(c * self.cat_se)) + out.append('Proportion of SNPs: ' + s(self.M_prop)) + out.append('Proportion of h2g: ' + s(self.prop)) + out.append('Enrichment: ' + s(self.enrichment)) + out.append('Coefficients: ' + s(self.coef)) + out.append('Coefficient SE: ' + s(self.coef_se)) + + out.append('Lambda GC: ' + s(self.lambda_gc)) + out.append('Mean Chi^2: ' + s(self.mean_chisq)) + if self.constrain_intercept: + out.append( + 'Intercept: constrained to {C}'.format(C=s(self.intercept))) + else: + out.append( + 'Intercept: ' + s(self.intercept) + ' (' + s(self.intercept_se) + ')') + if self.mean_chisq > 1: + if self.ratio < 0: + out.append( + 'Ratio < 0 (ususally indicates GC correction).') + else: + out.append( + 'Ratio: ' + s(self.ratio) + ' (' + s(self.ratio_se) + ')') + else: + out.append('Ratio: NA (mean chi^2 < 1)') + + return remove_brackets('\n'.join(out)) + + def _update_weights(self, ld, w_ld, N, M, hsq, intercept, ii=None): + if intercept is None: + intercept = self.__null_intercept__ + + return self.weights(ld, w_ld, N, M, hsq, intercept, ii) + + @classmethod + def weights(cls, ld, w_ld, N, M, hsq, intercept=None, ii=None): + ''' + Regression weights. + + Parameters + ---------- + ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned). + w_ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included + in the regression. + N : np.matrix of ints > 0 with shape (n_snp, 1) + Number of individuals sampled for each SNP. + M : float > 0 + Number of SNPs used for estimating LD Score (need not equal number of SNPs included in + the regression). + hsq : float in [0,1] + Heritability estimate. + + Returns + ------- + w : np.matrix with shape (n_snp, 1) + Regression weights. Approx equal to reciprocal of conditional variance function. + + ''' + M = float(M) + if intercept is None: + intercept = 1 + + hsq = max(hsq, 0.0) + hsq = min(hsq, 1.0) + ld = np.fmax(ld, 1.0) + w_ld = np.fmax(w_ld, 1.0) + c = hsq * N / M + het_w = 1.0 / (2 * np.square(intercept + np.multiply(c, ld))) + oc_w = 1.0 / w_ld + w = np.multiply(het_w, oc_w) + return w + + +class Gencov(LD_Score_Regression): + __null_intercept__ = 0 + + def __init__(self, z1, z2, x, w, N1, N2, M, hsq1, hsq2, intercept_hsq1, intercept_hsq2, + n_blocks=200, intercept_gencov=None, slow=False, twostep=None): + self.intercept_hsq1 = intercept_hsq1 + self.intercept_hsq2 = intercept_hsq2 + self.hsq1 = hsq1 + self.hsq2 = hsq2 + self.N1 = N1 + self.N2 = N2 + y = z1 * z2 + step1_ii = None + if twostep is not None: + step1_ii = np.logical_and(z1**2 < twostep, z2**2 < twostep) + + LD_Score_Regression.__init__(self, y, x, w, np.sqrt(N1 * N2), M, n_blocks, + intercept=intercept_gencov, slow=slow, step1_ii=step1_ii) + self.p, self.z = p_z_norm(self.tot, self.tot_se) + self.mean_z1z2 = np.mean(np.multiply(z1, z2)) + + def summary(self, ref_ld_colnames, P=None, K=None): + '''Print summary of the LD Score regression.''' + out = [] + if P is not None and K is not None and\ + all((i is not None for i in P)) and all((i is not None for i in K)): + T = 'Liability' + c = gencov_obs_to_liab(1, P[0], P[1], K[0], K[1]) + else: + T = 'Observed' + c = 1 + + out.append('Total ' + T + ' scale gencov: ' + + s(self.tot) + ' (' + s(self.tot_se) + ')') + if self.n_annot > 1: + out.append('Categories: ' + str(' '.join(ref_ld_colnames))) + out.append(T + ' scale gencov: ' + s(c * self.cat)) + out.append(T + ' scale gencov SE: ' + s(c * self.cat_se)) + out.append('Proportion of SNPs: ' + s(self.M_prop)) + out.append('Proportion of gencov: ' + s(self.prop)) + out.append('Enrichment: ' + s(self.enrichment)) + + out.append('Mean z1*z2: ' + s(self.mean_z1z2)) + if self.constrain_intercept: + out.append( + 'Intercept: constrained to {C}'.format(C=s(self.intercept))) + else: + out.append( + 'Intercept: ' + s(self.intercept) + ' (' + s(self.intercept_se) + ')') + + return remove_brackets('\n'.join(out)) + + def _update_func(self, x, ref_ld_tot, w_ld, N, M, Nbar, intercept=None, ii=None): + ''' + Update function for IRWLS + x is the output of np.linalg.lstsq. + x[0] is the regression coefficients + x[0].shape is (# of dimensions, 1) + the last element of x[0] is the intercept. + + ''' + rho_g = M * x[0][0] / Nbar + if intercept is None: # if the regression includes an intercept + intercept = x[0][1] + + # remove intercept if we have one + ld = ref_ld_tot[:, 0].reshape(w_ld.shape) + if ii is not None: + N1 = self.N1[ii].reshape((w_ld.shape)) + N2 = self.N2[ii].reshape((w_ld.shape)) + else: + N1 = self.N1 + N2 = self.N2 + + return self.weights(ld, w_ld, N1, N2, np.sum(M), self.hsq1, self.hsq2, rho_g, + intercept, self.intercept_hsq1, self.intercept_hsq2, ii) + + def _update_weights(self, ld, w_ld, sqrt_n1n2, M, rho_g, intercept, ii=None): + '''Weight function with the same signature for Hsq and Gencov.''' + w = self.weights(ld, w_ld, self.N1, self.N2, M, self.hsq1, self.hsq2, rho_g, + intercept, self.intercept_hsq1, self.intercept_hsq2) + return w + + @classmethod + def weights(cls, ld, w_ld, N1, N2, M, h1, h2, rho_g, intercept_gencov=None, + intercept_hsq1=None, intercept_hsq2=None, ii=None): + ''' + Regression weights. + + Parameters + ---------- + ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned) + w_ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included + in the regression. + M : float > 0 + Number of SNPs used for estimating LD Score (need not equal number of SNPs included in + the regression). + N1, N2 : np.matrix of ints > 0 with shape (n_snp, 1) + Number of individuals sampled for each SNP for each study. + h1, h2 : float in [0,1] + Heritability estimates for each study. + rhog : float in [0,1] + Genetic covariance estimate. + intercept : float + Genetic covariance intercept, on the z1*z2 scale (so should be Ns*rho/sqrt(N1*N2)). + + Returns + ------- + w : np.matrix with shape (n_snp, 1) + Regression weights. Approx equal to reciprocal of conditional variance function. + + ''' + M = float(M) + if intercept_gencov is None: + intercept_gencov = 0 + if intercept_hsq1 is None: + intercept_hsq1 = 1 + if intercept_hsq2 is None: + intercept_hsq2 = 1 + + h1, h2 = max(h1, 0.0), max(h2, 0.0) + h1, h2 = min(h1, 1.0), min(h2, 1.0) + rho_g = min(rho_g, 1.0) + rho_g = max(rho_g, -1.0) + ld = np.fmax(ld, 1.0) + w_ld = np.fmax(w_ld, 1.0) + a = np.multiply(N1, h1 * ld) / M + intercept_hsq1 + b = np.multiply(N2, h2 * ld) / M + intercept_hsq2 + sqrt_n1n2 = np.sqrt(np.multiply(N1, N2)) + c = np.multiply(sqrt_n1n2, rho_g * ld) / M + intercept_gencov + try: + het_w = 1.0 / (np.multiply(a, b) + np.square(c)) + except FloatingPointError: # bizarre error; should never happen + raise FloatingPointError('Why did you set hsq intercept <= 0?') + + oc_w = 1.0 / w_ld + w = np.multiply(het_w, oc_w) + return w + + +class RG(object): + + def __init__(self, z1, z2, x, w, N1, N2, M, intercept_hsq1=None, intercept_hsq2=None, + intercept_gencov=None, n_blocks=200, slow=False, twostep=None): + + self._negative_hsq = None + n_snp, n_annot = x.shape + hsq1 = Hsq(np.square(z1), x, w, N1, M, n_blocks=n_blocks, intercept=intercept_hsq1, + slow=slow, twostep=twostep) + hsq2 = Hsq(np.square(z2), x, w, N2, M, n_blocks=n_blocks, intercept=intercept_hsq2, + slow=slow, twostep=twostep) + gencov = Gencov(z1, z2, x, w, N1, N2, M, hsq1.tot, hsq2.tot, hsq1.intercept, + hsq2.intercept, n_blocks, intercept_gencov=intercept_gencov, slow=slow, + twostep=twostep) + gencov.N1 = None # save memory + gencov.N2 = None + self.hsq1, self.hsq2, self.gencov = hsq1, hsq2, gencov + if (hsq1.tot <= 0 or hsq2.tot <= 0): + self._negative_hsq = True + self.rg_ratio = self.rg = self.rg_se = 'NA' + self.p = self.z = 'NA' + else: + rg_ratio = np.array( + gencov.tot / np.sqrt(hsq1.tot * hsq2.tot)).reshape((1, 1)) + denom_delete_values = np.sqrt( + np.multiply(hsq1.tot_delete_values, hsq2.tot_delete_values)) + rg = jk.RatioJackknife( + rg_ratio, gencov.tot_delete_values, denom_delete_values) + self.rg_jknife = float(rg.jknife_est) + self.rg_se = float(rg.jknife_se) + self.rg_ratio = float(rg_ratio) + self.p, self.z = p_z_norm(self.rg_ratio, self.rg_se) + + def summary(self, silly=False): + '''Print output of Gencor object.''' + out = [] + if self._negative_hsq: + out.append('Genetic Correlation: nan (nan) (h2 out of bounds) ') + out.append('Z-score: nan (nan) (h2 out of bounds)') + out.append('P: nan (nan) (h2 out of bounds)') + out.append('WARNING: One of the h2\'s was out of bounds.') + out.append( + 'This usually indicates a data-munging error or that h2 or N is low.') + elif (self.rg_ratio > 1.2 or self.rg_ratio < -1.2) and not silly: + out.append('Genetic Correlation: nan (nan) (rg out of bounds) ') + out.append('Z-score: nan (nan) (rg out of bounds)') + out.append('P: nan (nan) (rg out of bounds)') + out.append('WARNING: rg was out of bounds.') + out.append( + 'This usually means that h2 is not significantly different from zero.') + else: + out.append( + 'Genetic Correlation: ' + s(self.rg_ratio) + ' (' + s(self.rg_se) + ')') + out.append('Z-score: ' + s(self.z)) + out.append('P: ' + s(self.p)) + + return remove_brackets('\n'.join(out)) diff --git a/ldscore/sumstats.py b/ldscore/sumstats.py index 18ed3e2b..e67ea03c 100644 --- a/ldscore/sumstats.py +++ b/ldscore/sumstats.py @@ -1,890 +1,499 @@ ''' (c) 2014 Brendan Bulik-Sullivan and Hilary Finucane -This module contains objects implementing LD Score related analyses on GWAS summary -statistics. +This module deals with getting all the data needed for LD Score regression from files +into memory and checking that the input makes sense. There is no math here. LD Score +regression is implemented in the regressions module. ''' - from __future__ import division -import parse as ps import numpy as np import pandas as pd -import jackknife as jk -import parse as ps -import sys, traceback import itertools as it -import time -from scipy import stats +import parse as ps +import regressions as reg +import sys +import traceback +import copy +_N_CHR = 22 # complementary bases -COMPLEMENT = { - 'A': 'T', - 'T': 'A', - 'C': 'G', - 'G': 'C' -} - +COMPLEMENT = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'} # bases BASES = COMPLEMENT.keys() - # true iff strand ambiguous -STRAND_AMBIGUOUS = {''.join(x): x[0] == COMPLEMENT[x[1]] - for x in it.product(BASES,BASES) - if x[0] != x[1]} - -# True iff SNP 1 has the same alleles as SNP 2 (possibly w/ strand or ref allele flip) -MATCH_ALLELES = {''.join(x): - ((x[0] == x[2]) and (x[1] == x[3])) or # strand and ref match - ((x[0] == COMPLEMENT[x[2]]) and (x[1] == COMPLEMENT[x[3]])) or # ref match, strand flip - ((x[0] == x[3]) and (x[1] == x[2])) or # ref flip, strand match - ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]])) # strand and ref flip - for x in it.product(BASES,BASES,BASES,BASES) - if (x[0] != x[1]) and (x[2] != x[3]) and - not STRAND_AMBIGUOUS[''.join(x[0:2])] and - not STRAND_AMBIGUOUS[''.join(x[2:4])]} - -# True iff SNP 1 has the same alleles as SNP 2 w/ ref allele flip (strand flip optional) +STRAND_AMBIGUOUS = {''.join(x): x[0] == COMPLEMENT[x[1]] + for x in it.product(BASES, BASES) + if x[0] != x[1]} +# SNPS we want to keep (pairs of alleles) +VALID_SNPS = {x for x in map(lambda y: ''.join(y), it.product(BASES, BASES)) + if x[0] != x[1] and not STRAND_AMBIGUOUS[x]} +# T iff SNP 1 has the same alleles as SNP 2 (allowing for strand or ref allele flip). +MATCH_ALLELES = {x for x in map(lambda y: ''.join(y), it.product(VALID_SNPS, VALID_SNPS)) + # strand and ref match + if ((x[0] == x[2]) and (x[1] == x[3])) or + # ref match, strand flip + ((x[0] == COMPLEMENT[x[2]]) and (x[1] == COMPLEMENT[x[3]])) or + # ref flip, strand match + ((x[0] == x[3]) and (x[1] == x[2])) or + ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]]))} # strand and ref flip +# T iff SNP 1 has the same alleles as SNP 2 w/ ref allele flip. FLIP_ALLELES = {''.join(x): - ((x[0] == x[3]) and (x[1] == x[2])) or # strand match - ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]])) # strand flip - for x in it.product(BASES,BASES,BASES,BASES) - if (x[0] != x[1]) and (x[2] != x[3]) and - (x[0] != COMPLEMENT[x[1]]) and (x[2] != COMPLEMENT[x[3]]) - and MATCH_ALLELES[''.join(x)]} - -def sec_to_str(t): - '''Convert seconds to days:hours:minutes:seconds''' - [d, h, m, s, n] = reduce(lambda ll,b : divmod(ll[0],b) + ll[1:], [(t, 1), 60,60, 24]) - f = '' - if d > 0: - f += '{D}d:'.format(D=d) - if h > 0: - f += '{H}h:'.format(H=h) - if m > 0: - f += '{M}m:'.format(M=m) - - f += '{S}s'.format(S=s) - return f + ((x[0] == x[3]) and (x[1] == x[2])) or # strand match + # strand flip + ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]])) + for x in MATCH_ALLELES} + + +def _select_and_log(x, ii, log, msg): + '''Fiter down to rows that are True in ii. Log # of SNPs removed.''' + new_len = ii.sum() + if new_len == 0: + raise ValueError(msg.format(N=0)) + else: + x = x[ii] + log.log(msg.format(N=new_len)) + return x + def smart_merge(x, y): - '''Check if SNP columns are equal. If so, save time by using concat instead of merge.''' - if len(x.SNP) == len(y.SNP) and (x.SNP == y.SNP).all(): - x = x.reset_index(drop=True) - y = y.reset_index(drop=True).drop('SNP',1) - out = pd.concat([x, y], axis=1) - - else: - out = pd.merge(x, y, how='inner', on='SNP') - - return out - -class Logger(object): - ''' - Lightweight logging. - - TODO: replace with logging module - - ''' - def __init__(self, fh): - self.log_name = fh - self.log_fh = open(fh, 'wb') - - def log(self, msg): - ''' - Print to log file and stdout with a single command. - - ''' - print >>self.log_fh, msg - print msg - - -class _sumstats(object): - ''' - Base class implementing basic summary statistic functions. - - One small compromise -- state is modified within methods only via log messages. - Waiting until the end of method and returning a log message is sometimes slow; this - would be inconvenient for debugging and interactive users. - - ''' - - def __init__(self, args, log, header=None): - raise NotImplementedError - - def _read_ref_ld(self, args, log): - '''Read reference LD Scores''' - try: - if args.ref_ld: - log.log('Reading reference LD Scores from {F} ...'.format(F=args.ref_ld)) - ref_ldscores = ps.ldscore(args.ref_ld) - elif args.ref_ld_chr: - if '@' in args.ref_ld_chr: - f = args.ref_ld_chr.replace('@','[1-22]') - else: - f = args.ref_ld_chr+'[1-22]' - log.log('Reading reference LD Scores from {F} ...'.format(F=f)) - ref_ldscores = ps.ldscore(args.ref_ld_chr, 22) - elif args.ref_ld_file: - log.log('Reading reference LD Scores listed in {F} ...'.format(F=args.ref_ld_file)) - ref_ldscores = ps.ldscore_fromfile(args.ref_ld_file) - elif args.ref_ld_file_chr: - log.log('Reading reference LD Scores listed in {F} ...'.format(F=args.ref_ld_file_chr)) - ref_ldscores = ps.ldscore_fromfile(args.ref_ld_file_chr, 22) - elif args.ref_ld_list: - log.log('Reading list of reference LD Scores...') - flist = args.ref_ld_list.split(',') - ref_ldscores = ps.ldscore_fromlist(flist) - elif args.ref_ld_list_chr: - log.log('Reading list of reference LD Scores...') - flist = args.ref_ld_list_chr.split(',') - ref_ldscores = ps.ldscore_fromlist(flist, 22) - - except ValueError as e: - log.log('Error parsing reference LD.') - raise e - - log_msg = 'Read reference panel LD Scores for {N} SNPs.' - log.log(log_msg.format(N=len(ref_ldscores))) - return ref_ldscores - - def _read_annot(self,args,log): - '''Read annot matrix''' - try: - if args.ref_ld: - log.log('Reading annot matrix from {F} ...'.format(F=args.ref_ld)) - [overlap_matrix, M_tot] = ps.annot([args.ref_ld],frqfile=args.frqfile) - elif args.ref_ld_chr: - if '@' in args.ref_ld_chr: - f = args.ref_ld_chr.replace('@','[1-22]') - else: - f = args.ref_ld_chr+'[1-22]' - log.log('Reading annot matrices from {F} ...'.format(F=f)) - [overlap_matrix, M_tot] = ps.annot([args.ref_ld_chr], 22,frqfile=args.frqfile) - elif args.ref_ld_file: - log.log('Reading annot matrices listed in {F} ...'.format(F=args.ref_ld_file)) - [overlap_matrix, M_tot] = ps.annot_fromfile(args.ref_ld_file,frqfile=args.frqfile) - elif args.ref_ld_file_chr: - log.log('Reading annot matrices listed in {F} ...'.format(F=args.ref_ld_file_chr)) - [overlap_matrix, M_tot] = ps.annot_fromfile(args.ref_ld_file_chr, 22,frqfile=args.frqfile) - elif args.ref_ld_list: - log.log('Reading annot matrices...') - flist = args.ref_ld_list.split(',') - [overlap_matrix, M_tot] = ps.annot(flist,frqfile=args.frqfile) - elif args.ref_ld_list_chr: - log.log('Reading annot matrices...') - flist = args.ref_ld_list_chr.split(',') - [overlap_matrix, M_tot] = ps.annot(flist, 22,frqfile=args.frqfile) - - except ValueError as e: - log.log('Error reading annot matrix.') - raise e - - log_msg = 'Read annot matrix.' - log.log(log_msg) - - return [overlap_matrix, M_tot] - - def _parse_sumstats(self, args, log, fh, require_alleles=False, keep_na=False): - # priority is pickle, bz2, gz, uncompressed - chisq = fh + '.chisq' - chisq += ps.which_compression(fh+'.chisq')[0] - self.log.log('Reading summary statistics from {S} ...'.format(S=chisq)) - sumstats = ps.chisq(chisq, require_alleles, keep_na, args.no_check) - log_msg = 'Read summary statistics for {N} SNPs.' - log.log(log_msg.format(N=len(sumstats))) - if args.no_check: - m = len(sumstats) - sumstats = sumstats.drop_duplicates(subset='SNP') - if m > len(sumstats): - log.log('Dropped {M} SNPs with duplicated rs numbers.'.format(M=m-len(sumstats))) - - return sumstats - - def _read_M(self, args, log): - '''Read M (--M, --M-file, etc)''' - if args.M: - try: - M_annot = [float(x) for x in args.M.split(',')] - except TypeError as e: - raise TypeError('Could not cast --M to float: ' + str(e.args)) - - if len(M_annot) != len(ref_ldscores.columns) - 1: - msg = 'Number of comma-separated terms in --M must match the number of partitioned' - msg += 'LD Scores in --ref-ld' - raise ValueError(msg) - - # read .M or --M-file - else: - if args.M_file: - if args.ref_ld: - M_annot = ps.M(args.M_file) - elif args.ref_ld_chr: - M_annot = ps.M(args.M_file, 22) - elif args.not_M_5_50: - if args.ref_ld: - M_annot = ps.M(args.ref_ld) - elif args.ref_ld_chr: - M_annot = ps.M(args.ref_ld_chr, 22) - elif args.ref_ld_file: - M_annot = ps.M_fromfile(args.ref_ld_file) - elif args.ref_ld_file_chr: - M_annot = ps.M_fromfile(args.ref_ld_file_chr, 22) - elif args.ref_ld_list: - flist = args.ref_ld_list.split(',') - M_annot = ps.M_fromlist(flist) - elif args.ref_ld_list_chr: - flist = args.ref_ld_list_chr.split(',') - M_annot = ps.M_fromlist(flist, 22) - else: - if args.ref_ld: - M_annot = ps.M(args.ref_ld, common=True) - elif args.ref_ld_chr: - M_annot = ps.M(args.ref_ld_chr, 22, common=True) - elif args.ref_ld_file: - M_annot = ps.M_fromfile(args.ref_ld_file, common=True) - elif args.ref_ld_file_chr: - M_annot = ps.M_fromfile(args.ref_ld_file_chr, 22, common=True) - elif args.ref_ld_list: - flist = args.ref_ld_list.split(',') - M_annot = ps.M_fromlist(flist, common=True) - elif args.ref_ld_list_chr: - flist = args.ref_ld_list_chr.split(',') - M_annot = ps.M_fromlist(flist, 22, common=True) - - return M_annot - - def _read_w_ld(self, args, log): - '''Read regression SNP LD''' - try: - if args.w_ld: - log.log('Reading regression weight LD Scores from {F} ...'.format(F=args.w_ld)) - w_ldscores = ps.ldscore(args.w_ld) - elif args.w_ld_chr: - if '@' in args.w_ld_chr: - f = args.w_ld_chr.replace('@','[1-22]') - else: - f = args.w_ld_chr+'[1-22]' - - log.log('Reading regression weight LD Scores from {F} ...'.format(F=f)) - w_ldscores = ps.ldscore(args.w_ld_chr, 22) - - except ValueError as e: - log.log('Error parsing regression SNP LD.') - raise e - - if len(w_ldscores.columns) != 2: - raise ValueError('--w-ld must point to a file with a single (non-partitioned) LD Score.') - - # to keep the column names from being the same - w_ldscores.columns = ['SNP','LD_weights'] - - log_msg = 'Read LD Scores for {N} SNPs to be retained for regression.' - log.log(log_msg.format(N=len(w_ldscores))) - return w_ldscores - - def _check_ld_condnum(self, args, log, M_annot, ref_ld): - '''Check condition number of LD Score matrix''' - if len(M_annot) > 1: - cond_num = np.linalg.cond(ref_ld) - if cond_num > 100000: - cond_num = round(cond_num, 3) - if args.invert_anyway: - warn = "WARNING: LD Score matrix condition number is {C}. " - warn += "Inverting anyway because the --invert-anyway flag is set." - log.log(warn.format(C=cond_num)) - else: - warn = "WARNING: LD Score matrix condition number is {C}. " - warn += "Remove collinear LD Scores or force ldsc to use a pseudoinverse with " - warn += "the --invert-anyway flag." - log.log(warn.format(C=cond_num)) - raise ValueError(warn.format(C=cond_num)) - - def _check_variance(self, log, M_annot, ref_ldscores): - '''Remove zero-variance LD Scores''' - ii = np.squeeze(np.array(ref_ldscores.iloc[:,0:len(ref_ldscores.columns)].var(axis=0) == 0)) - if np.all(ii): - raise ValueError('All LD Scores have zero variance.') - elif np.any(ii): - log.log('Removing partitioned LD Scores with zero variance.') - ii = np.insert(ii, 0, False) # keep the SNP column - ref_ldscores = ref_ldscores.ix[:,np.logical_not(ii)] - M_annot = [M_annot[i] for i in xrange(1,len(ii)) if not ii[i]] - n_annot = len(M_annot) - - return(M_annot, ref_ldscores) - - def _keep_ld(self, args, log, M_annot, ref_ldscores): - '''Filter down to SNPs specified by --keep-ld''' - if args.keep_ld is not None: - try: - keep_M_indices = [int(x) for x in args.keep_ld.split(',')] - keep_ld_colnums = [int(x)+1 for x in args.keep_ld.split(',')] - except ValueError as e: - raise ValueError('--keep-ld must be a comma-separated list of column numbers: '\ - +str(e.args)) - - if len(keep_ld_colnums) == 0: - raise ValueError('No reference LD columns retained by --keep-ld.') - - keep_ld_colnums = [0] + keep_ld_colnums - try: - M_annot = [M_annot[i] for i in keep_M_indices] - ref_ldscores = ref_ldscores.ix[:,keep_ld_colnums] - except IndexError as e: - raise IndexError('--keep-ld column numbers are out of bounds: '+str(e.args)) - - log.log('Using M = '+', '.join(map(str,np.array(M_annot)))) - #log.log('Using M = '+jk.kill_brackets(str(np.array(M_annot))).replace(' ','') ) - return(M_annot, ref_ldscores) - - def _merge_sumstats_ld(self, args, log, sumstats, M_annot, ref_ldscores, w_ldscores): - '''Merges summary statistics and LD into one data frame''' - sumstats = smart_merge(ref_ldscores, sumstats) - if len(sumstats) == 0: - raise ValueError('No SNPs remain after merging with reference panel LD') - else: - log_msg = 'After merging with reference panel LD, {N} SNPs remain.' - log.log(log_msg.format(N=len(sumstats))) - - # merge with regression SNP LD Scores - sumstats = smart_merge(sumstats, w_ldscores) - if len(sumstats) <= 1: - raise ValueError('No SNPs remain after merging with regression SNP LD') - else: - log_msg = 'After merging with regression SNP LD, {N} SNPs remain.' - log.log(log_msg.format(N=len(sumstats))) - - w_ld_colname = sumstats.columns[-1] - ref_ld_colnames = ref_ldscores.columns[1:len(ref_ldscores.columns)] - - return(w_ld_colname, ref_ld_colnames, sumstats) - - def _filter(self, args, log, sumstats): - raise NotImplementedError - # TODO -- get this working again - err_msg = 'No SNPs retained for analysis after filtering on {C} {P} {F}.' - log_msg = 'After filtering on {C} {P} {F}, {N} SNPs remain.' - loop = ['1','2'] if args.rg else [''] - var_to_arg = {'infomax': args.info_max, 'infomin': args.info_min, 'maf': args.maf} - var_to_cname = {'infomax': 'INFO', 'infomin': 'INFO', 'maf': 'MAF'} - var_to_pred = {'infomax': lambda x: x < args.info_max, - 'infomin': lambda x: x > args.info_min, - 'maf': lambda x: x > args.maf} - var_to_predstr = {'infomax': '<', 'infomin': '>', 'maf': '>'} - for v in var_to_arg.keys(): - arg = var_to_arg[v]; pred = var_to_pred[v]; pred_str = var_to_predstr[v] - for p in loop: - cname = var_to_cname[v] + p; - if arg is not None: - sumstats = ps.filter_df(sumstats, cname, pred) - snp_count = len(sumstats) - if snp_count == 0: - raise ValueError(err_msg.format(C=cname, F=arg, P=pred_str)) - else: - log.log(log_msg.format(C=cname, F=arg, N=snp_count, P=pred_str)) - - def _warn_length(self, log, sumstats): - if len(sumstats) < 200000: - log.log('WARNING: number of SNPs less than 200k; this is almost always bad.') - - def _print_cov(self, args, log, hsqhat, n_annot, ofh=None): - '''Prints covariance matrix of slopes''' - if not args.human_only and n_annot > 1: - if not ofh: ofh = args.out+'.hsq_cov' - log.log('Printing covariance matrix of the estimates to {F}'.format(F=ofh)) - np.savetxt(ofh, hsqhat.cat_hsq_cov) - - def _print_gencov_cov(self, args, log, gencovhat, n_annot, ofh=None): - '''Prints covariance matrix of slopes''' - if not args.human_only and n_annot > 1: - if not ofh: ofh = args.out+'.gencov_cov' - log.log('Printing covariance matrix of the estimates to {F}'.format(F=ofh)) - np.savetxt(ofh, gencovhat.gencov_cov) - - def _print_delete_k(self, args, log, hsqhat): - '''Prints block jackknife delete-k values''' - if args.print_delete_vals: - ofh = args.out+'.delete_k' - log.log('Printing block jackknife delete-k values to {F}'.format(F=ofh)) - out_mat = hsqhat._jknife.delete_values - if hsqhat.constrain_intercept is None: - ncol = out_mat.shape[1] - out_mat = out_mat[:,0:ncol-1] - - np.savetxt(ofh, out_mat) - - def _masthead_and_time(self, args, header): - self.log = Logger(args.out + ".log") - if header: - self.log.log(header) - - self.log.log('Beginning analysis at {T}'.format(T=time.ctime())) - self.start_time = time.time() - - def _print_end_time(self, args, log): - log.log('Analysis finished at {T}'.format(T=time.ctime()) ) - time_elapsed = round(time.time()-self.start_time,2) - log.log('Total time elapsed: {T}'.format(T=sec_to_str(time_elapsed))) - - def _overlap_output(self, args, overlap_matrix, M_annot, n_annot, hsqhat, category_names, M_tot): - - for i in range(n_annot): - overlap_matrix[i,:] = overlap_matrix[i,:]/M_annot - - prop_hsq_overlap = np.dot(overlap_matrix,hsqhat.prop_hsq.T).reshape((1,n_annot)) - prop_hsq_overlap_var = np.diag(np.dot(np.dot(overlap_matrix,hsqhat.prop_hsq_cov),overlap_matrix.T)) - prop_hsq_overlap_se = np.sqrt(prop_hsq_overlap_var).reshape((1,n_annot)) - - one_d_convert = lambda x : np.array(x)[0] - - prop_M_overlap = M_annot/M_tot - enrichment = prop_hsq_overlap/prop_M_overlap - enrichment_se = prop_hsq_overlap_se/prop_M_overlap - enrichment_p = stats.chi2.sf(one_d_convert((enrichment-1)/enrichment_se)**2, 1) - - df = pd.DataFrame({ - 'Category':category_names, - 'Prop._SNPs':one_d_convert(prop_M_overlap), - 'Prop._h2':one_d_convert(prop_hsq_overlap), - 'Prop._h2_std_error': one_d_convert(prop_hsq_overlap_se), - 'Enrichment': one_d_convert(enrichment), - 'Enrichment_std_error': one_d_convert(enrichment_se), - 'Enrichment_p': enrichment_p - }) - df = df[['Category','Prop._SNPs','Prop._h2','Prop._h2_std_error','Enrichment','Enrichment_std_error','Enrichment_p']] - if args.print_coefficients: - df['Coefficient'] = one_d_convert(hsqhat.coef) - df['Coefficient_std_error'] = hsqhat.coef_se - df['Coefficient_z-score'] = one_d_convert(hsqhat.coef/hsqhat.coef_se) - - df = df[np.logical_not(df['Prop._SNPs'] > .9999)] - df.to_csv(args.out+'.results',sep="\t",index=False) - -class H2(_sumstats): - ''' - Implements h2 and partitioned h2 estimation - ''' - def __init__(self, args, header): - - self._masthead_and_time(args, header) - # WARNING: sumstats contains NA values to speed up merge - - if args.overlap_annot: - [overlap_matrix, M_tot] = self._read_annot(args,self.log) - - #check that M_annot == np.sum(annot_matrix,axis=0) and n_annot == annot_matrix.shape[1] - # make --overlap-annot versions of __keep_ld and _check_variance - - sumstats = self._parse_sumstats(args, self.log, args.h2, keep_na=True) - ref_ldscores = self._read_ref_ld(args, self.log) - M_annot = self._read_M(args, self.log) - #update the next three functions - M_annot, ref_ldscores = self._keep_ld(args, self.log, M_annot, ref_ldscores) - M_annot, ref_ldscores = self._check_variance(self.log, M_annot, ref_ldscores) - w_ldscores = self._read_w_ld(args, self.log) - w_ld_colname, ref_ld_colnames, self.sumstats =\ - self._merge_sumstats_ld(args, self.log, sumstats, M_annot, ref_ldscores, w_ldscores) - del sumstats - - # Remove NA values from sumstats - ii = self.sumstats.CHISQ.notnull() - self.log.log('{N} SNPs with nonmissing values.'.format(N=ii.sum())) - self.sumstats = self.sumstats[ii] - self._check_ld_condnum(args, self.log, M_annot, self.sumstats[ref_ld_colnames]) - self._warn_length(self.log, self.sumstats) - self.sumstats = self._filter_chisq(args, self.log, self.sumstats, 0.001) - self.log.log('Estimating heritability.') - snp_count = len(self.sumstats); n_annot = len(ref_ld_colnames) - if snp_count < args.num_blocks: - args.num_blocks = snp_count - - log_msg = 'Estimating standard errors using a block jackknife with {N} blocks.' - self.log.log(log_msg.format(N=args.num_blocks)) - ref_ld = np.matrix(self.sumstats[ref_ld_colnames]).reshape((snp_count, n_annot)) - w_ld = np.matrix(self.sumstats[w_ld_colname]).reshape((snp_count, 1)) - M_annot = np.matrix(M_annot).reshape((1,n_annot)) - chisq = np.matrix(self.sumstats.CHISQ).reshape((snp_count, 1)) - N = np.matrix(self.sumstats.N).reshape((snp_count,1)) - - if args.no_intercept: - args.constrain_intercept = 1 - - if args.constrain_intercept: - try: - intercept = float(args.constrain_intercept) - except Exception as e: - err_type = type(e).__name__ - e = ' '.join([str(x) for x in e.args]) - e = err_type+': '+e - msg = 'Could not cast argument to --constrain-intercept to float.\n '+e - self.log.log('ValueError: '+msg) - raise ValueError(msg) - - self.log.log('Constraining LD Score regression intercept = {C}.'.format(C=intercept)) - hsqhat = jk.Hsq(chisq, ref_ld, w_ld, N, M_annot, args.num_blocks, - args.non_negative, intercept) - - elif args.aggregate: - if args.annot: - annot = ps.AnnotFile(args.annot) - num_annots,ma = len(annot.df.columns) - 4, len(annot.df) - self.log.log("Read {A} annotations for {M} SNPs from {f}.".format(f=args.annot, - A=num_annots, M=ma)) - annot_matrix = np.matrix(annot.df.iloc[:,4:]) - else: - raise ValueError("No annot file specified.") - - hsqhat = jk.Hsq_aggregate(chisq, ref_ld, w_ld, N, M_annot, annot_matrix, args.num_blocks) - else: - hsqhat = jk.Hsq(chisq, ref_ld, w_ld, N, M_annot, args.num_blocks, args.non_negative, - slow=args.slow) - - self._print_cov(args, self.log, hsqhat, n_annot) - self._print_delete_k(args, self.log, hsqhat) - - if args.overlap_annot: - self._overlap_output(args, overlap_matrix, M_annot, n_annot, hsqhat, ref_ld_colnames, M_tot) - - self.log.log(hsqhat.summary(ref_ld_colnames, args.overlap_annot, args.out)) - self.M_annot = M_annot - self.hsqhat = hsqhat - self.log.log('\n') - self._print_end_time(args, self.log) - - def _filter_chisq(self, args, log, sumstats, N_factor): - max_N = np.max(sumstats['N']) - if not args.no_filter_chisq: - if args.max_chisq is None: - max_chisq_min = 80 - max_chisq = max(N_factor*max_N, max_chisq_min) - else: - max_chisq = args.max_chisq - sumstats = sumstats[sumstats['CHISQ'] < max_chisq] - log_msg = 'After filtering on chi^2 < {C}, {N} SNPs remain.' - snp_count = len(sumstats) - if snp_count == 0: - raise ValueError(log_msg.format(C=max_chisq, N='no')) - else: - log.log(log_msg.format(C=max_chisq, N=snp_count)) - - return sumstats - - -class Intercept(H2): - ''' - LD Score regression intercept - ''' - def __init__(self, args, header): - self._masthead_and_time(args, header) - # WARNING: sumstats contains NA values to speed up merge - sumstats = self._parse_sumstats(args, self.log, args.intercept, keep_na=True) - ref_ldscores = self._read_ref_ld(args, self.log) - M_annot = self._read_M(args, self.log) - M_annot, ref_ldscores = self._keep_ld(args, self.log, M_annot, ref_ldscores) - M_annot, ref_ldscores = self._check_variance(self.log, M_annot, ref_ldscores) - w_ldscores = self._read_w_ld(args, self.log) - w_ld_colname, ref_ld_colnames, self.sumstats =\ - self._merge_sumstats_ld(args, self.log, sumstats, M_annot, ref_ldscores, w_ldscores) - del sumstats - # Remove NA values from sumstats - ii = self.sumstats.CHISQ.notnull() - self.log.log('{N} SNPs with nonmissing values.'.format(N=ii.sum())) - self.sumstats = self.sumstats[ii] - self._check_ld_condnum(args, self.log, M_annot, self.sumstats[ref_ld_colnames]) - self._warn_length(self.log, self.sumstats) - self.sumstats = self._filter_chisq(args, self.log, self.sumstats, 0.001) - self.log.log('Estimating LD Score regression intercept.') - # filter out large-effect loci - snp_count = len(self.sumstats); n_annot = len(ref_ld_colnames) - if snp_count < args.num_blocks: - args.num_blocks = snp_count - - log_msg = 'Estimating standard errors using a block jackknife with {N} blocks.' - self.log.log(log_msg.format(N=args.num_blocks)) - ref_ld = np.matrix(self.sumstats[ref_ld_colnames]).reshape((snp_count, n_annot)) - w_ld = np.matrix(self.sumstats[w_ld_colname]).reshape((snp_count, 1)) - M_annot = np.matrix(M_annot).reshape((1, n_annot)) - chisq = np.matrix(self.sumstats.CHISQ).reshape((snp_count, 1)) - N = np.matrix(self.sumstats.N).reshape((snp_count,1)) - hsqhat = jk.Hsq(chisq, ref_ld, w_ld, N, M_annot, args.num_blocks, slow=args.slow) - self.log.log(hsqhat.summary_intercept()) - self.hsqhat = hsqhat - self._print_end_time(args, self.log) - - -class Rg(_sumstats): - ''' - Implements rg estimation with fixed LD Scores, one fixed phenotype, and a loop over - a list (possibly with length one) of other phenotypes. - - ''' - - def __init__(self, args, header): - self._masthead_and_time(args, header) - if args.rg: - rg_file_list = args.rg.split(',') - elif args.rg_list: - rg_file_list = [x.rstrip('\n') for x in open(args.rg_list,'r').readlines()] - self.log.log('The following files are specified by --rg-list:') - self.log.log('\n'.join(rg_file_list)) - self.log.log('\n') - - rg_suffix_list = [x.split('/')[-1] for x in rg_file_list] - if len(rg_file_list) < 2: - raise ValueError('Must specify at least two phenotypes for rg estimation.') - pheno1 = rg_file_list[0] - out_prefix = args.out + rg_suffix_list[0] - sumstats = self._parse_sumstats(args, self.log, pheno1, require_alleles=True, - keep_na=True) - ref_ldscores = self._read_ref_ld(args, self.log) - M_annot = self._read_M(args, self.log) - M_annot, ref_ldscores = self._keep_ld(args, self.log, M_annot, ref_ldscores) - M_annot, ref_ldscores = self._check_variance(self.log, M_annot, ref_ldscores) - w_ldscores = self._read_w_ld(args, self.log) - w_ld_colname, ref_ld_colnames, self.sumstats =\ - self._merge_sumstats_ld(args, self.log, sumstats, M_annot, ref_ldscores, w_ldscores) - self.M_annot = M_annot - self.rghat = [] - self.rghat_se = [] - self.Z = [] - self.P = [] - for i,pheno2 in enumerate(rg_file_list[1:len(rg_file_list)]): - if len(rg_file_list) > 2: - log_msg = 'Computing genetic correlation for phenotype {I}/{N}' - self.log.log(log_msg.format(I=i+2, N=len(rg_file_list))) - else: - self.log.log('Computing genetic correlation.') - try: - sumstats2 = self._parse_sumstats(args, self.log, pheno2, require_alleles=True, - keep_na=True) - out_prefix_loop = out_prefix + '_' + rg_suffix_list[i+1] - sumstats_loop = self._merge_sumstats_sumstats(args, self.sumstats, sumstats2, self.log) - # missing data has now been removed - sumstats_loop = self._filter_chisq(args, self.log, sumstats_loop, 0.001) - self._check_ld_condnum(args, self.log, M_annot, sumstats_loop[ref_ld_colnames]) - self._warn_length(self.log, sumstats_loop) - snp_count = len(sumstats_loop); n_annot = len(ref_ld_colnames) - #if i == 0: - # rghat = self._rg(sumstats_loop, args, self.log, M_annot, ref_ld_colnames, - # w_ld_colname) - # hsq1 = rghat.hsq1 - #else: - # rghat = self._rg(sumstats_loop, args, self.log, M_annot, ref_ld_colnames, - # w_ld_colname, first_hsq=hsq1) - - # estimate hsq1 each time so that the jackknife blocks use the same SNPs - # this is clearly not optimal w.r.t. runtime - rghat = self._rg(sumstats_loop, args, self.log, M_annot, ref_ld_colnames, - w_ld_colname) - if not args.human_only and n_annot > 1: - gencov_jknife_ofh = out_prefix_loop+'.gencov.cov' - hsq1_jknife_ofh = out_prefix_loop+'.hsq1.cov' - hsq2_jknife_ofh = out_prefix_loop+'.hsq2.cov' - self._print_cov(args, self.log, rghat.hsq1, n_annot, hsq1_jknife_ofh) - self._print_cov(args, self.log, rghat.hsq2, n_annot, hsq2_jknife_ofh) - self._print_gencov_cov(args, self.log, rghat.gencov, n_annot, gencov_jknife_ofh) - - if args.print_delete_vals: - hsq1_delete_ofh = out_prefix_loop+'.hsq1.delete_k' - self._print_delete_k(rghat.hsq1, hsq1_delete_ofh, self.log) - hsq2_delete_ofh = out_prefix_loop+'.hsq2.delete_k' - self._print_delete_k(rghat.hsq2, hsq2_delete_ofh, self.log) - gencov_delete_ofh = out_prefix_loop+'.gencov.delete_k' - self._print_delete_k(rghat.gencov, gencov_delete_ofh, self.log) - - self._print_gencor(args, self.log, rghat, ref_ld_colnames, i, rg_file_list, i==0) - self.rghat.append(rghat.tot_gencor) - self.rghat_se.append(rghat.tot_gencor_se) - self.Z.append(rghat.Z) - self.P.append(rghat.P_val) - - except Exception as e: - ''' - Better to print an error message then keep going if phenotype 50/100 causes an - error but the other 99/100 phenotypes are OK - - ''' - - msg = 'ERROR computing rg for phenotype {I}/{N}, from file {F}.' - self.log.log(msg.format(I=i+2, N=len(rg_file_list), F=rg_file_list[i+1])) - ex_type, ex, tb = sys.exc_info() - self.log.log( traceback.format_exc(ex) ) - self.log.log('\n') - self.rghat_se.append('NA') - self.rghat.append('NA') - self.Z.append('NA') - self.P.append('NA') - - self.log.log('Summary of Genetic Correlation Results') - - x = pd.DataFrame({ - 'p1': [rg_file_list[0] for i in xrange(1,len(rg_file_list))], - 'p2': rg_file_list[1:len(rg_file_list)], - 'rg': self.rghat, - 'se': self.rghat_se, - 'z': self.Z, - 'p': self.P - }).to_string(header=True, index=False) - - self.log.log( x ) - self.log.log( '\n' ) - self._print_end_time(args, self.log) - - def _print_gencor(self, args, log, rghat, ref_ld_colnames,i, rg_file_list, print_hsq1): - if print_hsq1: - self.log.log( '\n' ) - self.log.log( 'Heritability of phenotype 1' ) - self.log.log( '---------------------------' ) - self.log.log(rghat.hsq1.summary(ref_ld_colnames, args.overlap_annot)) - - self.log.log( '\n' ) - msg = 'Heritability of phenotype {I}/{N}'.format(I=i+2, N=len(rg_file_list)) - self.log.log(msg) - self.log.log( ''.join(['-' for i in xrange(len(msg)) ] )) - self.log.log(rghat.hsq2.summary(ref_ld_colnames, args.overlap_annot)) - self.log.log( '\n' ) - self.log.log( 'Genetic Covariance' ) - self.log.log( '------------------' ) - self.log.log(rghat.gencov.summary(ref_ld_colnames, args.overlap_annot)) - self.log.log( '\n' ) - self.log.log( 'Genetic Correlation' ) - self.log.log( '-------------------' ) - self.log.log(rghat.summary() ) - self.log.log( '\n' ) - - def _merge_sumstats_sumstats(self, args, sumstats1, sumstats2, log): - ''' - Merge two sets of summary statistics and align strand + reference alleles. - - This function filters out NA's - - ''' - # rename and merge (ideally just concatenate sideways) - sumstats2.rename(columns={'INC_ALLELE': 'INC_ALLELE2', - 'DEC_ALLELE': 'DEC_ALLELE2', 'N': 'N2', 'CHISQ': 'BETAHAT2'}, inplace=True) - if 'BETAHAT1' not in sumstats1.columns: - sumstats1.rename(columns={'CHISQ': 'BETAHAT1', 'N': 'N1'}, inplace=True) - sumstats1['BETAHAT1'] = np.sqrt(sumstats1['BETAHAT1']/sumstats1['N1']) - - x = smart_merge(sumstats1, sumstats2) - if len(x) == 0: - raise ValueError('No SNPs remain after merge.') - # remove NA's - x['BETAHAT2'] = np.sqrt(x['BETAHAT2']/x['N2']) - ii = x.BETAHAT1.notnull() & x.BETAHAT2.notnull() - self.log.log('{N} SNPs with nonmissing values.'.format(N=ii.sum())) - x = x[ii] - if len(x) == 0: - raise ValueError('All remaining SNPs have null betahat.') - - if args.no_check: - # remove strand ambiguous SNPs - strand1 = (x.INC_ALLELE+x.DEC_ALLELE).apply(lambda y: STRAND_AMBIGUOUS[y]) - strand2 = (x.INC_ALLELE2+x.DEC_ALLELE2).apply(lambda y: STRAND_AMBIGUOUS[y]) - ii = ~(strand1 | strand2) - x = x[~(strand1 | strand2)] - if len(x) == 0: - raise ValueError('All remaining SNPs are strand ambiguous') - else: - msg = 'After removing strand ambiguous SNPs, {N} SNPs remain.' - log.log(msg.format(N=len(x))) - - # remove SNPs where the alleles do not match - if not args.no_check_alleles: - alleles = x.INC_ALLELE+x.DEC_ALLELE+x.INC_ALLELE2+x.DEC_ALLELE2 - match = alleles.apply(lambda y: MATCH_ALLELES[y]) - x = x[match] - if len(x) == 0: - raise ValueError('All SNPs have mismatched alleles.') - else: - msg = 'After removing SNPs with mismatched alleles, {N} SNPs remain.' - log.log(msg.format(N=len(x))) - - # flip sign of betahat where ref alleles differ - alleles = x.INC_ALLELE+x.DEC_ALLELE+x.INC_ALLELE2+x.DEC_ALLELE2 - flip = (-1)**alleles.apply(lambda y: FLIP_ALLELES[y]) - x['BETAHAT2'] *= flip - del x['INC_ALLELE']; del x['DEC_ALLELE']; del x['INC_ALLELE2']; del x['DEC_ALLELE2'] - - return x - - def _rg(self, sumstats_loop, args, log, M_annot, ref_ld_colnames, w_ld_colname, first_hsq=None): - self.log.log('Estimating genetic correlation.') - snp_count = len(sumstats_loop); n_annot = len(ref_ld_colnames) - if snp_count < args.num_blocks: - num_blocks = snp_count - else: - num_blocks = args.num_blocks - - self.log.log('Estimating standard errors using a block jackknife with {N} blocks.'.format(N=num_blocks)) - ref_ld = np.matrix(sumstats_loop[ref_ld_colnames]).reshape((snp_count, n_annot)) - w_ld = np.matrix(sumstats_loop[w_ld_colname]).reshape((snp_count, 1)) - M_annot = np.matrix(M_annot).reshape((1, n_annot)) - betahat1 = np.matrix(sumstats_loop.BETAHAT1).reshape((snp_count, 1)) - betahat2 = np.matrix(sumstats_loop.BETAHAT2).reshape((snp_count, 1)) - N1 = np.matrix(sumstats_loop.N1).reshape((snp_count,1)) - N2 = np.matrix(sumstats_loop.N2).reshape((snp_count,1)) - del sumstats_loop - - if args.no_intercept: - args.constrain_intercept = "1,1,0" - - if args.constrain_intercept: - intercepts = args.constrain_intercept.split(',') - if len(intercepts) != 3: - msg = 'If using --constrain-intercept with --sumstats_loop-gencor, must specify a ' - msg += 'comma-separated list of three intercepts. ' - msg += 'The first two for the h2 estimates; the third for the gencov estimate.' - self.log.log('ValueError: '+msg) - raise ValueError(msg) - - try: - intercepts = [float(x) for x in intercepts] - except Exception as e: - err_type = type(e).__name__ - e = ' '.join([str(x) for x in e.args]) - e = err_type+': '+e - msg = 'Could not coerce arguments to --constrain-intercept to floats.\n '+e - self.log.log('ValueError: '+msg) - raise ValueError(msg) - - self.log.log('Constraining intercept for first h2 estimate to {I}'.format(I=str(intercepts[0]))) - self.log.log('Constraining intercept for second h2 estimate to {I}'.format(I=str(intercepts[1]))) - self.log.log('Constraining intercept for gencov estimate to {I}'.format(I=str(intercepts[2]))) - - else: - intercepts = [None, None, None] - if first_hsq is None: - rghat = jk.Gencor(betahat1, betahat2, ref_ld, w_ld, N1, N2, M_annot, intercepts, - args.overlap, args.rho, num_blocks, return_silly_things=args.return_silly_things, - slow=args.slow) - else: - rghat = jk.Gencor(betahat1, betahat2, ref_ld, w_ld, N1, N2, M_annot, intercepts, - args.overlap, args.rho, num_blocks, return_silly_things=args.return_silly_things, - first_hsq=first_hsq, slow=args.slow) - - return rghat - - def _filter_chisq(self, args, log, sumstats, N_factor): - if not args.no_filter_chisq: - max_N1 = np.max(sumstats['N1']) - max_N2 = np.max(sumstats['N2']) - if args.max_chisq is None: - max_chisq_min = 80 - max_chisq1 = max(N_factor*max_N1, max_chisq_min) - max_chisq2 = max(N_factor*max_N2, max_chisq_min) - else: - max_chisq1, max_chisq2 = args.max_chisq, args.max_chisq - - bound1 = np.sqrt(max_chisq1 / max_N1) - bound2 = np.sqrt(max_chisq2 / max_N2) - # betahat should be in units of standard deviation - sumstats = sumstats[(sumstats.BETAHAT1.abs() < bound1) & (sumstats.BETAHAT2.abs() < bound2)] - if len(sumstats) > 0: - log_msg = 'After filtering on chi^2 < ({B1},{B2}), {N} SNPs remain.' - log.log(log_msg.format(N=len(sumstats), B1=round(max_chisq1,2), B2=round(max_chisq2,2))) - else: - raise ValueError('After filtering on chi^2, no SNPs remain.') - - return sumstats + '''Check if SNP columns are equal. If so, save time by using concat instead of merge.''' + if len(x) == len(y) and (x.SNP == y.SNP).all(): + x = x.reset_index(drop=True) + y = y.reset_index(drop=True).drop('SNP', 1) + out = pd.concat([x, y], axis=1) + else: + out = pd.merge(x, y, how='inner', on='SNP') + return out + + +def _read_ref_ld(args, log): + '''Read reference LD Scores.''' + ref_ld = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, + 'reference panel LD Score', ps.ldscore_fromlist) + log.log( + 'Read reference panel LD Scores for {N} SNPs.'.format(N=len(ref_ld))) + return ref_ld + + +def _read_annot(args, log): + '''Read annot matrix.''' + try: + if args.ref_ld is not None: + overlap_matrix, M_tot = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, + 'annot matrix', ps.annot, frqfile=args.frqfile) + elif args.ref_ld_chr is not None: + overlap_matrix, M_tot = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, + 'annot matrix', ps.annot, frqfile=args.frqfile_chr) + except Exception: + log.log('Error parsing .annot file.') + raise + + return overlap_matrix, M_tot + + +def _read_M(args, log, n_annot): + '''Read M (--M, --M-file, etc).''' + if args.M: + try: + M_annot = [float(x) for x in args.M.split(',')] + except ValueError as e: + raise ValueError('Could not cast --M to float: ' + str(e.args)) + else: + if args.ref_ld: + M_annot = ps.M_fromlist( + args.ref_ld.split(','), common=(not args.not_M_5_50)) + elif args.ref_ld_chr: + M_annot = ps.M_fromlist( + args.ref_ld_chr.split(','), _N_CHR, common=(not args.not_M_5_50)) + + try: + M_annot = np.array(M_annot).reshape((1, n_annot)) + except ValueError as e: + raise ValueError( + '# terms in --M must match # of LD Scores in --ref-ld.\n' + str(e.args)) + + return M_annot + + +def _read_w_ld(args, log): + '''Read regression SNP LD.''' + if (args.w_ld and ',' in args.w_ld) or (args.w_ld_chr and ',' in args.w_ld_chr): + raise ValueError( + '--w-ld must point to a single fileset (no commas allowed).') + w_ld = _read_chr_split_files(args.w_ld_chr, args.w_ld, log, + 'regression weight LD Score', ps.ldscore_fromlist) + if len(w_ld.columns) != 2: + raise ValueError('--w-ld may only have one LD Score column.') + w_ld.columns = ['SNP', 'LD_weights'] # prevent colname conflicts w/ ref ld + log.log( + 'Read regression weight LD Scores for {N} SNPs.'.format(N=len(w_ld))) + return w_ld + + +def _read_chr_split_files(chr_arg, not_chr_arg, log, noun, parsefunc, **kwargs): + '''Read files split across 22 chromosomes (annot, ref_ld, w_ld).''' + try: + if not_chr_arg: + log.log('Reading {N} from {F} ...'.format(F=not_chr_arg, N=noun)) + out = parsefunc(not_chr_arg.split(','), **kwargs) + elif chr_arg: + f = ps.sub_chr(chr_arg, '[1-22]') + log.log('Reading {N} from {F} ...'.format(F=f, N=noun)) + out = parsefunc(chr_arg.split(','), _N_CHR, **kwargs) + except ValueError as e: + log.log('Error parsing {N}.'.format(N=noun)) + raise e + + return out + + +def _read_sumstats(args, log, fh, alleles=False, dropna=False): + '''Parse summary statistics.''' + log.log('Reading summary statistics from {S} ...'.format(S=fh)) + sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna) + log_msg = 'Read summary statistics for {N} SNPs.' + log.log(log_msg.format(N=len(sumstats))) + m = len(sumstats) + sumstats = sumstats.drop_duplicates(subset='SNP') + if m > len(sumstats): + log.log( + 'Dropped {M} SNPs with duplicated rs numbers.'.format(M=m - len(sumstats))) + + return sumstats + + +def _check_ld_condnum(args, log, ref_ld): + '''Check condition number of LD Score matrix.''' + if len(ref_ld.shape) >= 2: + cond_num = int(np.linalg.cond(ref_ld)) + if cond_num > 100000: + if args.invert_anyway: + warn = "WARNING: LD Score matrix condition number is {C}. " + warn += "Inverting anyway because the --invert-anyway flag is set." + log.log(warn.format(C=cond_num)) + else: + warn = "WARNING: LD Score matrix condition number is {C}. " + warn += "Remove collinear LD Scores. " + raise ValueError(warn.format(C=cond_num)) + + +def _check_variance(log, M_annot, ref_ld): + '''Remove zero-variance LD Scores.''' + # TODO is there a SNP column here? + ii = ref_ld.var(axis=0) == 0 + if ii.all(): + raise ValueError('All LD Scores have zero variance.') + elif ii.any(): + log.log('Removing partitioned LD Scores with zero variance.') + ref_ld = ref_ld.ix[:, ~ii] + M_annot = M_annot[:, np.array(~ii)] + + return M_annot, ref_ld, ii + + +def _warn_length(log, sumstats): + if len(sumstats) < 200000: + log.log( + 'WARNING: number of SNPs less than 200k; this is almost always bad.') + + +def _print_cov(ldscore_reg, ofh, log): + '''Prints covariance matrix of slopes.''' + log.log( + 'Printing covariance matrix of the estimates to {F}.'.format(F=ofh)) + np.savetxt(ofh, ldscore_reg.cat_cov) + + +def _print_delete_values(ldscore_reg, ofh, log): + '''Prints block jackknife delete-k values''' + log.log('Printing block jackknife delete values to {F}.'.format(F=ofh)) + np.savetxt(ofh, ldscore_reg.tot_delete_values) + + +def _merge_and_log(ld, sumstats, noun, log): + '''Wrap smart merge with log messages about # of SNPs.''' + sumstats = smart_merge(ld, sumstats) + msg = 'After merging with {F}, {N} SNPs remain.' + if len(sumstats) == 0: + raise ValueError(msg.format(N=len(sumstats), F=noun)) + else: + log.log(msg.format(N=len(sumstats), F=noun)) + + return sumstats + + +def _read_ld_sumstats(args, log, fh, alleles=False, dropna=True): + sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna) + ref_ld = _read_ref_ld(args, log) + n_annot = len(ref_ld.columns) - 1 + M_annot = _read_M(args, log, n_annot) + M_annot, ref_ld, novar_cols = _check_variance(log, M_annot, ref_ld) + w_ld = _read_w_ld(args, log) + sumstats = _merge_and_log(ref_ld, sumstats, 'reference panel LD', log) + sumstats = _merge_and_log(sumstats, w_ld, 'regression SNP LD', log) + w_ld_cname = sumstats.columns[-1] + ref_ld_cnames = ref_ld.columns[1:len(ref_ld.columns)] + return M_annot, w_ld_cname, ref_ld_cnames, sumstats, novar_cols + + +def estimate_h2(args, log): + '''Estimate h2 and partitioned h2.''' + args = copy.deepcopy(args) + if args.samp_prev is not None and args.pop_prev is not None: + args.samp_prev, args.pop_prev = map( + float, [args.samp_prev, args.pop_prev]) + if args.intercept_h2 is not None: + args.intercept_h2 = float(args.intercept_h2) + if args.no_intercept: + args.intercept_h2 = 1 + M_annot, w_ld_cname, ref_ld_cnames, sumstats, novar_cols = _read_ld_sumstats( + args, log, args.h2) + ref_ld = np.array(sumstats[ref_ld_cnames]) + _check_ld_condnum(args, log, ref_ld_cnames) + _warn_length(log, sumstats) + n_snp = len(sumstats) + n_blocks = min(n_snp, args.n_blocks) + n_annot = len(ref_ld_cnames) + chisq_max = args.chisq_max + old_weights = False + if n_annot == 1: + if args.two_step is None and args.intercept_h2 is None: + args.two_step = 30 + else: + old_weights = True + if args.chisq_max is None: + chisq_max = max(0.001*sumstats.N.max(), 80) + + s = lambda x: np.array(x).reshape((n_snp, 1)) + chisq = s(sumstats.Z**2) + if chisq_max is not None: + ii = chisq < chisq_max + sumstats = sumstats[ii] + log.log('Removed {M} SNPs with chi^2 > {C} ({N} SNPs remain)'.format( + C=chisq_max, N=np.sum(ii), M=n_snp-np.sum(ii))) + n_snp = np.sum(ii) # lambdas are late-binding, so this works + ref_ld = np.array(sumstats[ref_ld_cnames]) + chisq = chisq[ii].reshape((n_snp, 1)) + + if args.two_step is not None: + log.log('Using two-step estimator with cutoff at {M}.'.format(M=args.two_step)) + + hsqhat = reg.Hsq(chisq, ref_ld, s(sumstats[w_ld_cname]), s(sumstats.N), + M_annot, n_blocks=n_blocks, intercept=args.intercept_h2, + twostep=args.two_step, old_weights=old_weights) + + if args.print_cov: + _print_cov(hsqhat, args.out + '.cov', log) + if args.print_delete_vals: + _print_delete_values(hsqhat, args.out + '.delete', log) + + log.log(hsqhat.summary(ref_ld_cnames, P=args.samp_prev, K=args.pop_prev)) + if args.overlap_annot: + overlap_matrix, M_tot = _read_annot(args, log) + + # overlap_matrix = overlap_matrix[np.array(~novar_cols), np.array(~novar_cols)]#np.logical_not + df_results = hsqhat._overlap_output(ref_ld_cnames, overlap_matrix, M_annot, M_tot, args.print_coefficients) + df_results.to_csv(args.out+'.results', sep="\t", index=False) + log.log('Results printed to '+args.out+'.results') + + return hsqhat + + +def estimate_rg(args, log): + '''Estimate rg between trait 1 and a list of other traits.''' + args = copy.deepcopy(args) + rg_paths, rg_files = _parse_rg(args.rg) + n_pheno = len(rg_paths) + f = lambda x: _split_or_none(x, n_pheno) + args.intercept_h2, args.intercept_gencov, args.samp_prev, args.pop_prev = map(f, + (args.intercept_h2, args.intercept_gencov, args.samp_prev, args.pop_prev)) + map(lambda x: _check_arg_len(x, n_pheno), ((args.intercept_h2, '--intercept-h2'), + (args.intercept_gencov, '--intercept-gencov'), + (args.samp_prev, '--samp-prev'), + (args.pop_prev, '--pop-prev'))) + if args.no_intercept: + args.intercept_h2 = [1 for _ in xrange(n_pheno)] + args.intercept_gencov = [0 for _ in xrange(n_pheno)] + p1 = rg_paths[0] + out_prefix = args.out + rg_files[0] + M_annot, w_ld_cname, ref_ld_cnames, sumstats, _ = _read_ld_sumstats(args, log, p1, + alleles=True, dropna=True) + RG = [] + n_annot = M_annot.shape[1] + if n_annot == 1 and args.two_step is None and args.intercept_h2 is None: + args.two_step = 30 + if args.two_step is not None: + log.log('Using two-step estimator with cutoff at {M}.'.format(M=args.two_step)) + + for i, p2 in enumerate(rg_paths[1:n_pheno]): + log.log( + 'Computing rg for phenotype {I}/{N}'.format(I=i + 2, N=len(rg_paths))) + try: + loop = _read_other_sumstats(args, log, p2, sumstats, ref_ld_cnames) + rghat = _rg(loop, args, log, M_annot, ref_ld_cnames, w_ld_cname, i) + RG.append(rghat) + _print_gencor(args, log, rghat, ref_ld_cnames, i, rg_paths, i == 0) + out_prefix_loop = out_prefix + '_' + rg_files[i + 1] + if args.print_cov: + _print_rg_cov(rghat, out_prefix_loop, log) + if args.print_delete_vals: + _print_rg_delete_values(rghat, out_prefix_loop, log) + + except Exception: # keep going if phenotype 50/100 causes an error + msg = 'ERROR computing rg for phenotype {I}/{N}, from file {F}.' + log.log(msg.format(I=i + 2, N=len(rg_paths), F=rg_paths[i + 1])) + ex_type, ex, tb = sys.exc_info() + log.log(traceback.format_exc(ex) + '\n') + if len(RG) <= i: # if exception raised before appending to RG + RG.append(None) + + log.log('\nSummary of Genetic Correlation Results\n' + + _get_rg_table(rg_paths, RG, args)) + return RG + + +def _read_other_sumstats(args, log, p2, sumstats, ref_ld_cnames): + loop = _read_sumstats(args, log, p2, alleles=True, dropna=False) + loop = _merge_sumstats_sumstats(args, sumstats, loop, log) + loop = loop.dropna(how='any') + alleles = loop.A1 + loop.A2 + loop.A1x + loop.A2x + if not args.no_check_alleles: + loop = _select_and_log(loop, _filter_alleles(alleles), log, + '{N} SNPs with valid alleles.') + + loop['Z2'] = _align_alleles(loop.Z2, alleles) + loop = loop.drop(['A1', 'A1x', 'A2', 'A2x'], axis=1) + _check_ld_condnum(args, log, loop[ref_ld_cnames]) + _warn_length(log, loop) + return loop + + +def _get_rg_table(rg_paths, RG, args): + '''Print a table of genetic correlations.''' + t = lambda attr: lambda obj: getattr(obj, attr, 'NA') + x = pd.DataFrame() + x['p1'] = [rg_paths[0] for i in xrange(1, len(rg_paths))] + x['p2'] = rg_paths[1:len(rg_paths)] + x['rg'] = map(t('rg_ratio'), RG) + x['se'] = map(lambda y: round(y, 3), map(t('rg_se'), RG)) + x['z'] = map(lambda y: round(y, 3), map(t('z'), RG)) + x['p'] = map(t('p'), RG) + if args.samp_prev is not None and args.pop_prev is not None and\ + all((i is not None for i in args.samp_prev)) and all((i is not None for it in args.pop_prev)): + c = reg.h2_obs_to_liab(1, args.samp_prev[1], args.pop_prev[1]) + x['h2_liab'] = map(lambda x: c * x, map(t('tot'), map(t('hsq2'), RG))) + x['h2_liab_se'] = map( + lambda x: c * x, map(t('tot_se'), map(t('hsq2'), RG))) + else: + x['h2_obs'] = map(t('tot'), map(t('hsq2'), RG)) + x['h2_obs_se'] = map(t('tot_se'), map(t('hsq2'), RG)) + + x['h2_int'] = map(t('intercept'), map(t('hsq2'), RG)) + x['h2_int_se'] = map(t('intercept_se'), map(t('hsq2'), RG)) + x['gcov_int'] = map(t('intercept'), map(t('gencov'), RG)) + x['gcov_int_se'] = map(t('intercept_se'), map(t('gencov'), RG)) + return x.to_string(header=True, index=False) + '\n' + + +def _print_gencor(args, log, rghat, ref_ld_cnames, i, rg_paths, print_hsq1): + l = lambda x: x + ''.join(['-' for i in range(len(x.replace('\n', '')))]) + P = [args.samp_prev[0], args.samp_prev[i + 1]] + K = [args.pop_prev[0], args.pop_prev[i + 1]] + if args.samp_prev is None and args.pop_prev is None: + args.samp_prev = [None, None] + args.pop_prev = [None, None] + if print_hsq1: + log.log(l('\nHeritability of phenotype 1\n')) + log.log(rghat.hsq1.summary(ref_ld_cnames, P=P[0], K=K[0])) + + log.log( + l('\nHeritability of phenotype {I}/{N}\n'.format(I=i + 2, N=len(rg_paths)))) + log.log(rghat.hsq2.summary(ref_ld_cnames, P=P[1], K=K[1])) + log.log(l('\nGenetic Covariance\n')) + log.log(rghat.gencov.summary(ref_ld_cnames, P=P, K=K)) + log.log(l('\nGenetic Correlation\n')) + log.log(rghat.summary() + '\n') + + +def _merge_sumstats_sumstats(args, sumstats1, sumstats2, log): + '''Merge two sets of summary statistics.''' + sumstats1.rename(columns={'N': 'N1', 'Z': 'Z1'}, inplace=True) + sumstats2.rename( + columns={'A1': 'A1x', 'A2': 'A2x', 'N': 'N2', 'Z': 'Z2'}, inplace=True) + x = _merge_and_log(sumstats1, sumstats2, 'summary statistics', log) + return x + + +def _filter_alleles(alleles): + '''Remove bad variants (mismatched alleles, non-SNPs, strand ambiguous).''' + ii = alleles.apply(lambda y: y in MATCH_ALLELES) + return ii + + +def _align_alleles(z, alleles): + '''Align Z1 and Z2 to same choice of ref allele (allowing for strand flip).''' + z *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y]) + return z + + +def _rg(sumstats, args, log, M_annot, ref_ld_cnames, w_ld_cname, i): + '''Run the regressions.''' + n_snp = len(sumstats) + s = lambda x: np.array(x).reshape((n_snp, 1)) + if args.chisq_max is not None: + ii = sumstats.Z1**2*sumstats.Z2**2 < args.chisq_max**2 + n_snp = np.sum(ii) # lambdas are late binding, so this works + sumstats = sumstats[ii] + + n_blocks = min(args.n_blocks, n_snp) + ref_ld = sumstats.as_matrix(columns=ref_ld_cnames) + intercepts = [args.intercept_h2[0], args.intercept_h2[ + i + 1], args.intercept_gencov[i + 1]] + rghat = reg.RG(s(sumstats.Z1), s(sumstats.Z2), + ref_ld, s(sumstats[w_ld_cname]), s( + sumstats.N1), s(sumstats.N2), M_annot, + intercept_hsq1=intercepts[0], intercept_hsq2=intercepts[1], + intercept_gencov=intercepts[2], n_blocks=n_blocks, twostep=args.two_step) + + return rghat + + +def _parse_rg(rg): + '''Parse args.rg.''' + rg_paths = rg.split(',') + rg_files = [x.split('/')[-1] for x in rg_paths] + if len(rg_paths) < 2: + raise ValueError( + 'Must specify at least two phenotypes for rg estimation.') + + return rg_paths, rg_files + + +def _print_rg_delete_values(rg, fh, log): + '''Print block jackknife delete values.''' + _print_delete_values(rg.hsq1, fh + '.hsq1.delete', log) + _print_delete_values(rg.hsq2, fh + '.hsq2.delete', log) + _print_delete_values(rg.gencov, fh + '.gencov.delete', log) + + +def _print_rg_cov(rghat, fh, log): + '''Print covariance matrix of estimates.''' + _print_cov(rghat.hsq1, fh + '.hsq1.cov', log) + _print_cov(rghat.hsq2, fh + '.hsq2.cov', log) + _print_cov(rghat.gencov, fh + '.gencov.cov', log) + + +def _split_or_none(x, n): + if x is not None: + y = map(float, x.replace('N', '-').split(',')) + else: + y = [None for _ in xrange(n)] + return y + + +def _check_arg_len(x, n): + x, m = x + if len(x) != n: + raise ValueError( + '{M} must have the same number of arguments as --rg/--h2.'.format(M=m)) diff --git a/munge_sumstats.py b/munge_sumstats.py new file mode 100644 index 00000000..75ca342b --- /dev/null +++ b/munge_sumstats.py @@ -0,0 +1,628 @@ +from __future__ import division +import pandas as pd +import numpy as np +import os +import sys +import traceback +import gzip +import bz2 +import argparse +from scipy.stats import chi2 +from ldscore import sumstats +from ldsc import MASTHEAD, Logger, sec_to_str +import time +np.seterr(invalid='ignore') + +null_values = { + + 'LOG_ODDS': 0, + 'BETA': 0, + 'OR': 1, + 'Z': 0 +} + +default_cnames = { + + # RS NUMBER + 'SNP': 'SNP', + 'MARKERNAME': 'SNP', + 'SNPID': 'SNP', + 'RS': 'SNP', + 'RSID': 'SNP', + 'RS_NUMBER': 'SNP', + 'RS_NUMBERS': 'SNP', + # NUMBER OF STUDIES + 'NSTUDY': 'NSTUDY', + 'N_STUDY': 'NSTUDY', + 'NSTUDIES': 'NSTUDY', + 'N_STUDIES': 'NSTUDY', + # P-VALUE + 'P': 'P', + 'PVALUE': 'P', + 'P_VALUE': 'P', + 'PVAL': 'P', + 'P_VAL': 'P', + 'GC_PVALUE': 'P', + # ALLELE 1 + 'A1': 'A1', + 'ALLELE1': 'A1', + 'ALLELE_1': 'A1', + 'EFFECT_ALLELE': 'A1', + 'REFERENCE_ALLELE': 'A1', + 'INC_ALLELE': 'A1', + 'EA': 'A1', + # ALLELE 2 + 'A2': 'A2', + 'ALLELE2': 'A2', + 'ALLELE_2': 'A2', + 'OTHER_ALLELE': 'A2', + 'NON_EFFECT_ALLELE': 'A2', + 'DEC_ALLELE': 'A2', + 'NEA': 'A2', + # N + 'N': 'N', + 'NCASE': 'N_CAS', + 'N_CASE': 'N_CAS', + 'N_CASES': 'N_CAS', + 'N_CONTROLS': 'N_CON', + 'N_CAS': 'N_CAS', + 'N_CON': 'N_CON', + 'N_CASE': 'N_CAS', + 'NCONTROL': 'N_CON', + 'N_CONTROL': 'N_CON', + 'WEIGHT': 'N', # metal does this. possibly risky. + # SIGNED STATISTICS + 'ZSCORE': 'Z', + 'Z-SCORE': 'Z', + 'GC_ZSCORE': 'Z', + 'Z': 'Z', + 'OR': 'OR', + 'BETA': 'BETA', + 'LOG_ODDS': 'LOG_ODDS', + 'EFFECTS': 'BETA', + 'EFFECT': 'BETA', + 'SIGNED_SUMSTAT': 'SIGNED_SUMSTAT', + # INFO + 'INFO': 'INFO', + # MAF + 'EAF': 'FRQ', + 'FRQ': 'FRQ', + 'MAF': 'FRQ', + 'FRQ_U': 'FRQ', + 'F_U': 'FRQ', +} + +describe_cname = { + 'SNP': 'Variant ID (e.g., rs number)', + 'P': 'p-Value', + 'A1': 'Allele 1', + 'A2': 'Allele 2', + 'N': 'Sample size', + 'N_CAS': 'Number of cases', + 'N_CON': 'Number of controls', + 'Z': 'Z-score (0 --> no effect; above 0 --> A1 is trait/risk increasing)', + 'OR': 'Odds ratio (1 --> no effect; above 1 --> A1 is risk increasing)', + 'BETA': '[linear/logistic] regression coefficient (0 --> no effect; above 0 --> A1 is trait/risk increasing)', + 'LOG_ODDS': 'Log odds ratio (0 --> no effect; above 0 --> A1 is risk increasing)', + 'INFO': 'INFO score (imputation quality; higher --> better imputation)', + 'FRQ': 'Allele frequency', + 'SIGNED_SUMSTAT': 'Directional summary statistic as specified by --signed-sumstats.', + 'NSTUDY': 'Number of studies in which the SNP was genotyped.' +} + + +def read_header(fh): + '''Read the first line of a file and returns a list with the column names.''' + (openfunc, compression) = get_compression(fh) + return [x.rstrip('\n') for x in openfunc(fh).readline().split()] + + +def get_cname_map(flag, default, ignore): + ''' + Figure out which column names to use. + + Priority is + (1) ignore everything in ignore + (2) use everything in flags that is not in ignore + (3) use everything in default that is not in ignore or in flags + + The keys of flag are cleaned. The entries of ignore are not cleaned. The keys of defualt + are cleaned. But all equality is modulo clean_header(). + + ''' + clean_ignore = [clean_header(x) for x in ignore] + cname_map = {x: flag[x] for x in flag if x not in clean_ignore} + cname_map.update({x: default[x] for x in default if x not in clean_ignore+flag.keys()}) + return cname_map + + +def get_compression(fh): + ''' + Read filename suffixes and figure out whether it is gzipped,bzip2'ed or not compressed + ''' + if fh.endswith('gz'): + compression = 'gzip' + openfunc = gzip.open + elif fh.endswith('bz2'): + compression = 'bz2' + openfunc = bz2.BZ2File + else: + openfunc = open + compression = None + + return openfunc, compression + + +def clean_header(header): + ''' + For cleaning file headers. + - convert to uppercase + - replace dashes '-' with underscores '_' + - replace dots '.' (as in R) with underscores '_' + - remove newlines ('\n') + ''' + return header.upper().replace('-', '_').replace('.', '_').replace('\n', '') + + +def filter_pvals(P, log, args): + '''Remove out-of-bounds P-values''' + ii = (P > 0) & (P <= 1) + bad_p = (~ii).sum() + if bad_p > 0: + msg = 'WARNING: {N} SNPs had P outside of (0,1]. The P column may be mislabeled.' + log.log(msg.format(N=bad_p)) + + return ii + + +def filter_info(info, log, args): + '''Remove INFO < args.info_min (default 0.9) and complain about out-of-bounds INFO.''' + if type(info) is pd.Series: # one INFO column + jj = ((info > 2.0) | (info < 0)) & info.notnull() + ii = info >= args.info_min + elif type(info) is pd.DataFrame: # several INFO columns + jj = (((info > 2.0) & info.notnull()).any(axis=1) | ((info < 0) & info.notnull()).any(axis=1)) + ii = (info.sum(axis=1) >= args.info_min*(len(info.columns))) + else: + raise ValueError('Expected pd.DataFrame or pd.Series.') + + bad_info = jj.sum() + if bad_info > 0: + msg = 'WARNING: {N} SNPs had INFO outside of [0,1.5]. The INFO column may be mislabeled.' + log.log(msg.format(N=bad_info)) + + return ii + + +def filter_frq(frq, log, args): + ''' + Filter on MAF. Remove MAF < args.maf_min and out-of-bounds MAF. + ''' + jj = (frq < 0) | (frq > 1) + bad_frq = jj.sum() + if bad_frq > 0: + msg = 'WARNING: {N} SNPs had FRQ outside of [0,1]. The FRQ column may be mislabeled.' + log.log(msg.format(N=bad_frq)) + + frq = np.minimum(frq, 1-frq) + ii = frq > args.maf_min + return ii & ~jj + + +def filter_alleles(a): + '''Remove alleles that do not describe strand-unambiguous SNPs''' + return a.isin(sumstats.VALID_SNPS) + + +def parse_dat(dat_gen, convert_colname, merge_alleles, log, args): + '''Parse and filter a sumstats file chunk-wise''' + tot_snps = 0 + dat_list = [] + msg = 'Reading sumstats from {F} into memory {N} SNPs at a time.' + log.log(msg.format(F=args.sumstats, N=args.chunksize)) + drops = {'NA': 0, 'P': 0, 'INFO': 0, 'FRQ': 0, 'A': 0, 'SNP': 0, 'MERGE': 0} + for block_num, dat in enumerate(dat_gen): + sys.stdout.write('.') + tot_snps += len(dat) + old = len(dat) + dat = dat.dropna(axis=0, how="any", subset=filter(lambda x: x != 'INFO', dat.columns)).reset_index(drop=True) + drops['NA'] += old - len(dat) + dat.columns = map(lambda x: convert_colname[x], dat.columns) + ii = np.array([True for i in xrange(len(dat))]) + if args.merge_alleles: + old = ii.sum() + ii = dat.SNP.isin(merge_alleles.SNP) + drops['MERGE'] += old - ii.sum() + if ii.sum() == 0: + continue + + dat = dat[ii].reset_index(drop=True) + ii = np.array([True for i in xrange(len(dat))]) + + if 'INFO' in dat.columns: + old = ii.sum() + ii &= filter_info(dat['INFO'], log, args) + new = ii.sum() + drops['INFO'] += old - new + old = new + + if 'FRQ' in dat.columns: + ii &= filter_frq(dat['FRQ'], log, args) + new = ii.sum() + drops['FRQ'] += old-new + old = new + + dat.drop([x for x in ['INFO', 'FRQ'] if x in dat.columns], inplace=True, axis=1) + ii &= filter_pvals(dat.P, log, args) + new = ii.sum() + drops['P'] += old-new + old = new + if not args.no_alleles: + dat.A1 = dat.A1.str.upper() + dat.A2 = dat.A2.str.upper() + ii &= filter_alleles(dat.A1+dat.A2) + drops['A'] += old-new + old = new + + if ii.sum() == 0: + continue + + dat_list.append(dat[ii].reset_index(drop=True)) + + sys.stdout.write(' done\n') + dat = pd.concat(dat_list, axis=0).reset_index(drop=True) + msg = 'Read {N} SNPs from --sumstats file.\n'.format(N=tot_snps) + if args.merge_alleles: + msg += 'Removed {N} SNPs not in --merge-alleles.\n'.format(N=drops['MERGE']) + + msg += 'Removed {N} SNPs with missing values.\n'.format(N=drops['NA']) + msg += 'Removed {N} SNPs with INFO <= {I}.\n'.format(N=drops['INFO'], I=args.info_min) + msg += 'Removed {N} SNPs with MAF <= {M}.\n'.format(N=drops['FRQ'], M=args.maf_min) + msg += 'Removed {N} SNPs with out-of-bounds p-values.\n'.format(N=drops['P']) + msg += 'Removed {N} variants that were not SNPs or were strand-ambiguous.\n'.format(N=drops['A']) + msg += '{N} SNPs remain.'.format(N=len(dat)) + log.log(msg) + return dat + + +def process_n(dat, args, log): + '''Determine sample size from --N* flags or N* columns. Filter out low N SNPs.s''' + if all(i in dat.columns for i in ['N_CAS', 'N_CON']): + N = dat.N_CAS + dat.N_CON + P = dat.N_CAS / N + dat['N'] = N * P / P[N == N.max()].mean() + dat.drop(['N_CAS', 'N_CON'], inplace=True, axis=1) + # NB no filtering on N done here -- that is done in the next code block + + if 'N' in dat.columns: + n_min = args.n_min if args.n_min else dat.N.quantile(0.9) / 1.5 + old = len(dat) + dat = dat[dat.N >= n_min].reset_index(drop=True) + new = len(dat) + log.log('Removed {M} SNPs with N < {MIN} ({N} SNPs remain).'.format(M=old-new, N=new, MIN=n_min)) + + elif 'NSTUDY' in dat.columns and 'N' not in dat.columns: + nstudy_min = args.nstudy_min if args.nstudy_min else dat.NSTUDY.max() + old = len(dat) + dat = dat[dat.NSTUDY >= nstudy_min].drop(['NSTUDY'], axis=1).reset_index(drop=True) + new = len(dat) + log.log('Removed {M} SNPs with NSTUDY < {MIN} ({N} SNPs remain).'.format(M=old-new, N=new, MIN=nstudy_min)) + + if 'N' not in dat.columns: + if args.N: + dat['N'] = args.N + log.log('Using N = {N}'.format(N=args.N)) + elif args.N_cas and args.N_con: + dat['N'] = args.N_cas + args.N_con + if args.daner is None: + msg = 'Using N_cas = {N1}; N_con = {N2}' + log.log(msg.format(N1=args.N_cas, N2=args.N_con)) + else: + raise ValueError('Cannot determine N. This message indicates a bug.\n' + 'N should have been checked earlier in the program.') + + return dat + + +def p_to_z(P, N): + '''Convert P-value and N to standardized beta.''' + return np.sqrt(chi2.isf(P, 1)) + + +def check_median(x, expected_median, tolerance, name): + '''Check that median(x) is within tolerance of expected_median.''' + m = np.median(x) + if np.abs(m-expected_median) > tolerance: + msg = 'WARNING: median value of {F} is {V} (should be close to {M}). This column may be mislabeled.' + raise ValueError(msg.format(F=name, M=expected_median, V=round(m, 2))) + else: + msg = 'Median value of {F} was {C}, which seems sensible.'.format(C=m, F=name) + + return msg + + +def parse_flag_cnames(log, args): + ''' + Parse flags that specify how to interpret nonstandard column names. + + flag_cnames is a dict that maps (cleaned) arguments to internal column names + ''' + cname_options = [ + [args.nstudy, 'NSTUDY', '--nstudy'], + [args.snp, 'SNP', '--snp'], + [args.N_col, 'N', '--N'], + [args.N_cas_col, 'N_CAS', '--N-cas-col'], + [args.N_con_col, 'N_CON', '--N-con-col'], + [args.a1, 'A1', '--a1'], + [args.a2, 'A2', '--a2'], + [args.p, 'P', '--P'], + [args.frq, 'FRQ', '--nstudy'], + [args.info, 'INFO', '--info'] + ] + flag_cnames = {clean_header(x[0]): x[1] for x in cname_options if x[0] is not None} + if args.info_list: + try: + flag_cnames.update({clean_header(x): 'INFO' for x in args.info_list.split(',')}) + except ValueError: + log.log('The argument to --info-list should be a comma-separated list of column names.') + raise + + null_value = None + if args.signed_sumstats: + try: + cname, null_value = args.signed_sumstats.split(',') + null_value = float(null_value) + flag_cnames[clean_header(cname)] = 'SIGNED_SUMSTAT' + except ValueError: + log.log('The argument to --signed-sumstats should be column header comma number.') + raise + + return [flag_cnames, null_value] + + +def allele_merge(dat, alleles, log): + ''' + WARNING: dat now contains a bunch of NA's~ + Note: dat now has the same SNPs in the same order as --merge alleles. + ''' + dat = pd.merge(alleles, dat, how='left', on='SNP', sort=False).reset_index(drop=True) + ii = dat.A1.notnull() + a1234 = dat.A1[ii] + dat.A2[ii] + dat.MA[ii] + match = a1234.apply(lambda y: y in sumstats.MATCH_ALLELES) + jj = pd.Series(np.zeros(len(dat), dtype=bool)) + jj[ii] = match + old = ii.sum() + n_mismatch = (~match).sum() + if n_mismatch < old: + log.log('Removed {M} SNPs whose alleles did not match --merge-alleles ({N} SNPs remain).'.format(M=n_mismatch, + N=old-n_mismatch)) + else: + raise ValueError('All SNPs have alleles that do not match --merge-alleles.') + + dat.loc[~jj, [i for i in dat.columns if i != 'SNP']] = float('nan') + dat.drop(['MA'], axis=1, inplace=True) + return dat + +parser = argparse.ArgumentParser() +parser.add_argument('--sumstats', default=None, type=str, + help="Input filename.") +parser.add_argument('--N', default=None, type=float, + help="Sample size If this option is not set, will try to infer the sample " + "size from the input file. If the input file contains a sample size " + "column, and this flag is set, the argument to this flag has priority.") +parser.add_argument('--N-cas', default=None, type=float, + help="Number of cases. If this option is not set, will try to infer the number " + "of cases from the input file. If the input file contains a number of cases " + "column, and this flag is set, the argument to this flag has priority.") +parser.add_argument('--N-con', default=None, type=float, + help="Number of controls. If this option is not set, will try to infer the number " + "of controls from the input file. If the input file contains a number of controls " + "column, and this flag is set, the argument to this flag has priority.") +parser.add_argument('--out', default=None, type=str, + help="Output filename prefix.") +parser.add_argument('--info-min', default=0.9, type=float, + help="Minimum INFO score.") +parser.add_argument('--maf-min', default=0.01, type=float, + help="Minimum MAF.") +parser.add_argument('--daner', default=False, action='store_true', + help="Use this flag to parse Step han Ripke's daner* file format.") +parser.add_argument('--no-alleles', default=False, action="store_true", + help="Don't require alleles. Useful if only unsigned summary statistics are available " + "and the goal is h2 / partitioned h2 estimation rather than rg estimation.") +parser.add_argument('--merge-alleles', default=None, type=str, + help="Same as --merge, except the file should have three columns: SNP, A1, A2, " + "and all alleles will be matched to the --merge-alleles file alleles.") +parser.add_argument('--n-min', default=None, type=float, + help='Minimum N (sample size). Default is (90th percentile N) / 2.') +parser.add_argument('--chunksize', default=5e6, type=int, + help='Chunksize.') + +# optional args to specify column names +parser.add_argument('--snp', default=None, type=str, + help='Name of SNP column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--N-col', default=None, type=str, + help='Name of N column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--N-cas-col', default=None, type=str, + help='Name of N column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--N-con-col', default=None, type=str, + help='Name of N column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--a1', default=None, type=str, + help='Name of A1 column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--a2', default=None, type=str, + help='Name of A2 column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--p', default=None, type=str, + help='Name of p-value column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--frq', default=None, type=str, + help='Name of FRQ or MAF column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--signed-sumstats', default=None, type=str, + help='Name of signed sumstat column, comma null value (e.g., Z,0 or OR,1). NB: case insensitive.') +parser.add_argument('--info', default=None, type=str, + help='Name of INFO column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--info-list', default=None, type=str, + help='Comma-separated list of INFO columns. Will filter on the mean. NB: case insensitive.') +parser.add_argument('--nstudy', default=None, type=str, + help='Name of NSTUDY column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--nstudy-min', default=None, type=float, + help='Minimum # of studies. Default is to remove everything below the max, unless there is an N column,' + ' in which case do nothing.') +parser.add_argument('--ignore', default=None, type=str, + help='Comma-separated list of column names to ignore.') +parser.add_argument('--a1-inc', default=False, action='store_true', + help='A1 is the increasing allele.') + + +def munge_sumstats(args, p=True): # set p = False for testing in order to prevent printing + if args.out is None: + raise ValueError('The --out flag is required.') + + START_TIME = time.time() + log = Logger(args.out + '.log') + try: + if args.sumstats is None: + raise ValueError('The --sumstats flag is required.') + if args.no_alleles and args.merge_alleles: + raise ValueError('--no-alleles and --merge-alleles are not compatible.') + + if p: + defaults = vars(parser.parse_args('')) + opts = vars(args) + non_defaults = [x for x in opts.keys() if opts[x] != defaults[x]] + header = MASTHEAD + header += "\nOptions: \n" + options = ['--'+x.replace('_', '-')+' '+str(opts[x]) for x in non_defaults] + header += '\n'.join(options).replace('True', '').replace('False', '') + header += '\n' + log.log(header) + + file_cnames = read_header(args.sumstats) # note keys not cleaned + flag_cnames, signed_sumstat_null = parse_flag_cnames(log, args) + if args.ignore: + ignore_cnames = [clean_header(x) for x in args.ignore.split(',')] + else: + ignore_cnames = [] + + if args.signed_sumstats is not None: # remove LOG_ODDS, BETA, Z, OR from the default list + mod_default_cnames = {x: default_cnames[x] for x in default_cnames if default_cnames[x] not in null_values} + else: + mod_default_cnames = default_cnames + + cname_map = get_cname_map(flag_cnames, mod_default_cnames, ignore_cnames) + if args.daner: + frq_u = filter(lambda x: x.startswith('FRQ_U_'), file_cnames)[0] + frq_a = filter(lambda x: x.startswith('FRQ_A_'), file_cnames)[0] + N_cas = float(frq_a[6:]) + N_con = float(frq_u[6:]) + log.log('Inferred that N_cas = {N1}, N_con = {N2} from the FRQ_[A/U] columns.'.format(N1=N_cas, N2=N_con)) + args.N_cas = N_cas + args.N_con = N_con + # drop any N, N_cas, N_con or FRQ columns + for c in ['N', 'N_CAS', 'N_CON', 'FRQ']: + for d in [x for x in cname_map if cname_map[x] == 'c']: + del cname_map[d] + + cname_map[frq_u] = 'FRQ' + + cname_translation = {x: cname_map[clean_header(x)] for x in file_cnames if + clean_header(x) in cname_map} # note keys not cleaned + cname_description = {x: describe_cname[cname_translation[x]] for x in cname_translation} + if args.signed_sumstats is None: + sign_cnames = [x for x in cname_translation if cname_translation[x] in null_values] + if len(sign_cnames) > 1: + raise ValueError('Too many signed sumstat columns. Specify which to ignore with the --ignore flag.') + + sign_cname = sign_cnames[0] + signed_sumstat_null = null_values[cname_translation[sign_cname]] + cname_translation[sign_cname] = 'SIGNED_SUMSTAT' + else: + sign_cname = 'SIGNED_SUMSTATS' + + # check that we have all the columns we need + for c in ['SNP', 'P', 'SIGNED_SUMSTAT']: + if c not in cname_translation.values(): + raise ValueError('Could not find {C} column.'.format(C=c)) + + if (not args.N) and (not (args.N_cas and args.N_con)) and ('N' not in cname_translation.values()) and\ + (any(x not in cname_translation.values() for x in ['N_CAS', 'N_CON'])): + raise ValueError('Could not determine N.') + if ('N' in cname_translation.values() or all(x in cname_translation.values() for x in ['N_CAS', 'N_CON']))\ + and 'NSTUDY' in cname_translation.values(): + nstudy = [x for x in cname_translation if cname_translation[x] == 'NSTUDY'] + for x in nstudy: + del cname_translation[x] + if not args.no_alleles and not all(x in cname_translation.values() for x in ['A1', 'A2']): + raise ValueError('Could not find A1/A2 columns.') + + log.log('Interpreting column names as follows:') + log.log('\n'.join([x + ':\t' + cname_description[x] for x in cname_description]) + '\n') + + if args.merge_alleles: + log.log('Reading list of SNPs for allele merge from {F}'.format(F=args.merge_alleles)) + (openfunc, compression) = get_compression(args.merge_alleles) + merge_alleles = pd.read_csv(args.merge_alleles, compression=compression, header=0, + delim_whitespace=True, na_values='.') + if any(x not in merge_alleles.columns for x in ["SNP", "A1", "A2"]): + raise ValueError('--merge-alleles must have columns SNP, A1, A2.') + + log.log('Read {N} SNPs for allele merge.'.format(N=len(merge_alleles))) + merge_alleles['MA'] = (merge_alleles.A1+merge_alleles.A2).apply(lambda y: y.upper()) + merge_alleles.drop([x for x in merge_alleles.columns if x not in ['SNP', 'MA']], axis=1, inplace=True) + else: + merge_alleles = None + + (openfunc, compression) = get_compression(args.sumstats) + dat_gen = pd.read_csv(args.sumstats, delim_whitespace=True, header=0, compression=compression, + usecols=cname_translation.keys(), na_values='.', iterator=True, chunksize=args.chunksize) + + dat = parse_dat(dat_gen, cname_translation, merge_alleles, log, args) + if len(dat) == 0: + raise ValueError('After applying filters, no SNPs remain.') + + old = len(dat) + dat = dat.drop_duplicates(subset='SNP').reset_index(drop=True) + new = len(dat) + log.log('Removed {M} SNPs with duplicated rs numbers ({N} SNPs remain).'.format(M=old-new, N=new)) + # filtering on N cannot be done chunkwise + dat = process_n(dat, args, log) + dat.P = p_to_z(dat.P, dat.N) + dat.rename(columns={'P': 'Z'}, inplace=True) + if not args.a1_inc: + log.log(check_median(dat.SIGNED_SUMSTAT, signed_sumstat_null, 0.1, sign_cname)) + dat.Z *= (-1)**(dat.SIGNED_SUMSTAT < signed_sumstat_null) + dat.drop('SIGNED_SUMSTAT', inplace=True, axis=1) + + # do this last so we don't have to worry about NA values in the rest of the program + if args.merge_alleles: + dat = allele_merge(dat, merge_alleles, log) + + out_fname = args.out+'.sumstats' + print_colnames = [c for c in dat.columns if c in ['SNP', 'N', 'Z', 'A1', 'A2']] + msg = 'Writing summary statistics for {M} SNPs ({N} with nonmissing beta) to {F}.' + log.log(msg.format(M=len(dat), F=out_fname+'.gz', N=dat.N.notnull().sum())) + if p: + dat.to_csv(out_fname, sep="\t", index=False, columns=print_colnames, float_format='%.3f') + os.system('gzip -f {F}'.format(F=out_fname)) + + log.log('\nMetadata:') + CHISQ = (dat.Z**2) + mean_chisq = CHISQ.mean() + log.log('Mean chi^2 = ' + str(round(mean_chisq, 3))) + if mean_chisq < 1.02: + log.log("WARNING: mean chi^2 may be too small.") + + log.log('Lambda GC = ' + str(round(CHISQ.median() / 0.4549, 3))) + log.log('Max chi^2 = ' + str(round(CHISQ.max(), 3))) + log.log('{N} Genome-wide significant SNPs (some may have been removed by filtering).'.format(N=(CHISQ + > 29).sum())) + return dat + + except Exception: + log.log('\nERROR converting summary statistics:\n') + ex_type, ex, tb = sys.exc_info() + log.log(traceback.format_exc(ex)) + raise + finally: + log.log('\nConversion finished at {T}'.format(T=time.ctime())) + log.log('Total time elapsed: {T}'.format(T=sec_to_str(round(time.time()-START_TIME, 2)))) + +if __name__ == '__main__': + munge_sumstats(parser.parse_args(), p=True) diff --git a/requirements.txt b/requirements.txt index aaacc971..573dc0b4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,6 @@ -argparse==1.2.1 +argparse==1.3.0 bitarray==0.8.1 -nose==1.3.1 -nose-parameterized==0.3.3 +nose==1.3.4 numpy==1.8.0 -pandas==0.15.0 -scipy==0.10.1 +pandas==0.15.2 +scipy==0.11.0 diff --git a/sumstats_to_chisq2.py b/sumstats_to_chisq2.py deleted file mode 100644 index 23dbe9a8..00000000 --- a/sumstats_to_chisq2.py +++ /dev/null @@ -1,918 +0,0 @@ -''' -Converts summary stats files to .chisq.gz and alleles files - -''' -from __future__ import division -import pandas as pd -import numpy as np -import os -import sys -import traceback -import gzip -import bz2 -import argparse -from scipy.stats import chi2 -from ldscore import sumstats -from ldscore import parse -from ldsc import MASTHEAD -import time - - -def convert_colname(cname, pre=None): - cname = clean_header(cname) - if pre is not None and cname in pre: - cname = pre[cname] - - return COLNAMES_CONVERSION[cname] - - -def clean_header(header): - ''' - For cleaning file headers. - - convert to uppercase - - replace dashes '-' with underscores '_' - - replace dots '.' (as in R) with underscores '_' - ''' - return header.upper().replace('-','_').replace('.','_') - - -def get_compression(fh): - if fh.endswith('gz'): - compression='gzip' - openfunc = gzip.open - elif fh.endswith('bz2'): - compression = 'bz2' - openfunc = bz2.BZ2File - else: - openfunc = open - compression=None - - return (openfunc, compression) - - -COL_TO_ENGLISH = { - 'SNP': 'Variant ID (e.g., rs number)', - 'P': 'p-Value', - 'A1': 'Allele 1', - 'A2': 'Allele 2', - 'N': 'Sample size', - 'N_CAS': 'Number of cases', - 'N_CON': 'Number of controls', - 'Z': 'Z-score (0 --> no effect; above 0 --> A1 is trait/risk increasing)', - 'OR': 'Odds ratio (1 --> no effect; above 1 --> A1 is risk increasing)', - 'BETA': '[linear/logistic] regression coefficient (0 --> no effect; above 0 --> A1 is trait/risk increasing)', - 'LOG_ODDS': 'Log odds ratio (0 --> no effect; above 0 --> A1 is risk increasing)', - 'INFO': 'INFO score (imputation quality; higher --> better imputation)', - 'FRQ': 'Allele frequency', - 'SIGNED_SUMSTAT': 'Directional summary statistic as specified by --signed-sumstats.', - 'NSTUDY': 'Number of studies in which the SNP was genotyped.' -} - - -COLNAMES_CONVERSION = { - # RS NUMBER - 'SNP': 'SNP', - 'MARKERNAME': 'SNP', - 'SNPID': 'SNP', - 'RS' : 'SNP', - 'RSID': 'SNP', - 'RS_NUMBER': 'SNP', - 'RS_NUMBERS': 'SNP', - - # NUMBER OF STUDIES - 'NSTUDY': 'NSTUDY', - 'N_STUDY': 'NSTUDY', - 'NSTUDIES': 'NSTUDY', - 'N_STUDIES': 'NSTUDY', - - # P-VALUE - 'P': 'P', - 'PVALUE': 'P', - 'P_VALUE': 'P', - 'PVAL' : 'P', - 'P_VAL' : 'P', - 'GC_PVALUE': 'P', - - # ALLELE 1 - 'A1': 'A1', - 'ALLELE1': 'A1', - 'ALLELE_1': 'A1', - 'EFFECT_ALLELE': 'A1', -# 'RISK_ALLELE': 'A1', - 'REFERENCE_ALLELE': 'A1', - 'INC_ALLELE': 'A1', - 'EA': 'A1', - - # ALLELE 2 - 'A2' : 'A2', - 'ALLELE2': 'A2', - 'ALLELE_2': 'A2', - 'OTHER_ALLELE' : 'A2', - 'NON_EFFECT_ALLELE' : 'A2', - 'DEC_ALLELE': 'A2', - 'NEA': 'A2', - - # N - 'N': 'N', - 'NCASE': 'N_CAS', - 'N_CASE': 'N_CAS', - 'N_CASES': 'N_CAS', - 'N_CONTROLS' : 'N_CON', - 'N_CAS': 'N_CAS', - 'N_CON' : 'N_CON', - 'N_CASE': 'N_CAS', - 'NCONTROL': 'N_CON', - 'N_CONTROL': 'N_CON', - 'WEIGHT' : 'N', # metal does this. possibly risky. - - # SIGNED STATISTICS - 'ZSCORE': 'Z', - 'Z-SCORE': 'Z', - 'GC_ZSCORE' : 'Z', - 'Z': 'Z', - 'OR': 'OR', - 'BETA': 'BETA', - 'LOG_ODDS': 'LOG_ODDS', - 'EFFECTS': 'BETA', - 'SIGNED_SUMSTAT': 'SIGNED_SUMSTAT', - - # INFO - 'INFO': 'INFO', - - # MAF - 'EAF' : 'FRQ', - 'FRQ': 'FRQ', - 'MAF': 'FRQ', - 'FRQ_U': 'FRQ', - 'F_U': 'FRQ', - #'CEUAF': 'FRQ', - #'CEU_AF': 'FRQ' -} - -def filter_verbose(old_len, new_len, phrase): - msg = 'Removed {M} SNPs with {P} ({N} SNPs remain).' - msg = msg.format(M=old_len-new_len, N=new_len, P=phrase) - if new_len == 0: - raise ValueError('No SNPs remain.') - - return msg - -def filter_na(dat, args, log, drops=None, verbose=True): - # check uniqueness of rs numbers & remove SNPs w/ rs == '.' - old_len = len(dat); dat = dat.drop_duplicates(subset='SNP'); new_len = len(dat) - if verbose: - log.log(filter_verbose(old_len, new_len, 'with duplicated rs numbers')) - if drops is not None: - drops['RS'] += old_len-new_len - - # remove NA's - subset = filter(lambda x: x != 'INFO', dat.columns) - old_len = len(dat); dat = dat.dropna(axis=0, how="any", subset=subset); new_len = len(dat) - if verbose: - log.log(filter_verbose(old_len, new_len, 'with missing values in columns other than INFO')) - if drops is not None: - drops['NA'] += old_len-new_len - - return (dat, drops) - - -def filter_snps(dat, args, log, drops=None, verbose=True): - - if dat.P.dtype != 'float': - dat.P = dat.P.astype('float') - - # filter p-vals - ii = (dat.P > 0) # bool index of SNPs to keep - jj = (dat.P <= 1) - bad_p = (~jj).sum() - if bad_p > 0: - ii = ii & jj - msg = 'WARNING: {N} SNPs had P > 1. The P column may be mislabeled.' - log.log(msg.format(N=bad_p)) - - old_len = len(dat); new_len = ii.sum() - if verbose: - log.log(filter_verbose(old_len, new_len, 'p-values outside of (0,1]')) - if drops is not None: - drops['P'] += old_len-new_len - - # filter on INFO - if 'INFO' in dat.columns: - # why does this have to be so awkward? - if type(dat.INFO) is pd.Series: # one INFO column - jj = ((dat.INFO > 1.5) | (dat.INFO < 0)) & dat.INFO.notnull() - ii = ii & (dat.INFO > args.info_min) - elif type(dat.INFO) is pd.DataFrame: # several INFO columns - jj = (((dat.INFO > 1.5) & dat.INFO.notnull()).any(axis=1) | ((dat.INFO < 0) & dat.INFO.notnull()).any(axis=1)) - ii = ii & (dat.INFO.sum(axis=1) > args.info_min*(len(dat.INFO.columns) ) ) - - bad_info = jj.sum() - if bad_info > 0: - msg = 'WARNING: {N} SNPs had INFO outside of [0,1.5]. The INFO column may be mislabeled.' - log.log(msg.format(N=bad_info)) - - old_len = new_len; new_len = ii.sum() - if verbose: - log.log(filter_verbose(old_len, new_len, 'INFO <= {C}'.format(C=args.info_min))) - if drops is not None: - drops['INFO'] += old_len-new_len - - dat.drop('INFO', inplace=True, axis=1) - - # convert FRQ to MAF and filter on MAF - if 'FRQ' in dat.columns: - jj = (dat.FRQ < 0) | (dat.FRQ > 1) - bad_frq = jj.sum() - if bad_frq > 0: - msg = 'WARNING: {N} SNPs had FRQ outside of [0,1]. The FRQ column may be mislabeled.' - log.log(msg.format(N=bad_frq)) - - dat.FRQ = np.minimum(dat.FRQ, 1-dat.FRQ) - ii = ii & (dat.FRQ > args.maf_min) - old_len = new_len; new_len = ii.sum() - - if verbose: - log.log(filter_verbose(old_len, new_len, 'MAF <= {C}'.format(C=args.maf_min))) - if drops is not None: - drops['FRQ'] += old_len-new_len - - dat.drop('FRQ', inplace=True, axis=1) - - dat = dat[ii] - return (dat, drops) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--sumstats', default=None, type=str, - help="Input filename.") - parser.add_argument('--N', default=None, type=float, - help="Sample size If this option is not set, will try to infer the sample " - "size from the input file. If the input file contains a sample size " - "column, and this flag is set, the argument to this flag has priority.") - parser.add_argument('--N-cas', default=None, type=float, - help="Number of cases. If this option is not set, will try to infer the number " - "of cases from the input file. If the input file contains a number of cases " - "column, and this flag is set, the argument to this flag has priority.") - parser.add_argument('--N-con', default=None, type=float, - help="Number of controls. If this option is not set, will try to infer the number " - "of controls from the input file. If the input file contains a number of controls " - "column, and this flag is set, the argument to this flag has priority.") - parser.add_argument('--out', default=None, type=str, - help="Output filename prefix.") - parser.add_argument('--info-min', default=0.9, type=float, - help="Minimum INFO score.") - parser.add_argument('--maf-min', default=0.01, type=float, - help="Minimum MAF.") - parser.add_argument('--daner', default=False, action='store_true', - help="Use this flag to parse Step han Ripke's daner* file format.") - parser.add_argument('--merge', default=None, type=str, - help="Path to file with a list of SNPs to merge w/ the SNPs in the input file. " - "Will log.log( the same SNPs in the same order as the --merge file, " - "with NA's for SNPs in the --merge file and not in the input file." ) - parser.add_argument('--no-alleles', default=False, action="store_true" , - help="Don't require alleles. Useful if only unsigned summary statistics are available " - "and the goal is h2 / partitioned h2 estimation rather than rg estimation.") - parser.add_argument('--pickle', default=None, action='store_true', - help="Save .chisq file as python pickle.") - parser.add_argument('--merge-alleles', default=None, type=str, - help="Same as --merge, except the file should have three columns: SNP, A1, A2, " - "and all alleles will be matched to the --merge-alleles file alleles.") - parser.add_argument('--no-filter-n', default=False, action='store_true', - help='Don\'t filter SNPs with low N.') - parser.add_argument('--n-min', default=None, type=float, - help='Minimum N (sample size). Default is (90th percentile N) / 2.') - parser.add_argument('--chunksize', default=5e6, type=int, - help='Chunksize for use with --bigmem.') - parser.add_argument('--bigmem', default=True, action='store_false', - help='Don\'t read the whole file into memory -- read one chunk at a time. ' - 'This can be somewhat slower, but reduces memory substantially if the --sumstats ' - 'file contains a lot of SNPs that will eventually be filtered out.') -# parser.add_argument('--filter-finally', default=False, action='store_true', -# help='For use with --bigmem and --merge-alleles. Can save time if --merge-alleles ' -# 'represents only a small subset of the SNPs in --sumstats.') - - # optional args to specify column names - parser.add_argument('--snp', default=None, type=str, - help='Name of SNP column (if not a name that ldsc understands). NB: case insensitive.') - parser.add_argument('--N-col', default=None, type=str, - help='Name of N column (if not a name that ldsc understands). NB: case insensitive.') - parser.add_argument('--N-cas-col', default=None, type=str, - help='Name of N column (if not a name that ldsc understands). NB: case insensitive.') - parser.add_argument('--N-con-col', default=None, type=str, - help='Name of N column (if not a name that ldsc understands). NB: case insensitive.') - parser.add_argument('--a1', default=None, type=str, - help='Name of A1 column (if not a name that ldsc understands). NB: case insensitive.') - parser.add_argument('--a2', default=None, type=str, - help='Name of A2 column (if not a name that ldsc understands). NB: case insensitive.') - parser.add_argument('--p', default=None, type=str, - help='Name of p-value column (if not a name that ldsc understands). NB: case insensitive.') - parser.add_argument('--frq', default=None, type=str, - help='Name of FRQ or MAF column (if not a name that ldsc understands). NB: case insensitive.') - parser.add_argument('--signed-sumstats', default=None, type=str, - help='Name of signed sumstat column, comma null value (e.g., Z,0 or OR,1). NB: case insensitive.') - parser.add_argument('--info', default=None, type=str, - help='Name of INFO column (if not a name that ldsc understands). NB: case insensitive.') - parser.add_argument('--info-list', default=None, type=str, - help='Comma-separated list of INFO columns. Will filter on the mean. NB: case insensitive.') - parser.add_argument('--nstudy', default=None, type=str, - help='Name of NSTUDY column (if not a name that ldsc understands). NB: case insensitive.') - parser.add_argument('--nstudy-min', default=None, type=float, - help='Minimum # of studies. Default is to remove everything below the max, unless there is an N column, in which case do nothing.') - parser.add_argument('--ignore', default=None, type=str, - help='Comma-separated list of column names to ignore.') - parser.add_argument('--a1-inc', default=False, action='store_true', - help='A1 is the increasing allele.') - - args = parser.parse_args() - if args.out is None: - raise ValueError('The --out flag is required.') - - log = sumstats.Logger(args.out + '.log') - if args.sumstats is None: - raise ValueError('The --sumstats flag is required.') - - try: - if not (args.sumstats and args.out): - raise ValueError('--sumstats and --out are required.') - - defaults = vars(parser.parse_args('')) - opts = vars(args) - non_defaults = [x for x in opts.keys() if opts[x] != defaults[x]] - header = MASTHEAD - header += "\nOptions: \n" - options = ['--'+x.replace('_','-')+' '+str(opts[x]) for x in non_defaults] - header += '\n'.join(options).replace('True','').replace('False','') - header += '\n' - log.log( header ) - - log.log('Beginning conversion at {T}'.format(T=time.ctime())) - start_time = time.time() - - print 'Writing log to {F}'.format(F=log.log_name) - if args.merge and args.merge_alleles: - raise ValueError('--merge and --merge-alleles are not compatible.') - - flag_colnames = dict() - if args.nstudy: - clean = clean_header(args.nstudy) - if clean in flag_colnames: - raise ValueError('The --snp flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] != 'NSTUDY': - msg = 'The --snp flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean] = 'NSTUDY' - - if args.snp: - clean = clean_header(args.snp) - if clean in flag_colnames: - raise ValueError('The --snp flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] != 'SNP': - msg = 'The --snp flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean] = 'SNP' - - if args.N_col: - clean = clean_header(args.N_col) - if clean in flag_colnames: - raise ValueError('The --N-col flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] != 'N': - msg = 'The --N-col flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean_header(args.N_col)] = 'N' - - if args.N_cas_col: - clean = clean_header(args.N_cas_col) - if clean in flag_colnames: - raise ValueError('The --N-cas-col flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] != 'N_CAS': - msg = 'The --N-cas-col flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean_header(args.N_cas_col)] = 'N_CAS' - - if args.N_con_col: - clean = clean_header(args.N_con_col) - if clean in flag_colnames: - raise ValueError('The --N-con-col flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] != 'N_CON': - msg = 'The --N-col flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean_header(args.N_con_col)] = 'N_CON' - - if args.a1: - clean = clean_header(args.a1) - if clean in flag_colnames: - raise ValueError('The --a1 flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] not in ['A1','A2']\ - and clean != 'EFFECT': - msg = 'The --a1 flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean_header(args.a1)] = 'A1' - - if args.a2: - clean = clean_header(args.a2) - if clean in flag_colnames: - raise ValueError('The --a2 flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] not in ['A1','A2']: - msg = 'The --a2 flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean_header(args.a2)] = 'A2' - - if args.p: - clean = clean_header(args.p) - if clean in flag_colnames: - raise ValueError('The --p flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] != 'P': - msg = 'The --p flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean_header(args.p)] = 'P' - - if args.frq: - clean = clean_header(args.frq) - if clean in flag_colnames: - raise ValueError('The --frq flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] != 'FRQ': - msg = 'The --frq flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean_header(args.frq)] = 'FRQ' - - if args.info: - clean = clean_header(args.info) - if clean in flag_colnames: - raise ValueError('The --info flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] != 'INFO': - msg = 'The --info flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean_header(args.info)] = 'INFO' - - if args.info_list: - try: - info_list = map(clean_header, args.info_list.split(',')) - except ValueError: - log.log('The argument to --info-list should be a comma-separated list of column names.') - raise - - for clean in info_list: - if clean in flag_colnames: - raise ValueError('The --info-list flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean] != 'INFO': - msg = 'The --info-list flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - flag_colnames[clean] = 'INFO' - - - if args.signed_sumstats: - try: - (SIGNED_SUMSTAT_CNAME, SIGNED_SUMSTAT_NULL_VALUE) = args.signed_sumstats.split(',') - clean = clean_header(SIGNED_SUMSTAT_CNAME) - except ValueError: - log.log('The argument to --signed-sumstats should be formatted as column header comma number.') - raise - - if clean in flag_colnames: - raise ValueError('The --signed-sumstats flag has overloaded a column name set by another flag.') - if clean in COLNAMES_CONVERSION and COLNAMES_CONVERSION[clean]\ - not in ['BETA','Z','OR','SIGNED_SUMSTAT','LOG_ODDS']: - msg = 'The --signed-sumstats flag conflicts with a protected column name, usually taken to mean {F}' - raise ValueError(msg.format(F=COLNAMES_CONVERSION[clean])) - - flag_colnames[clean_header(SIGNED_SUMSTAT_CNAME)] = 'SIGNED_SUMSTAT' - SIGNED_SUMSTAT_NULL_VALUE = float(SIGNED_SUMSTAT_NULL_VALUE) - - - (openfunc, compression) = get_compression(args.sumstats) - out_chisq = args.out+'.chisq' - colnames = openfunc(args.sumstats).readline().split() - - # also read FRQ_U_* and FRQ_A_* columns from Stephan Ripke's daner* files - if args.daner: - frq = filter(lambda x: x.startswith('FRQ_U_'), colnames)[0] - COLNAMES_CONVERSION[frq] = 'FRQ' - - log.log('Interpreting column names as follows:\n') - # first add the columns specified via flags - usecols = [x for x in colnames if clean_header(x) in flag_colnames.keys()] - clean_usecols = map(clean_header, usecols) - # if any of the columns specified by flags are not in the header, throw an error - for cname in flag_colnames.keys(): - if cname not in clean_usecols: - msg = 'Could not find a column labeled {M} (case-insensitive).' - raise ValueError(msg.format(M=cname)) - - # next add the columns from the main dict, so long as they aren't overriden by flags - usecols += [x for x in colnames if clean_header(x) in COLNAMES_CONVERSION.keys() and - x not in usecols and - COLNAMES_CONVERSION[clean_header(x)] not in flag_colnames.values()] - clean_usecols = [convert_colname(x, pre=flag_colnames) for x in usecols] - # resolve conflicts with multiple signed summary statistic columns - if args.signed_sumstats is None: - Z = 'Z' in clean_usecols - OR = 'OR' in clean_usecols - LOR = 'LOG_ODDS' in clean_usecols - BETA = 'BETA' in clean_usecols - if sum([Z,OR,LOR,BETA]) > 1: - msg = 'Multiple signed summary stats found: priority is OR, Z, BETA, LOG_ODDS. ' - msg += 'This can be adjusted with the --signed-sumstats flag.' - log.log(msg) - drop_cnames = [x for x in ['Z','LOR','BETA'] if x not in flag_colnames.keys()] - if OR: - usecols = filter(lambda x: x not in drop_cnames, usecols) - elif Z: - usecols = filter(lambda x: x not in drop_cnames[1:3], usecols) - elif BETA: - usecols = filter(lambda x: x not in drop_cnames[2:3], usecols) - - else: - # Drop all the signed sumstat colnames that aren't the --signed-sumstats colname - drop_cnames = [x for x in ['OR' 'Z','LOR','BETA'] if x not in flag_colnames.keys()] - usecols = filter(lambda x: x not in drop_cnames, usecols) - - clean_usecols = [convert_colname(x, pre=flag_colnames) for x in usecols] - msg = [c+': '+convert_colname(c, pre=flag_colnames)+' --> '+\ - COL_TO_ENGLISH[convert_colname(c, pre=flag_colnames)] for c in usecols] - - log.log('\n'.join(msg)) - log.log('') - if ('N' not in clean_usecols) and (args.N is None) and ((args.N_cas is None) or - (args.N_con is None)) and (('N_CAS' not in clean_usecols) or ('N_CON' not in clean_usecols)) and (not args.daner): - raise ValueError('Could not find an N / N_cas / N_con column and --N / --N-cas / --N-con are not set.') - if 'P' not in clean_usecols: - raise ValueError('Could not find a p-value column.') - if ('Z' not in clean_usecols) and ('BETA' not in clean_usecols)\ - and ('OR' not in clean_usecols) and ('LOG_ODDS' not in clean_usecols)\ - and (args.signed_sumstats is None) and args.a1_inc is False: - raise ValueError('Could not find a signed summary statistic column (Z, BETA, OR, LOG_ODDS).') - if 'SNP' not in clean_usecols: - raise ValueError('Could not find a SNP column.') - if ('A1' not in clean_usecols) or ('A2' not in clean_usecols): - raise ValueError('Could not find allele columns.') - if 'INFO' not in clean_usecols: - msg = 'WARNING: Could not find an INFO column. Note that imputation quality is ' - msg += 'a confounder for LD Score regression, and we recommend filtering on INFO > 0.9' - log.log(msg) - if 'FRQ' not in clean_usecols: - log.log('Could not find a FRQ column. Note that we recommend filtering on MAF > 0.01') - - if args.merge_alleles: - log.log('Reading list of SNPs for allele merge from {F}'.format(F=args.merge_alleles)) - (openfunc, compression) = get_compression(args.merge_alleles) - merge_alleles = pd.read_csv(args.merge_alleles, compression=compression, header=0, - delim_whitespace=True) - if len(merge_alleles.columns) == 1 | np.all(merge_alleles.columns != ["SNP","A1","A2"]): - raise ValueError('--merge-alleles must have columns SNP, A1, A2.') - - log.log('Read {N} SNPs for allele merge.'.format(N=len(merge_alleles))) - merge_alleles.A1 = merge_alleles.A1.apply(lambda y: y.upper()) - merge_alleles.A2 = merge_alleles.A2.apply(lambda y: y.upper()) - - (openfunc, compression) = get_compression(args.sumstats) - tot_snps = 0 - merge_drops = 0 - if args.bigmem: - ''' - Reduce memory footprint by filtering on MAF / INFO / etc on-disk rather than - in-memory. This writes to a new file with the same name as indicated by --out. This - will likely be somewhat slower. - - ''' - - dat_gen = pd.read_csv(args.sumstats, delim_whitespace=True, header=0, - compression=compression, usecols=usecols, na_values='.', iterator=True, - chunksize=args.chunksize) # default 1m - - msg = 'Reading sumstats from {F} into memory {N} SNPs at a time.' - log.log(msg.format(F=args.sumstats, N=args.chunksize)) - dat_list = [] - drops = { # of SNPs dropped for each reason - 'RS': 0, - 'NA': 0, - 'P': 0, - 'INFO': 0, - 'FRQ': 0, - } - for block_num,dat in enumerate(dat_gen): - tot_snps += len(dat) - dat.columns = map(lambda x: convert_colname(x, pre=flag_colnames), dat.columns) - dat, drops = filter_na(dat, args, log, drops, verbose=False) - if args.merge_alleles: - ii = dat.SNP.isin(merge_alleles.SNP) - merge_drops += len(dat) - ii.sum() - dat = dat[ii].reset_index(drop=True) - - dat, drops = filter_snps(dat, args, log, drops, verbose=False) - dat_list.append(dat) - sys.stdout.write('.') - - sys.stdout.write('\n') - dat = pd.concat(dat_list, axis=0) - msg = 'Read {N} SNPs from --sumstats file.\n'.format(N=tot_snps) - if args.merge_alleles: - msg += 'Removed {N} SNPs not in --merge-alleles.\n'.format(N=merge_drops) - - msg += 'Removed {N} SNPs with duplicated rs numbers.\n'.format(N=drops['RS']) - msg += 'Removed {N} SNPs with missing values.\n'.format(N=drops['NA']) - msg += 'Removed {N} SNPs with out-of-bounds p-values.\n'.format(N=drops['P']) - msg += 'Removed {N} SNPs with INFO <= {I}\n'.format(N=drops['INFO'], I=args.info_min) - msg += 'Removed {N} SNPs with MAF <= {M}\n'.format(N=drops['FRQ'], M=args.maf_min) - msg += 'At this point, {N} SNPs remain.'.format(N=len(dat)) - log.log(msg) - - if len(dat) == 0: - raise ValueError('No SNPs remain.') - - else: - ''' Read everything into memory all at once ''' - dat = pd.read_csv(args.sumstats, delim_whitespace=True, header=0, compression=compression, - usecols=usecols, na_values='.') - log.log( "Read summary statistics for {M} SNPs from {F}.".format(M=len(dat), F=args.sumstats)) - dat.columns = map(lambda x: convert_colname(x, pre=flag_colnames), dat.columns) - dat, drops = filter_na(dat, args, log) - if args.merge_alleles: - old_len = len(dat) - dat = dat[dat.SNP.isin(merge_alleles.SNP)].reset_index(drop=True) - new_len = len(dat) - msg = 'Removed {M} SNPs in --sumstats not in --merge-alleles ({N} SNPs remain).' - log.log(msg.format(M=old_len-new_len, N=new_len)) - if new_len == 0: - raise ValueError('No SNPs remain.') - - dat, drops = filter_snps(dat, args, log) - - # drop low NSTUDY (NB can't do this inside of bigmem, because max NSTUDY may differ f - # from chunk to chunk (though this is not likely) - if 'NSTUDY' in dat.columns and ('N' not in dat.columns) and ('N_CAS' not in dat.columns): - if args.nstudy_min: - nstudy_thresh = args.nstudy_min - else: - nstudy_thresh = dat.NSTUDY.max() - - ii = dat.NSTUDY < nstudy_thresh - old_len = len(dat); new_len = ii.sum() - log.log(filter_verbose(old_len, new_len, 'NSTUDY <= {C}'.format(C=nstudy_thresh))) - dat.drop('NSTUDY', inplace=True, axis=1) - - # infer # cases and # controls from daner* column headers - if args.daner: - log.log('Note that the --daner flag takes precedence over all other sample size and frequency flags and columns.') - N_con = int(filter(lambda x: x.startswith('FRQ_U_'), colnames)[0][6:]) - N_cas = int(filter(lambda x: x.startswith('FRQ_A_'), colnames)[0][6:]) - dat['N'] = N_cas + N_con - log.log( 'Inferred that N_cas = {N} from the FRQ_A column.'.format(N=N_cas)) - log.log( 'Inferred that N_con = {N} from the FRQ_U column.'.format(N=N_con)) - - # N - if args.N: - dat['N'] = args.N - log.log( 'Using N = {N}'.format(N=args.N)) - - elif args.N_cas and args.N_con: - dat['N'] = args.N_cas + args.N_con - msg = 'Using N_cas = {N1}; N_con = {N2}' - log.log( msg.format(N1=args.N_cas, N2=args.N_con)) - - elif 'N_CAS' in dat.columns and 'N_CON' in dat.columns: - log.log( 'Reading sample size from the N_cas and N_con columns.') - msg = 'Median N_cas = {N1}; Median N_con = {N2}' - log.log(msg.format(N1=round(np.median(dat.N_CAS), 0), N2=round(np.median(dat.N_CON),0))) - N = dat.N_CAS + dat.N_CON - P = dat.N_CAS / N - ii = N == N.max() - P_max = P[ii].mean() - log.log( "Using max sample prevalence = {P}.".format(P=round(P_max,2))) - dat['N'] = N * P / P_max - dat.drop(['N_CAS', 'N_CON'], inplace=True, axis=1) - - elif 'N' in dat.columns: - log.log( 'Reading sample size from the N column. Median N = {N}'.format(N=round(np.median(dat.N), 0))) - - else: - raise ValueError('No N specified.') - - # filter out low N - if (not args.no_filter_n) and (args.N is None) and (args.N_cas is None): - if args.n_min: - N_thresh = args.min_n - else: - N_thresh = dat.N.quantile(0.9) / 1.5 - old_len = len(dat) - ii = (dat.N > N_thresh) - new_len = ii.sum() - msg = 'Removed {M} SNPs with N below {T} ({N} SNPs remain).' - log.log(msg.format(N=new_len, M=old_len-new_len, T=round(N_thresh, 0))) - dat = dat[ii] - - # convert p-values to chi^2 - dat.P = chi2.isf(dat.P, 1) - dat.rename(columns={'P': 'CHISQ'}, inplace=True) - - # everything with alleles here - if 'A1' in dat.columns and 'A2' in dat.columns and not args.no_alleles: - - # capitalize alleles - dat.A1 = dat.A1.apply(lambda y: y.upper()) - dat.A2 = dat.A2.apply(lambda y: y.upper()) - - # filter out indels - ii = dat.A1.isin(['A','T','C','G']) - ii = ii & dat.A2.isin(['A','T','C','G']) - old_len = len(dat) - new_len = ii.sum() - if new_len == 0: - raise ValueError('Every SNP was not coded A/C/T/G. Something is wrong.') - else: - msg = 'Removed {M} variants not coded A/C/T/G ({N} SNPs remain).' - log.log( msg.format(N=new_len, M=old_len-new_len)) - - dat = dat[ii] - - # remove strand ambiguous SNPs - strand = (dat.A1 + dat.A2).apply(lambda y: sumstats.STRAND_AMBIGUOUS[y]) - dat = dat[~strand] - old_len = new_len - new_len = len(dat) - if new_len == 0: - raise ValueError('All remaining SNPs are strand ambiguous') - else: - msg = 'Removed {M} strand ambiguous SNPs ({N} SNPs remain).' - log.log( msg.format(N=new_len, M=old_len-new_len)) - - # signed summary stat and alleles - if args.signed_sumstats is not None: - log.log('Using argument of --signed-sumstats as the directional summary statistic.') - check = np.median(dat.SIGNED_SUMSTAT) - if np.abs(check-SIGNED_SUMSTAT_NULL_VALUE) > 0.1: - msg = 'WARNING: median value of --signed-sumstats column is {M} (should be close to {V}). This column may be mislabeled.' - log.log( msg.format(M=round(check,2), V=SIGNED_SUMSTAT_NULL_VALUE)) - else: - log.log('Median value of --signed-sumstats column was {C}, which seems sensible.'.format(C=check)) - - dat.SIGNED_SUMSTAT = dat.SIGNED_SUMSTAT.convert_objects(convert_numeric=True) - flip = dat.SIGNED_SUMSTAT < SIGNED_SUMSTAT_NULL_VALUE - dat.drop('SIGNED_SUMSTAT', inplace=True, axis=1) - - elif 'OR' in dat.columns: - log.log('Using OR (odds ratio) as the directional summary statistic.') - check = np.median(dat.OR) - if np.abs(check-1) > 0.1: - msg = 'WARNING: median OR is {M} (should be close to 1). This column may be mislabeled.' - log.log( msg.format(M=round(check,2))) - else: - log.log('Median value of OR was {C}, which seems sensible.'.format(C=check)) - - dat.OR = dat.OR.convert_objects(convert_numeric=True) - flip = dat.OR < 1 - dat.drop('OR', inplace=True, axis=1) - - elif 'Z' in dat.columns: - log.log('Using Z (Z-score) as the directional summary statistic.') - check = np.median(dat.Z) - if np.abs(check) > 0.1: - msg = 'WARNING: median Z is {M} (should be close to 0). This column may be mislabeled.' - log.log( msg.format(M=round(check,2)) ) - else: - log.log('Median value of Z was {C}, which seems sensible.'.format(C=check)) - - dat.Z = dat.Z.convert_objects(convert_numeric=True) - flip = dat.Z < 0 - dat.drop('Z', inplace=True, axis=1) - - elif 'BETA' in dat.columns: - log.log('Using BETA as the directional summary statistic.') - check = np.median(dat.BETA) - if np.abs(check) > 0.1: - msg = 'WARNING: median BETA is {M} (should be close to 0). This column may be mislabeled.' - log.log( msg.format(M=round(check,2))) - else: - log.log('Median value of BETA was {C}, which seems sensible.'.format(C=check)) - - dat.BETA = dat.BETA.convert_objects(convert_numeric=True) - flip = dat.BETA < 0 - dat.drop('BETA', inplace=True, axis=1) - - elif 'LOG_ODDS' in dat.columns: - log.log('Using Log odds as the directional summary statistic.') - check = np.median(dat.LOG_ODDS) - if np.abs(check) > 0.1: - msg = 'WARNING: median Log odds is {M} (should be close to 0). This column may be mislabeled.' - log.log( msg.format(M=round(check,2))) - else: - log.log('Median value of LOG_ODDS was {C}, which seems sensible.'.format(C=check)) - - dat.LOG_ODDS = dat.LOG_ODDS.convert_objects(convert_numeric=True) - flip = dat.LOG_ODDS < 0 - dat.drop('LOG_ODDS', inplace=True, axis=1) - - else: # assume A1 is trait increasing allele and print a warning - if args.a1_inc: - flip = pd.Series(False) - else: - log.log( 'WARNING: no signed summary stat found. Assuming A1 is risk/increasing allele.') - raise ValueError('WARNING: no signed summary stat found. Assuming A1 is risk/increasing allele.') - - # convert A1 and A2 to INC_ALLELE and DEC_ALLELE - INC_ALLELE = dat.A1 - DEC_ALLELE = dat.A2 - - if flip.any(): - x = dat.A1[flip] - INC_ALLELE[flip] = dat.A2[flip] - DEC_ALLELE[flip] = x - - dat['INC_ALLELE'] = INC_ALLELE - dat['DEC_ALLELE'] = DEC_ALLELE - dat.drop(['A1','A2'], inplace=True, axis=1) - - # merge with --merge-alleles - if args.merge_alleles: - - ''' - WARNING: dat now contains a bunch of NA's~ - Note: dat now has the same SNPs in the same order as --merge alleles. - ''' - - dat = pd.merge(merge_alleles, dat, how='left', on='SNP', sort=False).reset_index(drop=True) - ii = dat.N.notnull() - alleles = dat.INC_ALLELE[ii] + dat.DEC_ALLELE[ii] + dat.A1[ii] + dat.A2[ii] - try: - # true iff the alleles match; false --> throw out - match = alleles.apply(lambda y: sumstats.MATCH_ALLELES[y]) - except KeyError as e: - msg = 'Could not match alleles between --sumstats and --merge-alleles.\n' - msg += 'Does your --merge-alleles file contain indels or strand ambiguous SNPs?' - log.log( msg ) - raise ValueError(msg) - - x = dat[ii] - jj = pd.Series(np.zeros(len(dat),dtype=bool)) - jj[ii] = match - # set all SNPs that were already NA or had funky alleles to NA - dat.N[~jj] = float('nan') - dat.CHISQ[~jj] = float('nan') - dat.INC_ALLELE[~jj] = float('nan') - dat.DEC_ALLELE[~jj] = float('nan') - old_len = new_len - new_len = jj.sum() - if new_len == 0: - raise ValueError('All remaining SNPs have alleles that are discordant between --sumstats and --merge-alleles.') - else: - msg = 'Removed {M} SNPs whose alleles did not match --merge-alleles ({N} SNPs remain).' - log.log( msg.format(N=new_len, M=old_len-new_len)) - - dat = dat.drop(['A1','A2'], axis=1) - - elif not args.no_alleles: - raise ValueError('Could not find A1 and A2 columns in --sumstats.') - - # write chisq file - chisq_colnames = [c for c in ['SNP','INFO','N','CHISQ','MAF','INC_ALLELE','DEC_ALLELE'] - if c in dat.columns] - if not args.pickle: - msg = 'Writing chi^2 statistics for {M} SNPs ({N} of which have nonmissing chi^2) to {F}.' - log.log( msg.format(M=len(dat), F=out_chisq+'.gz', N=dat.N.notnull().sum())) - dat.ix[:,chisq_colnames].to_csv(out_chisq, sep="\t", index=False) - os.system('gzip -f {F}'.format(F=out_chisq)) - else: - msg = 'Writing chi^2 statistics for {M} SNPs ({N} of which have nonmissing chi^2) to {F}.' - log.log( msg.format(M=len(dat), F=out_chisq+'.pickle', N=dat.N.notnull().sum())) - out_chisq += '.pickle' - dat.ix[:,chisq_colnames].reset_index(drop=True).to_pickle(out_chisq) - - log.log( '\n' ) - log.log('Conversion finished at {T}'.format(T=time.ctime()) ) - time_elapsed = round(time.time()-start_time,2) - log.log('Total time elapsed: {T}'.format(T=sumstats.sec_to_str(time_elapsed))) - log.log( '\n' ) - log.log('Printing metadata') - # write metadata - np.set_printoptions(precision=4) - pd.set_option('precision', 4) - pd.set_option('display.max_rows', 100000) - - # mean chi^2 - mean_chisq = dat.CHISQ.mean() - log.log( 'Mean chi^2 = ' + str(round(mean_chisq,3))) - if mean_chisq < 1.02: - log.log( "WARNING: mean chi^2 may be too small.") - - # lambda GC - lambda_gc = dat.CHISQ.median() / 0.4549 - log.log( 'Lambda GC = ' + str(round(lambda_gc,3))) - - # min p-value - log.log('Max chi^2 = ' + str(np.matrix(dat.CHISQ.max())).replace('[[','').replace(']]','').replace(' ',' ')) - - # most significant SNPs - ii = dat.CHISQ > 29 - ngwsig = ii.sum() - if ngwsig > 0: - log.log( "{N} Genome-wide significant SNPs.".format(N=ngwsig)) - #log.log( dat[ii]) - else: - log.log('No genome-wide significant SNPs') - log.log('NB some gwsig SNPs may have been removed after various filtering steps.') - - except Exception as e: - - log.log('\nERROR converting summary statistics:\n') - ex_type, ex, tb = sys.exc_info() - log.log( traceback.format_exc(ex) ) \ No newline at end of file diff --git a/test/annot_test/test.annot b/test/annot_test/test.annot index 6b1700e9..38de6b9f 100644 --- a/test/annot_test/test.annot +++ b/test/annot_test/test.annot @@ -1,8 +1,4 @@ -1 1 rs_0 0 0 0 1 0 -1 2 rs_1 0 0 0 1 0 -1 3 rs_2 0 0 0 1 0 -1 4 rs_3 0 0 1 0 0 -1 5 rs_4 0 1 0 0 0 -1 6 rs_5 0 0 0 1 0 -1 7 rs_6 0 0 0 0 1 -1 8 rs_7 0 0 0 0 1 +CHR BP SNP CM C1 C2 C3 +1 1 rs_0 0 1 0 0 +1 2 rs_1 0 0 1 1 +1 3 rs_2 0 0 1 1 diff --git a/test/annot_test/test1.bed b/test/annot_test/test1.bed deleted file mode 100644 index e4f306b2..00000000 --- a/test/annot_test/test1.bed +++ /dev/null @@ -1,5 +0,0 @@ -chr1 1 3 -chr1 5 6 -chr2 6 15 -chr9 10000 10200 -chr9 14000 39000 \ No newline at end of file diff --git a/test/annot_test/test1.frq b/test/annot_test/test1.frq new file mode 100644 index 00000000..9738d1e2 --- /dev/null +++ b/test/annot_test/test1.frq @@ -0,0 +1,4 @@ +CHR SNP CM BP A1 FRQ +1 rs_0 0 1 0 0.7 +1 rs_1 0 2 0 0.1 +1 rs_2 0 3 2 0.01 diff --git a/test/annot_test/test2.bed b/test/annot_test/test2.bed deleted file mode 100644 index d384fb52..00000000 --- a/test/annot_test/test2.bed +++ /dev/null @@ -1 +0,0 @@ -chr1 4 5 \ No newline at end of file diff --git a/test/geno_test/test.bed b/test/geno_test/test.bed deleted file mode 100644 index 1451802d..00000000 Binary files a/test/geno_test/test.bed and /dev/null differ diff --git a/test/geno_test/test.bim b/test/geno_test/test.bim deleted file mode 100644 index 9c59174f..00000000 --- a/test/geno_test/test.bim +++ /dev/null @@ -1,10 +0,0 @@ -9 rs185444096 -0.000597 10177 C T -9 rs190296880 -0.000596 10192 T A -9 rs56377469 -0.000591 10469 G C -9 rs7341907 -0.000583 10869 C G -9 rs149305563 -0.000509 14665 G A -9 rs149079262 -0.000509 14690 C G -9 rs141156662 -0.000486 15883 A G -9 rs184525769 -0.000150 33204 C T -9 rs2492179 -0.000036 39037 A C -9 rs9408135 -0.000036 39043 T C diff --git a/test/geno_test/test.fam b/test/geno_test/test.fam deleted file mode 100644 index d1f11ba7..00000000 --- a/test/geno_test/test.fam +++ /dev/null @@ -1,379 +0,0 @@ -GBR HG00096 0 0 1 -9 -GBR HG00097 0 0 2 -9 -GBR HG00099 0 0 2 -9 -GBR HG00100 0 0 2 -9 -GBR HG00101 0 0 1 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0 1 -9 -TSI NA20810 0 0 1 -9 -TSI NA20811 0 0 1 -9 -TSI NA20812 0 0 1 -9 -TSI NA20813 0 0 2 -9 -TSI NA20814 0 0 1 -9 -TSI NA20815 0 0 1 -9 -TSI NA20816 0 0 1 -9 -TSI NA20818 0 0 2 -9 -TSI NA20819 0 0 2 -9 -TSI NA20826 0 0 2 -9 -TSI NA20828 0 0 2 -9 diff --git a/test/geno_test/test.geno b/test/geno_test/test.geno deleted file mode 100644 index cfca43de..00000000 --- a/test/geno_test/test.geno +++ /dev/null @@ -1,10 +0,0 @@ -0000100020000000002000000000010000010010000000000000000001000000000000000100000000001001000000000010100001100000000000000000000100000000000000000000000000000000010001000000000000000000000000000000000001000100000001000010000000000000000000000000000000010100000101000000000000000001000101001000000100100010011000110010000000000001111000000001010001000010000000000100000000100000000 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-NA20804 F TSI -NA20805 M TSI -NA20806 M TSI -NA20807 F TSI -NA20808 F TSI -NA20809 M TSI -NA20810 M TSI -NA20811 M TSI -NA20812 M TSI -NA20813 F TSI -NA20814 M TSI -NA20815 M TSI -NA20816 M TSI -NA20818 F TSI -NA20819 F TSI -NA20826 F TSI -NA20828 F TSI diff --git a/test/geno_test/test.snp b/test/geno_test/test.snp deleted file mode 100644 index dc4370a1..00000000 --- a/test/geno_test/test.snp +++ /dev/null @@ -1,10 +0,0 @@ -rs185444096 9 -0.000597 10177 C T -rs190296880 9 -0.000596 10192 T A -rs56377469 9 -0.000591 10469 G C -rs7341907 9 -0.000583 10869 C G -rs149305563 9 -0.000509 14665 G A -rs149079262 9 -0.000509 14690 C G -rs141156662 9 -0.000486 15883 A G -rs184525769 9 -0.000150 33204 C T -rs2492179 9 -0.000036 39037 A C -rs9408135 9 -0.000036 39043 T C diff --git a/test/munge_test/correct.sumstats b/test/munge_test/correct.sumstats new file mode 100644 index 00000000..16e76a00 --- /dev/null +++ b/test/munge_test/correct.sumstats @@ -0,0 +1,5 @@ +SNP A1 A2 Z N +rs11250701 A G 0.70376361 6702.0 +rs7085086 A G -0.77303782 6702.0 +rs113494187 T G -0.02893053 6702.0 +rs7911665 T G 1.24237021 6702.0 diff --git a/test/munge_test/correct_merge.sumstats b/test/munge_test/correct_merge.sumstats new file mode 100644 index 00000000..502db267 --- /dev/null +++ b/test/munge_test/correct_merge.sumstats @@ -0,0 +1,5 @@ +SNP A1 A2 Z N +rs117915320 +rs182753344 +rs188913771 +rs7911665 T G 1.24237021 6702.0 diff --git a/test/munge_test/merge_alleles b/test/munge_test/merge_alleles new file mode 100644 index 00000000..677c22ca --- /dev/null +++ b/test/munge_test/merge_alleles @@ -0,0 +1,5 @@ +SNP A1 A2 +rs117915320 A C +rs182753344 T C +rs188913771 A G +rs7911665 T G diff --git a/test/munge_test/merge_alleles_bad b/test/munge_test/merge_alleles_bad new file mode 100644 index 00000000..cdbcbe94 --- /dev/null +++ b/test/munge_test/merge_alleles_bad @@ -0,0 +1,4 @@ +rs117915320 A +rs182753344 T +rs188913771 A +rs7911665 T diff --git a/test/munge_test/sumstats b/test/munge_test/sumstats new file mode 100644 index 00000000..8fa7eb9f --- /dev/null +++ b/test/munge_test/sumstats @@ -0,0 +1,10 @@ +SNP A1 A2 FRQ_A_1234 FRQ_U_5468 INFO OR P +rs185339560 T C 0.00823 0.00758 0.005 1.03634 0.1341 +rs11250701 A G 0.667 0.656 0.948 1.02798 0.48158 +chr10_2622752_D I2 D 0.968 0.971 0.924 0.93417 0.7235 +rs7085086 A G 0.305 0.308 0.965 0.37648 0.4395 +rs113494187 T G 0.984 0.985 0.908 0.89709 0.97692 +rs117915320 A C 0.0132 0.0158 0.682 0.93941 0.2101 +rs182753344 T C 0.0935 0.096 0.633 0.94582 0.57894 +rs188913771 A G 0.0104 0.00928 0.663 1.13372 0.1794 +rs7911665 T G 0.689 0.691 0.995 1.01359 0.2141 diff --git a/test/parse_test/test.betaprod b/test/parse_test/test.betaprod deleted file mode 100644 index 69bee162..00000000 --- a/test/parse_test/test.betaprod +++ /dev/null @@ -1,4 +0,0 @@ -CHR SNP CM BP A1 A2 N1 CHISQ1 DIR1 N2 P2 DIR2 -1 rs1 0 1 A T 100 100 -1 5 0.025347318677468311 1 -1 rs2 0 2 A T 100 100 1 5 0.025347318677468311 -1 -1 rs3 0 3 A T 100 100 -1 5 0.025347318677468311 1 diff --git a/test/parse_test/test.chisq b/test/parse_test/test.chisq deleted file mode 100644 index 0652b9f4..00000000 --- a/test/parse_test/test.chisq +++ /dev/null @@ -1,4 +0,0 @@ -CHR SNP CM BP N P INFO MAF -1 rs1 0 1 100 0.1 1 0.5 -1 rs2 0 2 100 1 1 0.01 -1 rs3 0 3 100 0.5 1 0.99 diff --git a/test/parse_test/test.cts b/test/parse_test/test.cts new file mode 100644 index 00000000..31c4662f --- /dev/null +++ b/test/parse_test/test.cts @@ -0,0 +1,3 @@ +rs1 1 +rs2 2 +rs3 3 diff --git a/test/parse_test/test.l2.ldscore b/test/parse_test/test.l2.ldscore deleted file mode 100644 index 4ad8ed5c..00000000 --- a/test/parse_test/test.l2.ldscore +++ /dev/null @@ -1,23 +0,0 @@ -CHR SNP BP CM MAF AL2 BL2 -1 rs1 1 0 0.5 1 2 -2 rs2 1 0 0.5 2 4 -3 rs3 1 0 0.5 3 6 -4 rs4 1 0 0.5 4 8 -5 rs5 1 0 0.5 5 10 -6 rs6 1 0 0.5 6 12 -7 rs7 1 0 0.5 7 14 -8 rs8 1 0 0.5 8 16 -9 rs9 1 0 0.5 9 18 -10 rs10 1 0 0.5 10 20 -11 rs11 1 0 0.5 11 22 -12 rs12 1 0 0.5 12 24 -13 rs13 1 0 0.5 13 26 -14 rs14 1 0 0.5 14 28 -15 rs15 1 0 0.5 15 30 -16 rs16 1 0 0.5 16 32 -17 rs17 1 0 0.5 17 34 -18 rs18 1 0 0.5 18 36 -19 rs19 1 0 0.5 19 38 -20 rs20 1 0 0.5 20 40 -21 rs21 1 0 0.5 21 42 -22 rs22 1 0 0.5 22 44 diff --git a/test/parse_test/test.l2.ldscore.gz b/test/parse_test/test.l2.ldscore.gz new file mode 100644 index 00000000..28a27821 Binary files /dev/null and b/test/parse_test/test.l2.ldscore.gz differ diff --git a/test/parse_test/test.sumstats b/test/parse_test/test.sumstats new file mode 100644 index 00000000..2135f4da --- /dev/null +++ b/test/parse_test/test.sumstats @@ -0,0 +1,4 @@ +SNP N Z A1 A2 +rs1 10 10 A T +. 10 10 A T + diff --git a/test/parse_test/test1.frq b/test/parse_test/test1.frq new file mode 100644 index 00000000..ce38ab30 --- /dev/null +++ b/test/parse_test/test1.frq @@ -0,0 +1,9 @@ +CHR SNP CM BP A1 FRQ +1 rs_0 0 1 0 0.01 +1 rs_1 0 2 0 0.1 +1 rs_2 0 3 2 0.7 +1 rs_3 0 4 0 0.2 +1 rs_4 0 5 1 0.2 +1 rs_5 0 6 1 0.2 +1 rs_6 0 7 2 0.99 +1 rs_7 0 8 2 0.03 diff --git a/test/parse_test/test1.l2.ldscore b/test/parse_test/test1.l2.ldscore deleted file mode 100644 index 7a501499..00000000 --- a/test/parse_test/test1.l2.ldscore +++ /dev/null @@ -1,2 +0,0 @@ -CHR SNP BP CM MAF AL2 BL2 -1 rs1 1 0 0.5 1 2 diff --git a/test/parse_test/test1.l2.ldscore.bz2 b/test/parse_test/test1.l2.ldscore.bz2 new file mode 100644 index 00000000..3d54e835 Binary files /dev/null and b/test/parse_test/test1.l2.ldscore.bz2 differ diff --git a/test/parse_test/test2.frq.gz b/test/parse_test/test2.frq.gz new file mode 100644 index 00000000..73b5fb70 Binary files /dev/null and b/test/parse_test/test2.frq.gz differ diff --git a/test/parse_test/test2.l2.ldscore b/test/parse_test/test2.l2.ldscore deleted file mode 100644 index a05b2e0c..00000000 --- a/test/parse_test/test2.l2.ldscore +++ /dev/null @@ -1,2 +0,0 @@ -CHR SNP BP CM MAF AL2 BL2 -2 rs2 1 0 0.5 2 4 diff --git a/test/parse_test/test2.l2.ldscore.bz2 b/test/parse_test/test2.l2.ldscore.bz2 new file mode 100644 index 00000000..04ee7af3 Binary files /dev/null and b/test/parse_test/test2.l2.ldscore.bz2 differ diff --git a/test/simulate.py b/test/simulate.py new file mode 100644 index 00000000..b7a6cfed --- /dev/null +++ b/test/simulate.py @@ -0,0 +1,81 @@ +''' +Generates .sumstats and .l2.ldscore/.l2.M files used for simulation testing. + +''' +from __future__ import division +import numpy as np +import pandas as pd + +N_INDIV = 10000 +N_SIMS = 1000 +N_SNP = 1000 +h21 = 0.3 +h22 = 0.6 + + +def print_ld(x, fh, M): + l2 = '.l2.ldscore' + m = '.l2.M_5_50' + x.to_csv(fh + l2, sep='\t', index=False, float_format='%.3f') + print >>open(fh + m, 'wb'), '\t'.join(map(str, M)) + + # chr1 + y = x.iloc[0:int(len(x) / 2), ] + y.to_csv(fh + '1' + l2, sep='\t', index=False, float_format='%.3f') + print >>open(fh + '1' + m, 'wb'), '\t'.join((str(x / 2) for x in M)) + + # chr2 + y = x.iloc[int(len(x) / 2):len(x), ] + y.to_csv(fh + '2' + l2, sep='\t', index=False, float_format='%.3f') + print >>open(fh + '2' + m, 'wb'), '\t'.join((str(x / 2) for x in M)) + +two_ldsc = np.abs(100 * np.random.normal(size=2 * N_SNP)).reshape((N_SNP, 2)) +single_ldsc = np.sum(two_ldsc, axis=1).reshape((N_SNP, 1)) +M_two = np.sum(two_ldsc, axis=0) +M = np.sum(single_ldsc) +ld = pd.DataFrame({ + 'CHR': np.ones(N_SNP), + 'SNP': ['rs' + str(i) for i in xrange(1000)], + 'BP': np.arange(N_SNP)}) + +# 2 LD Scores 2 files +split_ldsc = ld.copy() +split_ldsc['LD'] = two_ldsc[:, 0] +print_ld(split_ldsc, 'simulate_test/ldscore/twold_firstfile', [M_two[0]]) +split_ldsc = ld.copy() +split_ldsc['LD'] = two_ldsc[:, 1] # both have same colname to test that this is ok +print_ld(split_ldsc, 'simulate_test/ldscore/twold_secondfile', [M_two[1]]) + +# 1 LD Score 1 file +ldsc = ld.copy() +ldsc['LD'] = single_ldsc +print_ld(ldsc, 'simulate_test/ldscore/oneld_onefile', [M]) + +# 2 LD Scores 1 file +ldsc = ld.copy() +ldsc['LD1'] = two_ldsc[:, 0] +ldsc['LD2'] = two_ldsc[:, 1] +print_ld(ldsc, 'simulate_test/ldscore/twold_onefile', M_two) + +# Weight LD Scores +w_ld = ld.copy() +w_ld['LD'] = np.ones(N_SNP) +w_ld.to_csv('simulate_test/ldscore/w.l2.ldscore', + index=False, sep='\t', float_format='%.3f') +# split across chromosomes +df = pd.DataFrame({ + 'SNP': ['rs' + str(i) for i in xrange(1000)], + 'A1': ['A' for _ in xrange(1000)], + 'A2': ['G' for _ in xrange(1000)], + 'N': np.ones(1000) * N_INDIV +}) +for i in xrange(N_SIMS): + z = np.random.normal(size=N_SNP).reshape((N_SNP,)) + c = np.sqrt( + 1 + N_INDIV * (h21 * two_ldsc[:, 0] / float(M_two[0]) + h22 * two_ldsc[:, 1] / float(M_two[1]))) + z = np.multiply(z, c) + dfi = df.copy() + dfi['Z'] = z + dfi.reindex(np.random.permutation(dfi.index)) + dfi.to_csv('simulate_test/sumstats/' + str(i), + sep='\t', index=False, float_format='%.3f') diff --git a/test/simulate_test/ldscore/oneld_onefile.l2.M_5_50 b/test/simulate_test/ldscore/oneld_onefile.l2.M_5_50 new file mode 100644 index 00000000..8edde6a8 --- /dev/null +++ b/test/simulate_test/ldscore/oneld_onefile.l2.M_5_50 @@ -0,0 +1 @@ +155881.252584 diff --git a/test/simulate_test/ldscore/oneld_onefile.l2.ldscore b/test/simulate_test/ldscore/oneld_onefile.l2.ldscore new file mode 100644 index 00000000..255711fd --- /dev/null +++ b/test/simulate_test/ldscore/oneld_onefile.l2.ldscore @@ -0,0 +1,1001 @@ +BP CHR SNP LD +0 1.000 rs0 228.453 +1 1.000 rs1 51.900 +2 1.000 rs2 113.142 +3 1.000 rs3 179.999 +4 1.000 rs4 246.752 +5 1.000 rs5 424.542 +6 1.000 rs6 140.384 +7 1.000 rs7 224.589 +8 1.000 rs8 65.089 +9 1.000 rs9 225.396 +10 1.000 rs10 248.173 +11 1.000 rs11 70.453 +12 1.000 rs12 167.901 +13 1.000 rs13 64.765 +14 1.000 rs14 150.991 +15 1.000 rs15 162.883 +16 1.000 rs16 176.922 +17 1.000 rs17 154.338 +18 1.000 rs18 90.853 +19 1.000 rs19 333.406 +20 1.000 rs20 185.977 +21 1.000 rs21 144.110 +22 1.000 rs22 269.242 +23 1.000 rs23 149.052 +24 1.000 rs24 101.656 +25 1.000 rs25 127.132 +26 1.000 rs26 22.401 +27 1.000 rs27 77.670 +28 1.000 rs28 63.532 +29 1.000 rs29 101.029 +30 1.000 rs30 101.362 +31 1.000 rs31 116.543 +32 1.000 rs32 156.035 +33 1.000 rs33 289.745 +34 1.000 rs34 161.758 +35 1.000 rs35 43.515 +36 1.000 rs36 370.780 +37 1.000 rs37 99.394 +38 1.000 rs38 57.236 +39 1.000 rs39 103.844 +40 1.000 rs40 180.376 +41 1.000 rs41 127.076 +42 1.000 rs42 207.350 +43 1.000 rs43 192.819 +44 1.000 rs44 88.353 +45 1.000 rs45 78.805 +46 1.000 rs46 165.005 +47 1.000 rs47 273.659 +48 1.000 rs48 69.854 +49 1.000 rs49 138.637 +50 1.000 rs50 223.341 +51 1.000 rs51 154.719 +52 1.000 rs52 89.909 +53 1.000 rs53 234.980 +54 1.000 rs54 315.812 +55 1.000 rs55 135.773 +56 1.000 rs56 122.389 +57 1.000 rs57 297.543 +58 1.000 rs58 221.428 +59 1.000 rs59 141.581 +60 1.000 rs60 118.121 +61 1.000 rs61 140.502 +62 1.000 rs62 259.852 +63 1.000 rs63 64.179 +64 1.000 rs64 228.637 +65 1.000 rs65 26.597 +66 1.000 rs66 136.320 +67 1.000 rs67 166.608 +68 1.000 rs68 36.155 +69 1.000 rs69 164.816 +70 1.000 rs70 320.764 +71 1.000 rs71 260.080 +72 1.000 rs72 268.741 +73 1.000 rs73 76.205 +74 1.000 rs74 129.032 +75 1.000 rs75 395.709 +76 1.000 rs76 88.789 +77 1.000 rs77 69.997 +78 1.000 rs78 128.218 +79 1.000 rs79 378.561 +80 1.000 rs80 131.097 +81 1.000 rs81 147.267 +82 1.000 rs82 67.843 +83 1.000 rs83 42.370 +84 1.000 rs84 15.852 +85 1.000 rs85 551.978 +86 1.000 rs86 288.176 +87 1.000 rs87 76.928 +88 1.000 rs88 17.690 +89 1.000 rs89 63.093 +90 1.000 rs90 152.559 +91 1.000 rs91 214.179 +92 1.000 rs92 311.134 +93 1.000 rs93 195.679 +94 1.000 rs94 179.488 +95 1.000 rs95 63.407 +96 1.000 rs96 198.830 +97 1.000 rs97 231.598 +98 1.000 rs98 195.196 +99 1.000 rs99 291.239 +100 1.000 rs100 179.589 +101 1.000 rs101 83.436 +102 1.000 rs102 84.593 +103 1.000 rs103 248.166 +104 1.000 rs104 75.730 +105 1.000 rs105 71.558 +106 1.000 rs106 129.712 +107 1.000 rs107 70.082 +108 1.000 rs108 83.284 +109 1.000 rs109 177.848 +110 1.000 rs110 235.774 +111 1.000 rs111 380.632 +112 1.000 rs112 114.539 +113 1.000 rs113 189.711 +114 1.000 rs114 119.250 +115 1.000 rs115 44.151 +116 1.000 rs116 38.884 +117 1.000 rs117 264.443 +118 1.000 rs118 208.938 +119 1.000 rs119 151.070 +120 1.000 rs120 57.048 +121 1.000 rs121 224.029 +122 1.000 rs122 325.320 +123 1.000 rs123 63.813 +124 1.000 rs124 157.071 +125 1.000 rs125 61.607 +126 1.000 rs126 207.432 +127 1.000 rs127 295.325 +128 1.000 rs128 100.946 +129 1.000 rs129 208.932 +130 1.000 rs130 212.705 +131 1.000 rs131 336.958 +132 1.000 rs132 125.787 +133 1.000 rs133 229.808 +134 1.000 rs134 187.403 +135 1.000 rs135 230.223 +136 1.000 rs136 256.451 +137 1.000 rs137 347.281 +138 1.000 rs138 149.000 +139 1.000 rs139 53.484 +140 1.000 rs140 161.731 +141 1.000 rs141 212.162 +142 1.000 rs142 307.195 +143 1.000 rs143 169.209 +144 1.000 rs144 17.921 +145 1.000 rs145 112.988 +146 1.000 rs146 156.389 +147 1.000 rs147 185.958 +148 1.000 rs148 106.920 +149 1.000 rs149 190.925 +150 1.000 rs150 135.757 +151 1.000 rs151 119.747 +152 1.000 rs152 126.319 +153 1.000 rs153 235.792 +154 1.000 rs154 95.391 +155 1.000 rs155 130.445 +156 1.000 rs156 258.119 +157 1.000 rs157 36.589 +158 1.000 rs158 67.175 +159 1.000 rs159 89.678 +160 1.000 rs160 158.243 +161 1.000 rs161 162.980 +162 1.000 rs162 157.246 +163 1.000 rs163 106.494 +164 1.000 rs164 188.433 +165 1.000 rs165 108.766 +166 1.000 rs166 177.946 +167 1.000 rs167 268.678 +168 1.000 rs168 109.174 +169 1.000 rs169 359.127 +170 1.000 rs170 146.520 +171 1.000 rs171 297.876 +172 1.000 rs172 58.340 +173 1.000 rs173 185.911 +174 1.000 rs174 249.212 +175 1.000 rs175 160.376 +176 1.000 rs176 122.052 +177 1.000 rs177 190.901 +178 1.000 rs178 95.024 +179 1.000 rs179 251.075 +180 1.000 rs180 211.427 +181 1.000 rs181 222.925 +182 1.000 rs182 36.129 +183 1.000 rs183 41.340 +184 1.000 rs184 156.218 +185 1.000 rs185 209.448 +186 1.000 rs186 161.914 +187 1.000 rs187 169.708 +188 1.000 rs188 134.231 +189 1.000 rs189 152.029 +190 1.000 rs190 260.814 +191 1.000 rs191 233.143 +192 1.000 rs192 207.775 +193 1.000 rs193 195.337 +194 1.000 rs194 29.756 +195 1.000 rs195 308.857 +196 1.000 rs196 241.830 +197 1.000 rs197 179.765 +198 1.000 rs198 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10000.000 rs915 1.140 +A G 10000.000 rs916 1.320 +A G 10000.000 rs917 -0.046 +A G 10000.000 rs918 5.564 +A G 10000.000 rs919 3.539 +A G 10000.000 rs920 1.164 +A G 10000.000 rs921 -0.172 +A G 10000.000 rs922 2.220 +A G 10000.000 rs923 -0.144 +A G 10000.000 rs924 -7.829 +A G 10000.000 rs925 3.236 +A G 10000.000 rs926 4.500 +A G 10000.000 rs927 -0.704 +A G 10000.000 rs928 -5.718 +A G 10000.000 rs929 1.124 +A G 10000.000 rs930 4.542 +A G 10000.000 rs931 -0.955 +A G 10000.000 rs932 -2.033 +A G 10000.000 rs933 2.867 +A G 10000.000 rs934 -2.413 +A G 10000.000 rs935 -2.483 +A G 10000.000 rs936 1.087 +A G 10000.000 rs937 1.873 +A G 10000.000 rs938 -3.642 +A G 10000.000 rs939 2.275 +A G 10000.000 rs940 5.195 +A G 10000.000 rs941 -1.758 +A G 10000.000 rs942 2.356 +A G 10000.000 rs943 -0.351 +A G 10000.000 rs944 2.092 +A G 10000.000 rs945 3.145 +A G 10000.000 rs946 5.015 +A G 10000.000 rs947 -3.721 +A G 10000.000 rs948 -1.912 +A G 10000.000 rs949 -3.013 +A G 10000.000 rs950 0.295 +A G 10000.000 rs951 -2.440 +A G 10000.000 rs952 -5.230 +A G 10000.000 rs953 -1.428 +A G 10000.000 rs954 0.971 +A G 10000.000 rs955 1.724 +A G 10000.000 rs956 -2.459 +A G 10000.000 rs957 1.831 +A G 10000.000 rs958 0.726 +A G 10000.000 rs959 0.552 +A G 10000.000 rs960 3.739 +A G 10000.000 rs961 -1.665 +A G 10000.000 rs962 -2.298 +A G 10000.000 rs963 4.114 +A G 10000.000 rs964 -6.145 +A G 10000.000 rs965 -4.128 +A G 10000.000 rs966 0.190 +A G 10000.000 rs967 -2.528 +A G 10000.000 rs968 -2.155 +A G 10000.000 rs969 7.400 +A G 10000.000 rs970 1.420 +A G 10000.000 rs971 -2.658 +A G 10000.000 rs972 3.785 +A G 10000.000 rs973 -1.857 +A G 10000.000 rs974 2.330 +A G 10000.000 rs975 -2.623 +A G 10000.000 rs976 -3.330 +A G 10000.000 rs977 1.482 +A G 10000.000 rs978 0.532 +A G 10000.000 rs979 1.186 +A G 10000.000 rs980 -3.137 +A G 10000.000 rs981 -0.751 +A G 10000.000 rs982 0.439 +A G 10000.000 rs983 -2.199 +A G 10000.000 rs984 -5.862 +A G 10000.000 rs985 0.635 +A G 10000.000 rs986 -3.139 +A G 10000.000 rs987 -3.604 +A G 10000.000 rs988 0.019 +A G 10000.000 rs989 5.774 +A G 10000.000 rs990 -2.945 +A G 10000.000 rs991 0.927 +A G 10000.000 rs992 -3.948 +A G 10000.000 rs993 -0.432 +A G 10000.000 rs994 -0.862 +A G 10000.000 rs995 0.103 +A G 10000.000 rs996 -3.027 +A G 10000.000 rs997 0.275 +A G 10000.000 rs998 -2.816 +A G 10000.000 rs999 -3.215 diff --git a/test/test_gwas.py b/test/test_gwas.py deleted file mode 100644 index d3f6fb40..00000000 --- a/test/test_gwas.py +++ /dev/null @@ -1,381 +0,0 @@ -from __future__ import division -import ldscore.gwas as gw -import unittest -import numpy as np -import scipy.stats as spstats -import nose -from nose_parameterized import parameterized as param - -''' -TODO : currently no checks that insert() is working properly -possibly solution: add some attribute that indicates which indivs were inserted - -''' - -BIG_N = 15000 - -class test_QT_GWAS_basic(unittest.TestCase): - '''Test basic functions, e.g., that genotypes are correctly normalized, etc''' - def setUp(self): - self.M = 3 - self.mafs = [0.5, 0.5, 0.5] - self.betas = (1, -1, 0) - self.N = BIG_N - self.hsqs = 0.5 - self.gwas = gw.QT_GWAS(self.M, self.N, self.mafs, self.betas, self.hsqs) - - def test_init(self): - self.assertEqual(self.gwas.M, self.M) - self.assertEqual(self.gwas.N, self.N) - self.assertTrue( np.all(self.gwas.mafs == self.mafs) ) - self.assertEqual(self.hsqs, self.gwas.hsqs) - self.assertEqual(1 - self.hsqs, self.gwas.e_var) - self.assertTrue(np.abs(np.dot(self.gwas.betas.T, self.gwas.betas) - self.hsqs < 10**-6)) - - def test_bv(self): - fake_geno = np.ones((self.N,self.M)) - self.assertTrue(np.all(self.gwas.__bv__(fake_geno) == np.dot(fake_geno, self.gwas.betas)) ) - - @param.expand(([1,2,3], - [-4,5,1], - [11,12,23])) - def test_calc_pheno(self, bv, ev, correct_pheno): - self.assertEqual(self.gwas.__calc_pheno__(bv, ev), correct_pheno) - - def test_geno(self): - self.gwas.sample() - geno = self.gwas.geno - self.assertEqual(geno.shape, (self.N, self.M)) - self.assertTrue(np.all(abs(geno.mean(axis=0)) < 0.1)) - self.assertTrue(np.all(abs(geno.var(axis=0)-1) < 0.05)) - - def test_hsq_0(self): - self.gwas = gw.QT_GWAS(self.M, self.N, self.mafs, self.betas, 0) - self.gwas.sample() - self.assertTrue(np.all(self.gwas.bv == np.zeros(self.M))) - self.assertTrue(np.abs(np.var(self.gwas.pheno) - 1) < 0.2) - self.assertTrue(np.abs(np.var(self.gwas.ev) - 1) < 0.2) - self.assertTrue(np.all(self.gwas.pheno == self.gwas.ev)) - - def test_hsq_1(self): - self.gwas = gw.QT_GWAS(self.M, self.N, self.mafs, self.betas, 1) - self.gwas.sample() - self.assertTrue(np.all(self.gwas.ev == np.zeros(self.M))) - self.assertTrue(np.abs(np.var(self.gwas.pheno) - 1) < 0.2) - self.assertTrue(np.abs(np.var(self.gwas.bv) - 1) < 0.2) - self.assertTrue(np.all(self.gwas.pheno == self.gwas.bv)) - - def test_sumstats(self): - self.gwas.sample() - betahat = self.gwas.betahat - chisq = self.gwas.chisq - self.assertEqual(chisq.shape, (3,1)) - self.assertEqual(betahat.shape, (3,1)) - self.assertTrue(chisq[0] > 1000) - self.assertTrue(chisq[1] > 1000) - self.assertTrue(chisq[2] < 30) - - -class test_QT_GWAS_vectorized(unittest.TestCase): - '''Test with multiple phenotypes in a single GWAS''' - def setUp(self): - self.M = 3; - self.N = BIG_N - self.mafs=[0.5,0.5,0.5] - self.betas = np.matrix((1,0, -1,1, 0,-1)).reshape((self.M, 2)) - self.hsqs = np.matrix((0.9,0.5)) - self.e_var = np.matrix((0.1,0.03,0.03,0.5)).reshape((2,2)) - self.gwas = gw.QT_GWAS(self.M, self.N, self.mafs, self.betas, self.hsqs, self.e_var) - - def test_init(self): - self.assertEqual(self.betas.shape, (self.M, 2)) - bb = np.diag(np.dot(self.gwas.betas.T, self.gwas.betas)) - self.assertTrue(np.all(np.abs(bb - self.hsqs < 10**-6))) - - def test_shapes(self): - self.gwas.sample() - self.assertEqual(self.gwas.pheno.shape, (self.N, 2)) - self.assertEqual(self.gwas.bv.shape, (self.N, 2)) - self.assertEqual(self.gwas.ev.shape, (self.N, 2)) - self.assertEqual(self.gwas.geno.shape, (self.N, self.M)) - self.assertEqual(self.gwas.betas.shape, (self.M, 2)) - self.assertEqual(self.gwas.hsqs.shape, (1,2)) - self.assertEqual(self.gwas.e_var.shape, (2,2)) - - def test_vars(self): - self.gwas.sample() - p1_cov = np.var(self.gwas.pheno[:,0]) - p2_cov = np.var(self.gwas.pheno[:,1]) - self.assertTrue(np.abs(p1_cov - 1) < 0.2) - self.assertTrue(np.abs(p2_cov - 1) < 0.2) - ev_cov = np.cov(self.gwas.ev.T) - self.assertTrue(np.all(np.abs(ev_cov - self.e_var) < 0.1)) - - def test_sumstats(self): - chisq = self.gwas.chisq - betahat = self.gwas.betahat - self.assertEqual(chisq.shape, (self.M,1)) - self.assertEqual(betahat.shape, (self.M,1)) - self.assertTrue(chisq[0] > 1000) - self.assertTrue(chisq[1] > 1000) - self.assertTrue(chisq[2] < 30) - - -class test_QT_insert(unittest.TestCase): - - def setUp(self): - mafs = np.ones(10)/2 - betas1 = np.matrix(np.random.normal(size=20)).reshape((10,2)) - betas2 = betas1[:,1] - hsqs1 = np.array((0.5, 0.9)) - hsqs2 = 0.9 - e_var1 = np.matrix((0.5,0.03,0.03,0.1)).reshape((2,2)) - self.gwas1 = gw.QT_GWAS(10, 5, mafs, betas1, hsqs1, e_var1) - self.gwas1.sample() - self.gwas2 = gw.QT_GWAS(10, 10, mafs, betas2, hsqs2) - self.extract = self.gwas1.__extract__(np.arange(5), 1) - self.gwas2.__insert__(self.extract) ### TODO this interface sucks - self.gwas2.sample() - - def test_ev(self): - self.assertTrue(np.all(self.gwas2.ev[0:5,0] == self.gwas1.ev[:,1])) - - def test_bv(self): - self.assertTrue(np.all(self.gwas2.bv[0:5,0] == self.gwas1.bv[:,1])) - - def test_geno(self): - self.assertTrue(np.all(self.gwas2.geno[0:5,:] == self.gwas1.geno)) - - -class test_CC_GWAS(unittest.TestCase): - - def setUp(self): - self.M = 3 - self.mafs = [0.5, 0.5, 0.5] - self.betas = (1, -1, 0) - self.N_cas = 10; self.N_con = 10 - self.P = 0.5 - self.hsqs = 0.5 - self.chunk_size = 10 - self.gwas = gw.CC_GWAS(self.M, self.N_cas, self.N_con, self.mafs, self.betas, - self.hsqs, self.P, chunk_size=self.chunk_size) - self.gwas.sample() - - @param.expand(([0.01, 2.3263478740408408, 0.02665214220345808], - [0.1, 1.2815515655446004, 0.17549833193248685], - [0.5, 0.0, 0.3989422804014327])) - def test_z_thresh(self, P, correct_thresh, correct_z): - gwas = gw.CC_GWAS(self.M, self.N_cas, self.N_con, self.mafs, self.betas, self.hsqs, - P, chunk_size=self.chunk_size) - self.assertEqual(gwas.thresh, correct_thresh) - self.assertEqual(gwas.__z__, correct_z) - - def test_init(self): - self.assertEqual(self.gwas.P, self.P) - self.assertEqual(self.gwas.N_cas, self.N_cas) - self.assertEqual(self.gwas.N_con, self.N_con) - self.assertEqual(self.gwas.N, self.N_cas+self.N_con) - self.assertEqual(self.gwas.K, self.N_cas/(self.N_cas + self.N_con)) - self.assertEqual(self.gwas.chunk_size, self.chunk_size) - - @param.expand(([0.5,0.5,1], [-0.4,3,1], [-2,-2,0], [0,0.1,1])) - def test_calc_pheno(self, bv, ev, correct_pheno): - self.assertEqual(self.gwas.__calc_pheno__(bv, ev), correct_pheno) - - @param.expand(( [ (0.25,0.25), (0.25,0.25), (1,0) ], - [ (-0.25,1), (-0.25,1), (0,1) ], - [ (5,4), (3,2), (1,1) ], - [ (-0.25,-1), (-0.25,-1), (0,0) ] - )) - def test_calc_pheno_vectorized(self, bv, ev, correct_pheno): - bv = np.matrix(bv).reshape((1,2)) - ev = np.matrix(ev).reshape((1,2)) - correct_pheno = np.matrix(correct_pheno).reshape((1,2)) - self.gwas.thresh = np.matrix(spstats.norm.isf((0.5,0.1))).reshape((1,2)) - self.assertTrue(np.all(self.gwas.__calc_pheno__(bv, ev) == correct_pheno)) - - @param.expand(([np.matrix((1,0,1)).reshape((3,1)), np.array((True,False,True))], - [np.matrix((0,0,0)).reshape((3,1)), np.array((False,False,False))], - [np.matrix((1,1,1)).reshape((3,1)), np.array((True,True,True))], - [np.matrix((1,0,1,0,1,0)).reshape((3,2)), np.array((True,True,True))], - [np.matrix((0,0,0,1,0,1)).reshape((3,2)), np.array((False,False,False))] - )) - def test_ascertain_cases(self, phenos, correct_phenos): - draw = (0,0,0,phenos) - self.assertTrue(np.all(self.gwas.__ascertain_cases__(draw) == correct_phenos)) - - @param.expand(([np.matrix((0,0,1)).reshape((3,1))], - [np.matrix((0,0,0)).reshape((3,1))], - [np.matrix((1,1,1)).reshape((3,1))], - [np.matrix((0,0,0,0,1,0)).reshape((3,2))], - [np.matrix((0,0,0,1,0,1)).reshape((3,2))], - [np.matrix((1,0,0,1,0,1)).reshape((3,2))] - )) - def test_ascertain_controls(self, phenos): - draw = (0,0,0,phenos) - correct_phenos = np.squeeze(np.asarray(np.logical_not(np.prod(phenos,axis=1)))) - self.assertTrue(np.all(self.gwas.__ascertain_controls__(draw) == correct_phenos)) - - -class test_QT_QT_GWAS(unittest.TestCase): - - def setUp(self): - self.M = 3 - self.mafs = [0.5, 0.5, 0.5] - self.beta1 = (1, -1, 0) - self.beta2 = (0,-1,1) - self.N1 = BIG_N - self.N2 = BIG_N + 1 - self.hsq1 = 0.9 - self.hsq2 = 0.5 - self.e_var = np.matrix((0.1,0.03,0.03,0.5)).reshape((2,2)) - - @param.expand(([0],[5],[10])) - def test_init(self, overlap): - self.gwas = gw.QT_QT_GWAS(self.M, self.N1, self.N2, self.mafs, self.beta1, self.beta2, - self.hsq1, self.hsq2, self.e_var, overlap) - self.gwas.sample() - self.assertEqual(self.N1, self.gwas.gwas1.N) - self.assertEqual(self.N2, self.gwas.gwas2.N) - self.assertEqual(self.M, self.gwas.gwas1.M) - self.assertEqual(self.M, self.gwas.gwas2.M) - self.assertTrue(np.all(self.mafs == self.gwas.gwas1.mafs)) - self.assertTrue(np.all(self.mafs == self.gwas.gwas2.mafs)) - self.assertTrue(np.all(np.matrix((self.hsq1, self.hsq2)) == self.gwas.gwas1.hsqs)) - self.assertEqual(self.hsq2, self.gwas.gwas2.hsqs) - self.assertEqual(overlap, self.gwas.overlap) - self.assertTrue(np.all(self.e_var == self.gwas.gwas1.e_var)) - self.assertEqual(self.e_var[1,1], self.gwas.gwas2.e_var) - - @param.expand(([0],[5],[10])) - def test_sample(self, overlap): - self.gwas = gw.QT_QT_GWAS(self.M, self.N1, self.N2, self.mafs, self.beta1, self.beta2, - self.hsq1, self.hsq2, self.e_var, overlap) - self.gwas.sample() - self.assertEqual(self.gwas.gwas1.__current_N__, self.gwas.gwas1.N) - self.assertEqual(self.gwas.gwas2.__current_N__, self.gwas.gwas2.N) - - @param.expand(([0],[5],[10])) - def test_sumstats(self, overlap): - self.gwas = gw.QT_QT_GWAS(self.M, self.N1, self.N2, self.mafs, self.beta1, self.beta2, - self.hsq1, self.hsq2, self.e_var, overlap) - chisq = self.gwas.chisq - betahat = self.gwas.betahat - self.assertEqual(chisq.shape, (self.M,2)) - self.assertEqual(betahat.shape, (self.M,2)) - self.assertTrue(chisq[0,0] > 1000) - self.assertTrue(chisq[1,0] > 1000) - self.assertTrue(chisq[2,0] < 30) - self.assertTrue(chisq[0,1] < 30) - self.assertTrue(chisq[1,1] > 500) - self.assertTrue(chisq[2,1] > 500) - - -class test_QT_CC_GWAS(unittest.TestCase): - - def setUp(self): - self.M = 3 - self.mafs = [0.5, 0.5, 0.5] - self.beta1 = (1, -1, 0) - self.beta2 = (0,-1,1) - self.N1 = BIG_N - self.N_cas = 1000 - self.N_con = 1000 - self.hsq1 = 0.8 - self.hsq2 = 0.9 - self.P = 0.5 - self.e_var = np.matrix((0.2,0.03,0.03,0.1)).reshape((2,2)) - self.overlap = 10 - self.chunk_size = 100 - - @param.expand(([0],[1000],[2000])) - def test_init(self, overlap): - self.gwas = gw.QT_CC_GWAS(self.M, self.N1, self.N_cas, self.N_con, self.mafs, - self.beta1, self.beta2, self.hsq1, self.hsq2, self.P, self.e_var, overlap, - self.chunk_size) - self.gwas.sample() - self.assertEqual(self.N1, self.gwas.gwas1.N) - self.assertEqual(self.N_cas + self.N_con, self.gwas.gwas2.N) - self.assertEqual(self.M, self.gwas.gwas1.M) - self.assertEqual(self.M, self.gwas.gwas2.M) - self.assertTrue(np.all(self.mafs == self.gwas.gwas1.mafs)) - self.assertTrue(np.all(self.mafs == self.gwas.gwas2.mafs)) - self.assertTrue(np.all(np.matrix((self.hsq1, self.hsq2)) == self.gwas.gwas1.hsqs)) - self.assertEqual(self.hsq2, self.gwas.gwas2.hsqs) - self.assertEqual(overlap, self.gwas.overlap) - self.assertTrue(np.all(self.e_var == self.gwas.gwas1.e_var)) - self.assertTrue(self.e_var[1,1] - self.gwas.gwas2.e_var < 10**-6) - - @param.expand(([0],[1000],[2000])) - def test_sample(self, overlap): - self.gwas = gw.QT_CC_GWAS(self.M, self.N1, self.N_cas, self.N_con, self.mafs, - self.beta1, self.beta2, self.hsq1, self.hsq2, self.P, self.e_var, overlap, - self.chunk_size) - self.gwas.sample() - self.assertEqual(self.gwas.gwas2.__current_N_cas__, self.N_cas) - self.assertEqual(self.gwas.gwas2.__current_N_con__, self.N_con) - self.assertEqual(self.gwas.gwas2.__current_N__, self.N_cas + self.N_con) - - @param.expand(([0],[1000],[2000])) - def test_sumstats(self, overlap): - self.gwas = gw.QT_CC_GWAS(self.M, self.N1, self.N_cas, self.N_con, self.mafs, - self.beta1, self.beta2, self.hsq1, self.hsq2, self.P, self.e_var, overlap, - self.chunk_size) - self.gwas.sample() - chisq = self.gwas.chisq - betahat = self.gwas.betahat - self.assertEqual(chisq.shape, (self.M,2)) - self.assertEqual(betahat.shape, (self.M,2)) - self.assertTrue(chisq[0,0] > 1000) - self.assertTrue(chisq[1,0] > 1000) - self.assertTrue(chisq[2,0] < 30) - self.assertTrue(chisq[0,1] < 30) - self.assertTrue(chisq[1,1] > 50) - self.assertTrue(chisq[2,1] > 50) - -''' -def __init__(self,M,N_cas,N_con,mafs,betas,hsq,e_var,P,overlap,chunk_size=4000): - -''' - -class test_CC_CC_GWAS(unittest.TestCase): - - def setUp(self): - self.M = 3 - self.N_cas = np.array((100, 101)) - self.N_con = np.array((102, 103)) - self.mafs = [0.5, 0.5, 0.5] - self.betas = np.matrix((1,0,-1,1,0,1)).reshape((3,2)) - self.hsqs = np.array((0.99,0.95)) - self.P = np.array((0.4,0.3)) - self.chunk_size = 40 - self.e_var = np.matrix((0.01,0,0,0.05)).reshape((2,2)) - - @param.expand(([0],[50],[101])) - def test_init(self, overlap): - self.gwas = gw.CC_CC_GWAS(self.M, self.N_cas, self.N_con, self.mafs, self.betas, - self.hsqs,self.P, self.e_var, overlap, self.chunk_size) - self.gwas.sample() - self.assertEqual(self.N_cas[0], self.gwas.gwas1.N_cas) - self.assertEqual(self.N_cas[1], self.gwas.gwas2.N_cas) - self.assertEqual(self.N_con[0], self.gwas.gwas1.N_con) - self.assertEqual(self.N_con[1], self.gwas.gwas2.N_con) - self.assertEqual(self.M, self.gwas.gwas1.M) - self.assertEqual(self.M, self.gwas.gwas2.M) - self.assertTrue(np.all(self.mafs == self.gwas.gwas1.mafs)) - self.assertTrue(np.all(self.mafs == self.gwas.gwas2.mafs)) - self.assertEqual(overlap, self.gwas.overlap) - self.assertTrue(np.all(self.gwas.gwas1.e_var == self.e_var)) - self.assertTrue(np.all(self.gwas.gwas2.e_var == self.e_var)) - - @param.expand(([0],[50],[101])) - def test_sample(self, overlap): - self.gwas = gw.CC_CC_GWAS(self.M, self.N_cas, self.N_con, self.mafs, self.betas, - self.hsqs,self.P, self.e_var, overlap, self.chunk_size) - self.gwas.sample() - self.assertEqual(self.gwas.gwas1.__current_N_cas__, self.N_cas[0]) - self.assertEqual(self.gwas.gwas1.__current_N_con__, self.N_con[0]) - self.assertEqual(self.gwas.gwas1.__current_N__, self.N_cas[0] + self.N_con[0]) - self.assertEqual(self.gwas.gwas2.__current_N_cas__, self.N_cas[1]) - self.assertEqual(self.gwas.gwas2.__current_N_con__, self.N_con[1]) - self.assertEqual(self.gwas.gwas2.__current_N__, self.N_cas[1] + self.N_con[1]) \ No newline at end of file diff --git a/test/test_irwls.py b/test/test_irwls.py new file mode 100644 index 00000000..7bfe7c4c --- /dev/null +++ b/test/test_irwls.py @@ -0,0 +1,69 @@ +from __future__ import division +from ldscore.irwls import IRWLS +import unittest +import numpy as np +import nose +from numpy.testing import assert_array_equal, assert_array_almost_equal +from nose.tools import assert_raises + + +class Test_IRWLS_2D(unittest.TestCase): + + def setUp(self): + self.x = np.vstack([np.ones(4), [1, 4, 3, 2]]).T + self.y = np.sum(self.x, axis=1).reshape((4, 1)) + self.w = np.abs(np.random.normal(size=4).reshape((4, 1))) + self.w = self.w / np.sum(self.w) + self.update_func = lambda x: np.ones((4, 1)) + print 'w=\n', self.w + + def test_weight_2d(self): + x = np.ones((4, 2)) + assert_array_almost_equal( + IRWLS._weight(x, self.w), np.hstack([self.w, self.w])) + + def test_wls_2d(self): + z = IRWLS.wls(self.x, self.y, self.w) + assert_array_almost_equal(z[0], np.ones((2, 1))) + + def test_irwls_2d(self): + z = IRWLS.irwls(self.x, self.y, self.update_func, 2, self.w) + assert_array_equal(z.est.shape, (1, 2)) + assert_array_almost_equal(z.est, np.ones((1, 2))) + + def test_integrates(self): + z = IRWLS(self.x, self.y, self.update_func, 2) + assert_array_equal(z.est.shape, (1, 2)) + assert_array_almost_equal(z.est, np.ones((1, 2))) + + +class Test_IRWLS_1D(unittest.TestCase): + + def setUp(self): + self.x = np.ones((4, 1)) + self.y = np.ones((4, 1)) + self.w = np.abs(np.random.normal(size=4).reshape((4, 1))) + self.w = self.w / np.sum(self.w) + self.update_func = lambda x: np.ones((4, 1)) + print 'w=\n', self.w + + def test_weight_1d(self): + assert_array_almost_equal(IRWLS._weight(self.x, self.w), self.w) + + def test_neg_weight(self): + self.w *= 0 + assert_raises(ValueError, IRWLS._weight, self.x, self.w) + + def test_wls_1d(self): + z = IRWLS.wls(self.x, self.y, self.w) + assert_array_almost_equal(z[0], 1) + + def test_irwls_1d(self): + z = IRWLS.irwls(self.x, self.y, self.update_func, 2, self.w) + assert_array_equal(z.est.shape, (1, 1)) + assert_array_almost_equal(z.est, 1) + + def test_integrated(self): + z = IRWLS(self.x, self.y, self.update_func, 2) + assert_array_equal(z.est.shape, (1, 1)) + assert_array_almost_equal(z.est, 1) diff --git a/test/test_jackknife.py b/test/test_jackknife.py index 760c07a4..4ec075e8 100644 --- a/test/test_jackknife.py +++ b/test/test_jackknife.py @@ -3,246 +3,265 @@ import unittest import numpy as np import nose -from nose_parameterized import parameterized as param - - -class TestLstsqJackknife(unittest.TestCase): - - def setUp(self): - pass - - @param.expand(( - [np.array([1,2,3,4,5]), 10], - [np.array([0,-1,-2,0,100]),20], - [np.array([1,1,1,1,1]),100] - )) - def test_2d_reg_exact(self, beta, num_blocks): - x = np.matrix(np.random.normal(size=500).reshape((100,5))) - x += 10 - y = np.dot(x, beta).T - j = jk.LstsqJackknife(x, y, num_blocks) - assert np.all(np.abs(j.est - beta) < 10e-6) - self.assertEqual(j.est.shape, (1,5) ) - assert np.all(np.abs(j.jknife_est - beta) < 1e-6) - self.assertEqual(j.jknife_est.shape, (1,5) ) - assert np.all(np.abs(j.jknife_se) < 1e-6) - self.assertEqual(j.jknife_se.shape, (1,5) ) - assert np.all(np.abs(j.jknife_var) < 1e-6) - self.assertEqual(j.jknife_var.shape, (1,5) ) - assert np.all(np.abs(j.jknife_cov) < 1e-6) - self.assertEqual(j.jknife_cov.shape, (5,5) ) - - @param.expand(( - [np.array([0,0,1,0,0]), 10], - [np.array([11,1.123,-233,0,100]),20], - [np.array([14,15,1,1,1]),100] - )) - def test_2d_reg_noise(self, beta, num_blocks): - x = np.matrix(np.random.normal(size=500, loc=-10).reshape((100,5))) - y = np.dot(x, beta).T - y += np.random.normal(size=100, scale=1e-6).reshape((100,1)) - j = jk.LstsqJackknife(x, y, num_blocks) - assert np.all(np.abs(j.est - beta) < 1e-6) - assert np.all(np.abs(j.jknife_est - beta) < 1e-6) - assert np.all(np.abs(j.jknife_se) < 1e-3) - assert np.all(np.abs(j.jknife_var) < 1e-6) - assert np.all(np.abs(j.jknife_cov) < 1e-6) - - def test_block_action(self): - x = np.array((-1,0,1,-1,0,1,-1,0,1,)) - y = np.array((-1,0,1, -2,0,2, -3,0,3 )) - j = jk.LstsqJackknife(x, y, 3) - xtx_vals = j.block_vals[1] - xty_vals = j.block_vals[0] - block_vals = np.array(xty_vals) / np.array(xtx_vals) - assert np.all( np.abs(block_vals.reshape(3) - (1,2,3)) < 1e-6) - - def test_autocor(self): - x = np.matrix(np.random.normal(size=500, loc=-10).reshape((100,5))) - y = np.sum(x, axis=1) - y += np.random.normal(size=100, scale=1e-6).reshape((100,1)) - j = jk.LstsqJackknife(x, y, 20) - j.autocor(1) - - -class Test_RatioJackknife_1D(unittest.TestCase): - - def setUp(self): - self.numer_delete_vals = np.matrix(np.arange(1,11)).T - self.denom_delete_vals = - np.matrix(np.arange(1,11)).T; self.denom_delete_vals[9,0] += 1 - self.est = np.matrix(-1) - self.num_blocks = self.numer_delete_vals.shape[0] - self.jknife = jk.RatioJackknife(self.est, self.numer_delete_vals, self.denom_delete_vals) - - def test_shapes(self): - self.assertEqual(self.jknife.est, self.est) - self.assertEqual(self.jknife.est.shape, (1,1)) - self.assertEqual(self.jknife.numer_delete_vals.shape, (self.num_blocks,1)) - assert np.all(self.jknife.numer_delete_vals == self.numer_delete_vals) - self.assertEqual(self.jknife.denom_delete_vals.shape, (self.num_blocks,1)) - assert np.all(self.jknife.denom_delete_vals == self.denom_delete_vals) - self.assertEqual(self.jknife.num_blocks, self.num_blocks) - self.assertEqual(self.jknife.pseudovalues.shape, self.jknife.numer_delete_vals.shape) - - def test_jknife(self): - assert np.all(self.jknife.pseudovalues[0:9,:] == -1) - self.assertEqual(self.jknife.pseudovalues[9,:], 0) - assert np.abs(self.jknife.jknife_est[0,0] + 0.9) < 1e-6 - assert np.abs(self.jknife.jknife_se - 0.1) < 1e-6 - assert np.abs(self.jknife.jknife_var - 0.01) < 1e-6 - assert np.abs(self.jknife.jknife_cov - 0.01) < 1e-6 - - -class Test_RatioJackknife_2D(unittest.TestCase): - - def setUp(self): - self.numer_delete_vals = np.matrix(np.vstack((np.arange(1,11), 2*np.arange(1,11)))).T - x = - np.arange(1,11); x[9] += 1 - self.denom_delete_vals = np.vstack((x,4*x)).T - self.est = np.matrix((-1, -0.5)) - self.num_blocks = self.numer_delete_vals.shape[0] - self.jknife = jk.RatioJackknife(self.est, self.numer_delete_vals, self.denom_delete_vals) - - def test_shapes(self): - assert np.all(self.jknife.est == self.est) - self.assertEqual(self.jknife.est.shape, (1,2)) - self.assertEqual(self.jknife.numer_delete_vals.shape, (self.num_blocks,2)) - assert np.all(self.jknife.numer_delete_vals == self.numer_delete_vals) - self.assertEqual(self.jknife.denom_delete_vals.shape, (self.num_blocks,2)) - print self.jknife.denom_delete_vals - assert np.all(self.jknife.denom_delete_vals == self.denom_delete_vals) - self.assertEqual(self.jknife.num_blocks, self.num_blocks) - self.assertEqual(self.jknife.pseudovalues.shape, self.jknife.numer_delete_vals.shape) - - def test_jknife(self): - assert np.all(self.jknife.pseudovalues[0:9,0] == -1) - self.assertEqual(self.jknife.pseudovalues[9,0], 0) - assert np.all(self.jknife.pseudovalues[0:9,1] == -0.5) - self.assertEqual(self.jknife.pseudovalues[9,1], 0) - assert np.all(np.abs(self.jknife.jknife_est + (0.9, 0.45)) < 1e-6) - assert np.all(np.abs(self.jknife.jknife_se - (0.1,0.05)) < 1e-6) - assert np.all(np.abs(self.jknife.jknife_var - (0.01,0.0025)) < 1e-6) - assert np.all(np.abs(self.jknife.jknife_cov - np.matrix(((0.01,0.005),(0.005,0.0025)))) < 1e-6) - - -class Test_append_intercept(unittest.TestCase): - - def test_simple(self): - x = np.matrix(np.ones(10).reshape((5,2))) - correct_x = np.matrix(np.ones(15).reshape((5,3))) - assert np.all(jk._append_intercept(x) == correct_x) - - -class Test_weight(unittest.TestCase): - - def test_symmetric(self): - x = np.matrix(np.ones((2,2))) - w = np.matrix((0.25,0.75)).T - correct_x = np.matrix(( (0.5, 0.5), (0.8660254, 0.8660254)) ) - print jk._weight(x, w) - assert np.all(np.abs(jk._weight(x, w) - correct_x) < 1e-6) - - def test_asymmetric(self): - x = np.matrix(np.ones((10,2))) - w = np.matrix(np.arange(10)+1 ).T - wp = np.sqrt(w / float(np.sum(w))) - correct_x = np.hstack((wp,wp)) - print jk._weight(x, w) - print correct_x - assert np.all(np.abs(jk._weight(x, w) - correct_x) < 1e-6) - - @param.expand(( - [10,22,np.arange(10)+1, 0.5], - [3,33,np.ones(10)+1, 1], - )) - def test_weights(self, N, M, ld, hsq): - x = jk._hsq_weights(ld, np.ones_like(ld), N, M, hsq) - assert np.all( np.abs(x - 1 / (1 + hsq*N*ld/M)**2) < 1e-6) - - -class Test_Hsq_1D(unittest.TestCase): - - def setUp(self): - self.ldScores = np.matrix(np.arange(20)).reshape((20,1)) - self.w = np.matrix(np.ones(20)).reshape((20,1)) - self.y = np.matrix(np.arange(20)).reshape((20,1)) - self.N = np.matrix(np.ones(20)).reshape((20,1)) - self.M = np.matrix(1) - self.num_blocks = 10 - - def test_reg(self): - x = jk.Hsq(self.y, self.ldScores, self.w, self.N, self.M, num_blocks=self.num_blocks) - self.assertTrue(np.all( np.abs(x.tot_hsq - 1) < 1e-6) ) - self.assertTrue(np.all( np.abs(x.tot_hsq_se - 0) < 1e-6) ) - self.assertTrue(np.all( np.abs(x._jknife.jknife_var - 0) < 1e-6)) - - -class Test_Hsq_2D(unittest.TestCase): - - def setUp(self): - self.ldScores = np.matrix(np.vstack((np.arange(6), (0,-2,4,3,-1,-8))).T).reshape((6,2)) - self.y = np.sum(self.ldScores, axis=1) - self.w = np.matrix(np.ones(6)).reshape((6,1)) - self.num_blocks = 2 - self.N = np.matrix(np.ones(6)).reshape((6,1)) - self.M = np.matrix((1,1)).reshape((1,2)) - - def test_reg(self): - x = jk.Hsq(self.y, self.ldScores, self.w, self.N, self.M, num_blocks=self.num_blocks) - self.assertTrue(np.all( np.abs(x.cat_hsq[0,0:2] - (1,1)) < 1e-6) ) - self.assertTrue(np.all( np.abs(x.cat_hsq_se[0,0:2] - (0,0)) < 1e-6) ) - self.assertTrue(np.all( np.abs(x._jknife.jknife_var[0,0:2] - (0,0)) < 1e-6)) - self.assertTrue(np.all( np.abs(x._jknife.jknife_est[0,0:2] - (1,1)) < 1e-6)) - - -class Test_Hsq_Agg_1D(unittest.TestCase): - - def setUp(self): - M = 1000 - self.ldScores = np.matrix(np.random.random(M)).reshape((M,1)) - self.y = self.ldScores+1 - self.w = np.matrix(np.ones(M)).reshape((M,1)) - self.num_blocks = max(2,int(M/10)) - self.N = np.matrix(np.ones(M)).reshape((M,1)) - self.M = np.matrix(1) - self.annot_matrix = np.matrix(np.ones(M)).reshape((M,1)) - - def test_reg(self): - x = jk.Hsq_aggregate(self.y, self.ldScores, self.w, self.N, self.M, self.annot_matrix, num_blocks=self.num_blocks) - self.assertTrue(np.all( np.abs(x.tot_hsq - 1) < 1e-6) ) - self.assertTrue(np.all( np.abs(x.tot_hsq_se - 0) < 1e-6) ) - self.assertTrue(np.all( np.abs(x._jknife.jknife_var - 0) < 1e-6)) - -class Test_Hsq_Agg_2D(unittest.TestCase): - - def setUp(self): - M = 1000 - self.ldScores = np.matrix(np.vstack((np.random.random(M), np.random.random(M)))).reshape((M,2)) - self.y = np.sum(self.ldScores, axis=1)+1 - self.w = np.matrix(np.ones(M)).reshape((M,1)) - self.num_blocks = max(2,int(M/10)) - self.N = np.matrix(np.ones(M)).reshape((M,1)) - self.M = np.matrix((1,1)).reshape((1,2)) - self.annot_matrix = np.matrix([0,1,1,0]*(int(M/2))).reshape(M,2) - - def test_reg(self): - x = jk.Hsq_aggregate(self.y, self.ldScores, self.w, self.N, self.M, self.annot_matrix, num_blocks=self.num_blocks) - self.assertTrue(np.all( np.abs(x.cat_hsq[0,0:2] - (1,1)) < 1e-6) ) - self.assertTrue(np.all( np.abs(x.cat_hsq_se[0,0:2] - (0,0)) < 1e-6) ) - self.assertTrue(np.all( np.abs(x._jknife.jknife_var[0,0:2] - (0,0)) < 1e-6)) - self.assertTrue(np.all( np.abs(x._jknife.jknife_est[0,0:2] - (1,1)) < 1e-6)) - -''' -class Test_gencov_weights(unittest.TestCase): - - -def test_basic(self): - pass - - - -class Test_obs_to_liab(unittest.TestCase): - - pass - -''' +from numpy.testing import assert_array_equal, assert_array_almost_equal +from nose.tools import assert_raises + + +class Test_Jackknife(unittest.TestCase): + + def test_separators(self): + N = 20 + x = np.arange(N) + for i in xrange(2, int(np.floor(N / 2))): + s = jk.Jackknife.get_separators(N, i) + lengths = [len(x[s[j]:s[j + 1]]) for j in xrange(len(s) - 2)] + + self.assertTrue(max(lengths) - min(lengths) <= 1) + + def test_jknife_1d(self): + pseudovalues = np.atleast_2d(np.arange(10)).T + (est, var, se, cov) = jk.Jackknife.jknife(pseudovalues) + nose.tools.assert_almost_equal(var, 0.91666667) + nose.tools.assert_almost_equal(est, 4.5) + nose.tools.assert_almost_equal(cov, var) + nose.tools.assert_almost_equal(se ** 2, var) + self.assertTrue(not np.any(np.isnan(cov))) + assert_array_equal(cov.shape, (1, 1)) + assert_array_equal(var.shape, (1, 1)) + assert_array_equal(est.shape, (1, 1)) + assert_array_equal(se.shape, (1, 1)) + + def test_jknife_2d(self): + pseudovalues = np.vstack([np.arange(10), np.arange(10)]).T + (est, var, se, cov) = jk.Jackknife.jknife(pseudovalues) + assert_array_almost_equal(var, np.array([[0.91666667, 0.91666667]])) + assert_array_almost_equal(est, np.array([[4.5, 4.5]])) + assert_array_almost_equal( + cov, np.matrix([[0.91666667, 0.91666667], [0.91666667, 0.91666667]])) + assert_array_almost_equal(se ** 2, var) + assert_array_equal(cov.shape, (2, 2)) + assert_array_equal(var.shape, (1, 2)) + assert_array_equal(est.shape, (1, 2)) + assert_array_equal(se.shape, (1, 2)) + + def test_delete_to_pseudo(self): + for dim in [1, 2]: + est = np.ones((1, dim)) + delete_values = np.ones((20, dim)) + x = jk.Jackknife.delete_values_to_pseudovalues(delete_values, est) + assert_array_equal(x, np.ones_like(delete_values)) + + est = est.T + nose.tools.assert_raises( + ValueError, jk.Jackknife.delete_values_to_pseudovalues, delete_values, est) + + +class Test_LstsqJackknifeSlow(unittest.TestCase): + + def test_delete_values_1d(self): + func = lambda x, y: np.atleast_2d(np.sum(x + y)) + s = [0, 5, 10] + x = np.atleast_2d(np.arange(10)).T + y = np.atleast_2d(np.arange(10)).T + p = jk.LstsqJackknifeSlow.delete_values(x, y, func, s) + # 2 blocks, 1D data + assert_array_equal(p.shape, (2, 1)) + assert_array_almost_equal(p, [[70], [20]]) + + def test_delete_values_2d_1(self): + func = lambda x, y: np.atleast_2d(np.sum(x + y, axis=0)) + s = [0, 2, 4, 6, 8, 10] + x = np.vstack([np.arange(10), 2 * np.arange(10)]).T + y = np.atleast_2d(np.arange(10)).T + p = jk.LstsqJackknifeSlow.delete_values(x, y, func, s) + # 5 blocks, 2D data + assert_array_equal(p.shape, (5, 2)) + correct = [[88, 132], + [80, 120], + [72, 108], + [64, 96], + [56, 84]] + assert_array_almost_equal(p, correct) + + def test_delete_values_2d_2(self): + func = lambda x, y: np.atleast_2d(np.sum(x + y, axis=0)) + s = [0, 5, 10] + x = np.vstack([np.arange(10), 2 * np.arange(10), 3 * np.arange(10)]).T + y = np.atleast_2d(np.arange(10)).T + p = jk.LstsqJackknifeSlow.delete_values(x, y, func, s) + # 2 blocks, 3D data + assert_array_equal(p.shape, (2, 3)) + correct = [[70, 105, 140], + [20, 30, 40]] + assert_array_almost_equal(p, correct) + + def test_lstsqjackknifeslow(self): + x = np.atleast_2d(np.arange(10)).T + y = np.atleast_2d(2 * np.arange(10)).T + reg = jk.LstsqJackknifeSlow(x, y, n_blocks=10) + regnn = jk.LstsqJackknifeSlow(x, y, n_blocks=10, nn=True) + assert_array_almost_equal(reg.est, [[2.]]) + assert_array_almost_equal(regnn.est, [[2.]]) + + # TODO add tests for the SE etc + + def test_bad_data(self): + x = np.arange(10) + y = 2 * np.arange(9) + assert_raises(ValueError, jk.LstsqJackknifeSlow, x, y, 10) + + def test_too_many_blocks(self): + x = np.arange(10) + y = 2 * np.arange(10) + assert_raises(ValueError, jk.LstsqJackknifeSlow, x, y, 11) + + +class Test_LsqtsqJackknifeFast(unittest.TestCase): + + def test_block_values_1d(self): + x = np.arange(6).reshape((6, 1)) + y = np.arange(6).reshape((6, 1)) + s = [0, 2, 4, 6] + xty, xtx = jk.LstsqJackknifeFast.block_values(x, y, s) + assert_array_equal(xty.shape, (3, 1)) + assert_array_equal(xtx.shape, (3, 1, 1)) + correct_xty = [[1], [13], [41]] + assert_array_almost_equal(xty, correct_xty) + correct_xtx = [[[1]], [[13]], [[41]]] + assert_array_almost_equal(xtx, correct_xtx) + + def test_block_values_2d(self): + x = np.vstack([np.arange(6), 2 * np.arange(6)]).T + y = np.arange(6).reshape((6, 1)) + s = [0, 2, 4, 6] + xty, xtx = jk.LstsqJackknifeFast.block_values(x, y, s) + assert_array_equal(xty.shape, (3, 2)) + assert_array_equal(xtx.shape, (3, 2, 2)) + correct_xty = [[1, 2], [13, 26], [41, 82]] + assert_array_almost_equal(xty, correct_xty) + correct_xtx = [ + [[1, 2], [2, 4]], + [[13, 26], [26, 52]], + [[41, 82], [82, 164]]] + assert_array_almost_equal(xtx, correct_xtx) + + def test_block_to_est_1d(self): + x = np.arange(6).reshape((6, 1)) + y = np.arange(6).reshape((6, 1)) + for s in [[0, 3, 6], [0, 2, 4, 6], [0, 1, 5, 6]]: + xty, xtx = jk.LstsqJackknifeFast.block_values(x, y, s) + est = jk.LstsqJackknifeFast.block_values_to_est(xty, xtx) + assert_array_equal(est.shape, (1, 1)) + assert_array_almost_equal(est, [[1.]]) + + def test_block_to_est_2d(self): + x = np.vstack([np.arange(6), [1, 7, 6, 5, 2, 10]]).T + y = np.atleast_2d(np.sum(x, axis=1)).T + for s in [[0, 3, 6], [0, 2, 4, 6], [0, 1, 5, 6]]: + xty, xtx = jk.LstsqJackknifeFast.block_values(x, y, s) + est = jk.LstsqJackknifeFast.block_values_to_est(xty, xtx) + assert_array_equal(est.shape, (1, 2)) + assert_array_almost_equal(est, [[1, 1]]) + + # test the dimension checking + assert_raises( + ValueError, jk.LstsqJackknifeFast.block_values_to_est, xty[0:2], xtx) + assert_raises( + ValueError, jk.LstsqJackknifeFast.block_values_to_est, xty, xtx[:, :, 0:1]) + assert_raises( + ValueError, jk.LstsqJackknifeFast.block_values_to_est, xty, xtx[:, :, 0]) + + def test_block_to_delete_1d(self): + x = np.arange(6).reshape((6, 1)) + y = np.arange(6).reshape((6, 1)) + for s in [[0, 3, 6], [0, 2, 4, 6], [0, 1, 5, 6]]: + xty, xtx = jk.LstsqJackknifeFast.block_values(x, y, s) + delete = jk.LstsqJackknifeFast.block_values_to_delete_values( + xty, xtx) + assert_array_equal(delete.shape, (len(s) - 1, 1)) + assert_array_almost_equal(delete, np.ones_like(delete)) + + def test_block_to_delete_2d(self): + x = np.vstack([np.arange(6), [1, 7, 6, 5, 2, 10]]).T + y = np.atleast_2d(np.sum(x, axis=1)).T + for s in [[0, 3, 6], [0, 2, 4, 6], [0, 1, 5, 6]]: + xty, xtx = jk.LstsqJackknifeFast.block_values(x, y, s) + delete = jk.LstsqJackknifeFast.block_values_to_delete_values( + xty, xtx) + assert_array_equal(delete.shape, (len(s) - 1, 2)) + assert_array_almost_equal(delete, np.ones_like(delete)) + + def test_eq_slow(self): + x = np.atleast_2d(np.random.normal(size=(100, 2))) + y = np.atleast_2d(np.random.normal(size=(100, 1))) + print x.shape + for n_blocks in xrange(2, 49): + b1 = jk.LstsqJackknifeFast(x, y, n_blocks=n_blocks).est + b2 = jk.LstsqJackknifeSlow(x, y, n_blocks=n_blocks).est + assert_array_almost_equal(b1, b2) + + def test_bad_data(self): + x = np.arange(6).reshape((1, 6)) + assert_raises(ValueError, jk.LstsqJackknifeFast, x, x, n_blocks=3) + assert_raises(ValueError, jk.LstsqJackknifeFast, x.T, x.T, n_blocks=8) + assert_raises( + ValueError, jk.LstsqJackknifeFast, x.T, x.T, separators=range(10)) + + +class Test_RatioJackknife(unittest.TestCase): + + def test_1d(self): + self.numer_delete_values = np.matrix(np.arange(1, 11)).T + self.denom_delete_values = - np.matrix(np.arange(1, 11)).T + self.denom_delete_values[9, 0] += 1 + self.est = np.matrix(-1) + self.n_blocks = self.numer_delete_values.shape[0] + self.jknife = jk.RatioJackknife( + self.est, self.numer_delete_values, self.denom_delete_values) + self.assertEqual(self.jknife.est, self.est) + assert_array_almost_equal(self.jknife.pseudovalues[0:9, :], -1) + self.assertEqual(self.jknife.pseudovalues[9, :], 0) + assert_array_almost_equal(self.jknife.jknife_est[0, 0], -0.9) + assert_array_almost_equal(self.jknife.jknife_se, 0.1) + assert_array_almost_equal(self.jknife.jknife_var, 0.01) + assert_array_almost_equal(self.jknife.jknife_cov, 0.01) + + def test_divide_by_zero_1d(self): + est = np.ones((1, 1)) + numer_delete_vals = np.ones((10, 1)) + denom_delete_vals = np.ones((10, 1)) + denom_delete_vals[9, 0] = 0 + # with warnings.catch_warnings(record=True) as w: + # jknife = jk.RatioJackknife(est, numer_delete_vals, denom_delete_vals) + assert_raises(FloatingPointError, jk.RatioJackknife, + est, numer_delete_vals, denom_delete_vals) + + def test_2d(self): + self.numer_delete_values = np.matrix( + np.vstack((np.arange(1, 11), 2 * np.arange(1, 11)))).T + x = - np.arange(1, 11) + x[9] += 1 + self.denom_delete_values = np.vstack((x, 4 * x)).T + self.est = np.matrix((-1, -0.5)) + self.n_blocks = self.numer_delete_values.shape[0] + self.jknife = jk.RatioJackknife( + self.est, self.numer_delete_values, self.denom_delete_values) + assert_array_almost_equal(self.jknife.est, self.est) + self.assertEqual(self.jknife.est.shape, (1, 2)) + assert_array_almost_equal(self.jknife.pseudovalues[0:9, 0], -1) + self.assertEqual(self.jknife.pseudovalues[9, 0], 0) + assert_array_almost_equal(self.jknife.pseudovalues[0:9, 1], -0.5) + self.assertEqual(self.jknife.pseudovalues[9, 1], 0) + assert_array_almost_equal(self.jknife.jknife_est, [[-0.9, -0.45]]) + assert_array_almost_equal(self.jknife.jknife_se, [[0.1, 0.05]]) + assert_array_almost_equal(self.jknife.jknife_var, [[0.01, 0.0025]]) + assert_array_almost_equal( + self.jknife.jknife_cov, np.matrix(((0.01, 0.005), (0.005, 0.0025)))) + + def test_divide_by_zero_2d(self): + est = np.ones((1, 2)) + numer_delete_vals = np.ones((10, 2)) + denom_delete_vals = np.ones((10, 2)) + denom_delete_vals[9, 0] = 0 + assert_raises(FloatingPointError, jk.RatioJackknife, + est, numer_delete_vals, denom_delete_vals) diff --git a/test/test_ldscore.py b/test/test_ldscore.py index b67cf130..2c34ab72 100644 --- a/test/test_ldscore.py +++ b/test/test_ldscore.py @@ -3,329 +3,120 @@ import bitarray as ba import numpy as np import nose -import pandas as pd import ldscore.parse as ps from nose_parameterized import parameterized as param -########################################################################################## +########################################################################## # MISC FUNCTIONS # -########################################################################################## +########################################################################## @param([ - (np.arange(1,6),5,np.zeros(5)), - (np.arange(1,6),0,np.arange(0,5)), - ((1,4,6,7,7,8),2,(0,1,1,2,2,2))]) + (np.arange(1, 6), 5, np.zeros(5)), + (np.arange(1, 6), 0, np.arange(0, 5)), + ((1, 4, 6, 7, 7, 8), 2, (0, 1, 1, 2, 2, 2))]) def test_getBlockLefts(coords, max_dist, correct): - assert np.all(ld.getBlockLefts(coords, max_dist) == correct) + assert np.all(ld.getBlockLefts(coords, max_dist) == correct) + @param([ - ((0,0,0,0,0),(5,5,5,5,5)), - ((0,1,2,3,4,5), (1,2,3,4,5,6)), - ((0,0,2,2), (2,2,4,4)) - ]) + ((0, 0, 0, 0, 0), (5, 5, 5, 5, 5)), + ((0, 1, 2, 3, 4, 5), (1, 2, 3, 4, 5, 6)), + ((0, 0, 2, 2), (2, 2, 4, 4)) +]) def test_block_left_to_right(block_left, correct_answer): - block_right = ld.block_left_to_right(block_left) - print block_right - correct_answer - assert np.all(block_right == correct_answer) + block_right = ld.block_left_to_right(block_left) + print block_right - correct_answer + assert np.all(block_right == correct_answer) -########################################################################################## +########################################################################## # BED PARSER # -########################################################################################## +########################################################################## class test_bed(unittest.TestCase): - - def setUp(self): - self.M = 8 - self.N = 5 - self.bim = ps.PlinkBIMFile('test/plink_test/plink.bim') - - def test_bed(self): - bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim) - # remove three monomorphic SNPs - print bed.geno - print bed.m - assert bed.m == 4 - # no individuals removed - print bed.n - assert self.N == bed.n - # 5 indivs * 4 polymorphic SNPs - print len(bed.geno) - assert len(bed.geno) == 64 - print bed.freq - correct = np.array([0.59999999999999998, 0.59999999999999998, 0.625, 0.625]) - assert np.all(bed.freq == correct) - - def test_filter_snps(self): - keep_snps = [1,4] - bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim, - keep_snps=keep_snps) - assert bed.m == 1 - assert bed.n == 5 - # pad bits are initialized with random memory --> can't test them - assert bed.geno[0:10] == ba.bitarray('0001011111') - - def test_filter_indivs(self): - keep_indivs = [0,1] - bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim, - keep_indivs=keep_indivs) - assert bed.m == 2 - assert bed.n == 2 - # pad bits are initialized with random memory --> can't test them - assert bed.geno[0:4] == ba.bitarray('0001') - assert bed.geno[8:12] == ba.bitarray('0001') - - def test_filter_indivs_and_snps(self): - keep_indivs = [0,1] - keep_snps = [1,5] - bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim, - keep_snps=keep_snps, keep_indivs=keep_indivs) - assert bed.m == 1 - assert bed.n == 2 - print bed.geno - assert bed.geno[0:4] == ba.bitarray('0001') - - @nose.tools.raises(ValueError) - def test_bad_filename(self): - bed = ld.PlinkBEDFile('test/plink_test/plink.bim', 9, self.bim) - - @nose.tools.raises(ValueError) - def test_nextSNPs_errors1(self): - bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim) - bed.nextSNPs(0) - - @nose.tools.raises(ValueError) - def test_nextSNPs_errors2(self): - bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim) - bed.nextSNPs(5) - - @param.expand([([1]),([2]),([3])]) - def test_nextSNPs(self, b): - bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim) - x = bed.nextSNPs(b) - print x - print np.std(x,axis=0) - assert x.shape == (5,b) - assert np.all(np.abs(np.mean(x, axis=0)) < 0.01) - assert np.all(np.abs(np.std(x, axis=0) - 1) < 0.01) - - def test_nextSNPs_maf_ref(self): - b = 4 - bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim) - x = bed.nextSNPs(b) - bed._currentSNP -= b - y = bed.nextSNPs(b, minorRef=True) - assert np.all(x == -y) - -########################################################################################## -# VCF PARSER # -########################################################################################## - - -class test_bin(unittest.TestCase): - - def setUp(self): - self.M = 6 - self.N = 5 - self.geno = ba.bitarray('01100011000101001010') - self.snp = ps.VcfSNPFile('test/vcf_test/test.snp') - ''' - The genotype matrix looks like this before any filtering - - (indivs) x (snps) - - 010000 - 011111 - 011100 - 010011 - 010000 - - The two monomorphic SNPs at left should get removed in all tests - ''' - - def test_bin(self): - bin = ld.VcfBINFile('test/vcf_test/test.bin', self.N, self.snp, mafMin=0.2) - print bin.geno - print len(bin.geno) - print bin.freq - - assert bin.m == 4 - assert bin.n == self.N - assert len(bin.geno) == bin.n*bin.m - assert bin.geno == self.geno - assert np.all(bin.freq==0.4*np.ones(4)) - - @nose.tools.raises(ValueError) - def test_mafMin(self): - bin = ld.VcfBINFile('test/vcf_test/test.bin', self.N, self.snp, mafMin=0.45) - - def test_filter_snps(self): - ''' - The genotype matrix should look like this after filtering SNPs - - 100 - 111 - 110 - 101 - 100 - - The monomorphic SNP at left should be removed by _filter_monomorphic - ''' - k = [1,3,4] - bin = ld.VcfBINFile('test/vcf_test/test.bin', self.N, self.snp, keep_snps=k) - print bin.geno - assert bin.m == 2 - assert bin.n == 5 - assert bin.geno == ba.bitarray('0110001010') - - def test_filter_indivs(self): - ''' - The genotype matrix should look like this after filtering individuals - - 011111 - 011100 - 010011 - - The monomorphic SNPs at left should be removed by _filter_monomorphic - ''' - k = [1,2,3] - bin = ld.VcfBINFile('test/vcf_test/test.bin', self.N, self.snp, keep_indivs=k) - assert bin.n == 3 - assert bin.m == 4 - assert bin.geno == ba.bitarray('110110101101') - - def test_filter_snps_and_indivs(self): - ''' - The genotype matrix should look like this after filtering SNPs and individuals - - 111 - 110 - 101 - - The monomorphic SNP at left should be removed by _filter_monomorphic - ''' - ks = [1,3,5] - ki = [1,2,3] - bin = ld.VcfBINFile('test/vcf_test/test.bin', self.N, self.snp, keep_snps=ks, - keep_indivs=ki) - assert bin.n == 3 - assert bin.m == 2 - assert bin.geno == ba.bitarray('110101') - - @nose.tools.raises(ValueError) - def test_bad_filename(self): - bin = ld.VcfBINFile('test/vcf_test/test.ind', self.N, self.snp) - - @nose.tools.raises(ValueError) - def test_nextSNPs_errors1(self): - bin = ld.VcfBINFile('test/vcf_test/test.bin', self.N, self.snp) - bin.nextSNPs(0) - - @nose.tools.raises(ValueError) - def test_nextSNPs_errors2(self): - bin = ld.VcfBINFile('test/vcf_test/test.bin', self.N, self.snp) - bin.nextSNPs(7) - - @param.expand([([1]),([2]),([3])]) - def test_nextSNPs(self, b): - bin = ld.VcfBINFile('test/vcf_test/test.bin', self.N, self.snp) - x = bin.nextSNPs(b) - assert x.shape == (5,b) - print np.mean(x,axis=0) - # roundoff error --> can't test equality to zero directly - assert np.all(np.abs(np.mean(x, axis=0)) < 0.01) - assert np.all(np.abs(np.std(x, axis=0) - 1) < 0.01) - - @param.expand([([1]),([2]),([3])]) - def test_nextSNPs_maf_ref(self, b): - bin = ld.VcfBINFile('test/vcf_test/test.bin', self.N, self.snp) - x = bin.nextSNPs(b) - bin._currentSNP -= b - y = bin.nextSNPs(b, minorRef=True) - assert np.all(x == y) - # switch reference alleles - bin.geno = ~ bin.geno; bin.freq = np.ones(bin.m) - bin.freq - print bin.geno - bin._currentSNP -=b - z = bin.nextSNPs(b, minorRef=True) - assert np.all(y == z) - - -########################################################################################## -# LD SCORE # -########################################################################################## - - -class test_ldscore_vcf(unittest.TestCase): - - def setUp(self): - self.M = 10 - self.N = 2 - annot = np.array((1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,1,1,0,0,1),dtype='float64') - self.annot = annot.reshape((10,2)) - self.snp = ps.VcfSNPFile('test/vcf_test/test.ldscore.snp') - self.bin = ld.VcfBINFile('test/vcf_test/test.ldscore.bin', self.N, self.snp) - - def test_no_snps_to_left(self): - # test the edge case where no SNPs to left are in the window - block_left = np.arange(10) - x = self.bin.ldScoreVarBlocks(block_left,1,annot=self.annot) - assert np.all(x == self.annot) - - def test_no_annot(self): - block_left = np.array((0,0,0,0,2,2,4,4,8,8)) - x = self.bin.ldScoreVarBlocks(block_left,2,annot=None).reshape((10)) - correct_x = np.array((4,4,6,6,6,6,4,4,2,2)) - assert np.all(x == correct_x) - @param.expand([(1,(2,2,2,2,2,4,2,4,2,4,2,4,1,3,1,3,1,1,1,1)), - (2,(2,2,2,2,2,4,2,4,2,4,2,4,1,3,1,3,1,1,1,1)), - (3,(2,4,2,4,2,4,4,5,4,5,4,5,3,4,3,4,3,4,2,2))]) - def test_var_block_size(self, c, correct_x): - correct_x = np.array(correct_x,dtype='float64').reshape((10,2)) - block_left = np.array((0,0,0,0,2,2,4,4,8,8)) - x = self.bin.ldScoreVarBlocks(block_left,c,self.annot) - print self.annot - print x - correct_x - assert np.all(x == correct_x) - - def test_l1_to_l1sqhat_(self): - block_left = np.zeros(10) - block_right = np.repeat(10,10) - l1hat = np.ones((10,2)) - x = self.bin._GenotypeArrayInMemory____l1_to_l1sqhat_(l1hat, block_left, - block_right) - assert np.all(x == -4.5) - - -########################################################################################## -# LD SCORE + JACKKNIFE SE # -########################################################################################## -''' -class test_ldscore_jackknife_vcf(unittest.TestCase): - - def setUp(self): - self.M = 10 - self.N = 2 - annot = np.array((1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,1,1,0,0,1),dtype='float64') - self.annot = annot.reshape((10,2)) - self.snp = ps.VcfSNPFile('test/vcf_test/test.ldscore.snp') - self.bin = ld.VcfBINFile('test/vcf_test/test.ldscore.bin', self.N, self.snp) - - def test(self): - block_left = np.array((0,0,1,1,1,1,4,4,4,4)) - x = self.bin.ldScoreBlockJackknife(block_left,c=1,annot=None,jN=2) - - @param.expand([(1,(2,2,2,2,2,4,2,4,2,4,2,4,1,3,1,3,1,1,1,1)), - (2,(2,2,2,2,2,4,2,4,2,4,2,4,1,3,1,3,1,1,1,1)), - #(3,(2,4,2,4,2,4,4,5,4,5,4,5,3,4,3,4,3,4,2,2)) - ]) - def test_var_block_size_jknife(self, c, correct_x): - correct_x = np.array(correct_x,dtype='float64').reshape((10,2)) - block_left = np.array((0,0,0,0,2,2,4,4,8,8)) - x = self.bin.ldScoreBlockJackknife(block_left,c=c,annot=self.annot,jN=2)[0] - assert np.all(x == correct_x) -''' \ No newline at end of file + def setUp(self): + self.M = 8 + self.N = 5 + self.bim = ps.PlinkBIMFile('test/plink_test/plink.bim') + + def test_bed(self): + bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim) + # remove three monomorphic SNPs + print bed.geno + print bed.m + assert bed.m == 4 + # no individuals removed + print bed.n + assert self.N == bed.n + # 5 indivs * 4 polymorphic SNPs + print len(bed.geno) + assert len(bed.geno) == 64 + print bed.freq + correct = np.array( + [0.59999999999999998, 0.59999999999999998, 0.625, 0.625]) + assert np.all(bed.freq == correct) + + def test_filter_snps(self): + keep_snps = [1, 4] + bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim, + keep_snps=keep_snps) + assert bed.m == 1 + assert bed.n == 5 + # pad bits are initialized with random memory --> can't test them + assert bed.geno[0:10] == ba.bitarray('0001011111') + + def test_filter_indivs(self): + keep_indivs = [0, 1] + bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim, + keep_indivs=keep_indivs) + assert bed.m == 2 + assert bed.n == 2 + # pad bits are initialized with random memory --> can't test them + assert bed.geno[0:4] == ba.bitarray('0001') + assert bed.geno[8:12] == ba.bitarray('0001') + + def test_filter_indivs_and_snps(self): + keep_indivs = [0, 1] + keep_snps = [1, 5] + bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim, + keep_snps=keep_snps, keep_indivs=keep_indivs) + assert bed.m == 1 + assert bed.n == 2 + print bed.geno + assert bed.geno[0:4] == ba.bitarray('0001') + + @nose.tools.raises(ValueError) + def test_bad_filename(self): + bed = ld.PlinkBEDFile('test/plink_test/plink.bim', 9, self.bim) + + @nose.tools.raises(ValueError) + def test_nextSNPs_errors1(self): + bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim) + bed.nextSNPs(0) + + @nose.tools.raises(ValueError) + def test_nextSNPs_errors2(self): + bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim) + bed.nextSNPs(5) + + @param.expand([([1]), ([2]), ([3])]) + def test_nextSNPs(self, b): + bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim) + x = bed.nextSNPs(b) + print x + print np.std(x, axis=0) + assert x.shape == (5, b) + assert np.all(np.abs(np.mean(x, axis=0)) < 0.01) + assert np.all(np.abs(np.std(x, axis=0) - 1) < 0.01) + + def test_nextSNPs_maf_ref(self): + b = 4 + bed = ld.PlinkBEDFile('test/plink_test/plink.bed', self.N, self.bim) + x = bed.nextSNPs(b) + bed._currentSNP -= b + y = bed.nextSNPs(b, minorRef=True) + assert np.all(x == -y) diff --git a/test/test_munge_sumstats.py b/test/test_munge_sumstats.py new file mode 100644 index 00000000..3d52d119 --- /dev/null +++ b/test/test_munge_sumstats.py @@ -0,0 +1,358 @@ +from __future__ import division +import munge_sumstats as munge +import unittest +import numpy as np +import pandas as pd +import nose +from pandas.util.testing import assert_series_equal +from pandas.util.testing import assert_frame_equal +from numpy.testing import assert_array_equal, assert_array_almost_equal, assert_allclose + + +class Mock(object): + + ''' + Dumb object for mocking args and log + ''' + + def __init__(self): + pass + + def log(self, x): + pass + +log = Mock() +args = munge.parser.parse_args('') + + +class test_p_to_z(unittest.TestCase): + + def setUp(self): + self.N = pd.Series([1, 2, 3]) + self.P = pd.Series([0.1, 0.1, 0.1]) + self.Z = pd.Series([1.644854, 1.644854, 1.644854]) + + def test_p_to_z(self): + assert_allclose(munge.p_to_z(self.P, self.N), self.Z, atol=1e-5) + + +class test_check_median(unittest.TestCase): + + def setUp(self): + self.x = pd.Series([1, 2, 3]) + + def test_good_median(self): + msg = munge.check_median(self.x, 2, 0, 'TEST') + self.assertEqual( + msg, 'Median value of TEST was 2.0, which seems sensible.') + + def test_bad_median(self): + nose.tools.assert_raises( + ValueError, munge.check_median, self.x, 0, 0.1, 'TEST') + + +class test_process_n(unittest.TestCase): + + def setUp(self): + self.dat = pd.DataFrame(['rs1', 'rs2', 'rs3'], columns=['SNP']) + self.dat_filtered = pd.DataFrame(['rs2', 'rs3'], columns=['SNP']) + self.dat_filtered['N'] = [1234, 1234.0] + self.dat_filtered9999 = pd.DataFrame(['rs2', 'rs3'], columns=['SNP']) + self.dat_filtered9999['N'] = [9999, 9999.0] + self.args = munge.parser.parse_args('') + # these flags are either re-set in test cases or should be overridden + self.args.N = 9999.0 + self.args.N_cas = 9999.0 + self.args.N_con = 9999.0 + self.N_const = pd.Series([1234, 1234, 1234.0]) + self.N = pd.Series([1, 1234, 1234.0]) + self.N_filt = pd.Series([1234, 1234.0]) + self.N9999 = pd.Series([9999.0, 9999, 9999]) + + def test_n_col(self): + self.dat['N'] = self.N + dat = munge.process_n(self.dat, self.args, log) + print dat + print self.dat_filtered + assert_frame_equal(dat, self.dat_filtered) + + def test_nstudy(self): + # should filter on NSTUDY if the --N flag is set, but N gets set to + # 9999 + self.dat['NSTUDY'] = self.N + dat = munge.process_n(self.dat, self.args, log) + assert_frame_equal(dat, self.dat_filtered9999) + + def test_n_cas_con_col(self): + self.dat['N_CAS'] = self.N + self.dat['N_CON'] = [0.0, 0, 0] + dat = munge.process_n(self.dat, self.args, log) + assert_frame_equal(dat, self.dat_filtered) + + def test_n_flag(self): + self.args.N = 1234.0 + self.args.N_cas = None + self.args.N_con = None + dat = munge.process_n(self.dat, self.args, log) + assert_series_equal(dat.N, self.N_const) + + def test_n_cas_con_flag(self): + self.args.N = None + self.args.N_cas = 1000.0 + self.args.N_con = 234.0 + dat = munge.process_n(self.dat, self.args, log) + assert_series_equal(dat.N, self.N_const) + + +def test_filter_pvals(): + P = pd.Series([0, 0.1, 1, 2]) + x = munge.filter_pvals(P, log, args) + assert_series_equal(x, pd.Series([False, True, True, False])) + + +def test_single_info(): + dat = pd.Series([0.8, 1, 1]) + x = munge.filter_info(dat, log, args) + assert_series_equal(x, pd.Series([False, True, True])) + + +def test_multiple_info(): + i1 = pd.Series([0.8, 1, 1]) + i2 = pd.Series([1.01, 0.5, 9]) + dat = pd.concat([i1, i2], axis=1).reset_index(drop=True) + dat.columns = ['INFO', 'INFO'] + x = munge.filter_info(dat, log, args) + assert_series_equal(x, pd.Series([True, False, True])) + + +def test_filter_frq(): + frq = pd.Series([-1, 0, 0.005, 0.4, 0.6, 0.999, 1, 2]) + x = munge.filter_frq(frq, log, args) + assert_series_equal( + x, pd.Series([False, False, False, True, True, False, False, False])) + + +def test_filter_alleles(): + a = pd.Series( + ['AC', 'AG', 'CA', 'CT', 'GA', 'GT', 'TC', 'TG', 'DI', 'AAT', 'RA']) + x = munge.filter_alleles(a) + y = pd.Series([i < 8 for i in range(11)]) + assert_series_equal(x, y) + + +class test_allele_merge(unittest.TestCase): + + def setUp(self): + self.dat = pd.DataFrame(np.transpose([ + ['a', 'b', 'c'], + ['A', 'T', 'C'], + ['C', 'G', 'A']] + )) + self.dat.columns = ['SNP', 'A1', 'A2'] + self.alleles = pd.DataFrame(np.transpose([ + ['a', 'extra', 'b', 'c'], + ['AG', 'TC', 'AC', 'AC']] + )) + self.alleles.columns = ['SNP', 'MA'] + + def test_merge(self): + x = munge.allele_merge(self.dat, self.alleles, log) + answer = pd.DataFrame(np.transpose([ + ['a', 'extra', 'b', 'c'], + ['a', 'a', 'T', 'C'], + ['a', 'a', 'G', 'A']] + )) + answer.columns = ['SNP', 'A1', 'A2'] + answer.loc[[0, 1], ['A1', 'A2']] = float('nan') + assert_frame_equal(x, answer) + + +class test_parse_dat(unittest.TestCase): + + def setUp(self): + dat = pd.DataFrame() + dat['SNP'] = ['rs' + str(i) for i in range(10)] + dat['A1'] = ['A' for i in range(10)] + dat['A2'] = ['G' for i in range(10)] + dat['INFO'] = np.ones(10) + dat['FRQ'] = np.ones(10) / 2 + dat['P'] = np.ones(10) + self.dat = dat + self.dat_gen = [ + dat.loc[0:4, :], dat.loc[5:9, :].reset_index(drop=True)] + self.convert_colname = {x: x for x in self.dat_gen[0].columns} + self.args = munge.parser.parse_args('') + + def tearDown(self): + args = munge.parser.parse_args('') + + def test_no_alleles(self): + # test that it doesn't crash with no allele columns and the + # --no-alleles flag set + dat = self.dat.drop(['A1', 'A2'], axis=1) + dat_gen = [dat.loc[0:4, :], dat.loc[5:9, :].reset_index(drop=True)] + self.args.no_alleles = True + dat = munge.parse_dat( + dat_gen, self.convert_colname, None, log, self.args) + assert_frame_equal( + dat, self.dat.drop(['INFO', 'FRQ', 'A1', 'A2'], axis=1)) + + def test_merge_alleles(self): + self.args.merge_alleles = True + merge_alleles = pd.DataFrame() + merge_alleles['SNP'] = ['rs' + str(i) for i in range(3)] + merge_alleles['MA'] = ['AG', 'AG', 'AG'] + dat = munge.parse_dat( + self.dat_gen, self.convert_colname, merge_alleles, log, self.args) + print self.dat.loc[0:2, ['SNP', 'A1', 'A2', 'P']] + assert_frame_equal(dat, self.dat.loc[0:2, ['SNP', 'A1', 'A2', 'P']]) + + def test_standard(self): + dat = munge.parse_dat( + self.dat_gen, self.convert_colname, None, log, self.args) + assert_frame_equal(dat, self.dat.drop(['INFO', 'FRQ'], axis=1)) + + def test_na(self): + self.dat.loc[0, 'SNP'] = float('NaN') + self.dat.loc[1, 'A2'] = float('NaN') + self.dat_gen = [ + self.dat.loc[0:4, :], self.dat.loc[5:9, :].reset_index(drop=True)] + dat = munge.parse_dat( + self.dat_gen, self.convert_colname, None, log, self.args) + assert_frame_equal( + dat, self.dat.loc[2:, ['SNP', 'A1', 'A2', 'P']].reset_index(drop=True)) + + +def test_clean_header(): + nose.tools.eq_(munge.clean_header('foo-bar.foo_BaR'), 'FOO_BAR_FOO_BAR') + + +def test_get_compression_gzip(): + y, x = munge.get_compression('foo.gz') + nose.tools.eq_(x, 'gzip') + y, x = munge.get_compression('foo.bz2') + nose.tools.eq_(x, 'bz2') + y, x = munge.get_compression('foo.bar') + nose.tools.eq_(x, None) + + +class test_parse_flag_cnames(unittest.TestCase): + + def setUp(self): + self.args = munge.parser.parse_args('') + + def test_basic(self): + self.args.nstudy = 'nstudy1' + self.args.snp = 'snp1' + self.args.N_col = 'n.col1' + self.args.N_cas_col = 'n-cas.col1' + self.args.N_con_col = 'n-con.col1' + self.args.a1 = 'a11' + self.args.a2 = 'a21' + self.args.p = 'p1' + self.args.frq = 'frq1' + self.args.info = 'info1' + self.args.info_list = 'info111,info222' + self.args.signed_sumstats = 'beta1,0' + x, y = munge.parse_flag_cnames(log, self.args) + self.assertEqual(y, 0) + self.assertEqual(x['NSTUDY1'], 'NSTUDY') + self.assertEqual(x['SNP1'], 'SNP') + self.assertEqual(x['N_COL1'], 'N') + self.assertEqual(x['N_CAS_COL1'], 'N_CAS') + self.assertEqual(x['N_CON_COL1'], 'N_CON') + self.assertEqual(x['A11'], 'A1') + self.assertEqual(x['A21'], 'A2') + self.assertEqual(x['P1'], 'P') + self.assertEqual(x['FRQ1'], 'FRQ') + self.assertEqual(x['INFO1'], 'INFO') + self.assertEqual(x['INFO111'], 'INFO') + self.assertEqual(x['INFO222'], 'INFO') + + def test_sign_error(self): + self.args.signed_sumstats = '1,2,3' + nose.tools.assert_raises( + ValueError, munge.parse_flag_cnames, log, self.args) + self.args.signed_sumstats = 'BETA,B' + nose.tools.assert_raises( + ValueError, munge.parse_flag_cnames, log, self.args) + self.args.signed_sumstats = 'BETA' + nose.tools.assert_raises( + ValueError, munge.parse_flag_cnames, log, self.args) + + +class test_cname_map(unittest.TestCase): + + def setUp(self): + pass + + def test_no_flags(self): + x = munge.get_cname_map({}, munge.default_cnames, []) + self.assertEqual(x, munge.default_cnames) + + def test_ignore(self): + ignore = ['sNp', 'a1'] + flag_cnames = {'SNP': 'SNP', 'ASDF': 'ASDF', 'N': 'FOOBAR'} + x = munge.get_cname_map(flag_cnames, munge.default_cnames, ignore) + # check that ignore columns are ignored + nose.tools.assert_raises(KeyError, x.__getitem__, 'SNP') + nose.tools.assert_raises(KeyError, x.__getitem__, 'A1') + # check that flag columns make it into the dict + self.assertEqual(x['ASDF'], 'ASDF') + # check that default columns make it into the dict + self.assertEqual(x['A2'], 'A2') + # check that flags override default + self.assertEqual(x['N'], 'FOOBAR') + + +class test_end_to_end(unittest.TestCase): + + def setUp(self): + self.args = munge.parser.parse_args('') + self.args.sumstats = 'test/munge_test/sumstats' + self.args.out = 'asdf' + self.args.daner = True + + def test_basic(self): + x = munge.munge_sumstats(self.args, p=False) + correct = pd.read_csv( + 'test/munge_test/correct.sumstats', delim_whitespace=True, header=0) + assert_frame_equal(x, correct) + + def test_merge_alleles(self): + self.args.merge_alleles = 'test/munge_test/merge_alleles' + x = munge.munge_sumstats(self.args, p=False) + correct = pd.read_csv( + 'test/munge_test/correct_merge.sumstats', delim_whitespace=True, header=0) + assert_frame_equal(x, correct) + + def test_bad_merge_alleles(self): + self.args.merge_alleles = 'test/munge_test/merge_alleles_bad' + nose.tools.assert_raises( + ValueError, munge.munge_sumstats, self.args, p=False) + + def test_bad_flags1(self): + self.args.sumstats = None + nose.tools.assert_raises( + ValueError, munge.munge_sumstats, self.args, p=False) + + def test_bad_flags2(self): + self.args.out = None + nose.tools.assert_raises( + ValueError, munge.munge_sumstats, self.args, p=False) + + def test_bad_flags3(self): + self.args.merge_alleles = 'foo' + self.args.no_alleles = 'bar' + nose.tools.assert_raises( + ValueError, munge.munge_sumstats, self.args, p=False) + + def test_bad_sumstats1(self): + self.args.signed_sumstats = 'OR,0' + nose.tools.assert_raises( + ValueError, munge.munge_sumstats, self.args, p=False) + + def test_bad_sumstats1(self): + self.args.signed_sumstats = 'BETA,0' + nose.tools.assert_raises( + ValueError, munge.munge_sumstats, self.args, p=False) diff --git a/test/test_parse.py b/test/test_parse.py index 04120786..85e926e9 100644 --- a/test/test_parse.py +++ b/test/test_parse.py @@ -4,204 +4,126 @@ import numpy as np import pandas as pd import nose -from nose_parameterized import parameterized as param - - -class Test_dir(unittest.TestCase): - - @nose.tools.raises(ValueError) - def test(self): - dir = np.array((1,-1,0)) - ps.check_dir(dir) - - -class Test_check_pvalue(unittest.TestCase): - - @nose.tools.raises(ValueError) - def test_missing(self): - p = np.array((1,0.5,float('nan'))) - ps.check_pvalue(p) - - @nose.tools.raises(ValueError) - def test_high(self): - p = np.array((1,0.5,2)) - ps.check_pvalue(p) - - @nose.tools.raises(ValueError) - def test_low(self): - p = np.array((1,0.5,0)) - ps.check_pvalue(p) - - -class Test_check_chisq(unittest.TestCase): - - @nose.tools.raises(ValueError) - def test_missing(self): - chisq = np.array((1,0.5,float('nan'))) - ps.check_chisq(chisq) - - @nose.tools.raises(ValueError) - def test_inf(self): - chisq = np.array((1,0.5,float('inf'))) - ps.check_chisq(chisq) - - @nose.tools.raises(ValueError) - def test_inf(self): - chisq = np.array((1,0.5,-1)) - ps.check_chisq(chisq) - - -class test_check_maf(unittest.TestCase): - - @nose.tools.raises(ValueError) - def test_missing(self): - maf = np.array((1,0.5,float('nan'))) - ps.check_maf(maf) - - @nose.tools.raises(ValueError) - def test_high(self): - maf = np.array((1,0.5,0.2)) - ps.check_maf(maf) - - @nose.tools.raises(ValueError) - def test_low(self): - maf = np.array((0.1,0.5,0)) - ps.check_maf(maf) - - -class Test_check_N(unittest.TestCase): - - @nose.tools.raises(ValueError) - def test_low(self): - N = np.array((2,1,-1)) - ps.check_N(N) - - -class Test_chisq(unittest.TestCase): - - def test_chisq(self): - x = ps.chisq('test/parse_test/test.chisq') - self.assertEqual(list(x['SNP']), ['rs1', 'rs2','rs3']) - self.assertEqual(list(x['N']), [100, 100, 100]) - self.assertEqual(list(x['INFO']), [1, 1, 1]) - assert np.all(np.abs(x['MAF'] - [0.5, 0.01, 0.01]) < 10e-6) - assert np.all(x.columns == ['SNP','N','CHISQ','INFO','MAF']) - -class Test_betaprod(unittest.TestCase): - - def test_betaprod(self): - x = ps.betaprod('test/parse_test/test.betaprod') - self.assertEqual(list(x['SNP']), ['rs1', 'rs2','rs3']) - self.assertEqual(list(x['N1']), [100, 100, 100]) - self.assertEqual(list(x['N2']), [5, 5, 5]) - self.assertEqual(list(x.columns), ['SNP','N1','BETAHAT1','N2','BETAHAT2']) - self.assertEqual(list(x['BETAHAT1']), [-1,1,-1]) - self.assertEqual(list(x['BETAHAT2']), [1,-1,1]) +import os +from nose.tools import * +from numpy.testing import assert_array_equal, assert_array_almost_equal + +DIR = os.path.dirname(__file__) + + +def test_series_eq(): + x = pd.Series([1, 2, 3]) + y = pd.Series([1, 2]) + z = pd.Series([1, 2, 4]) + assert ps.series_eq(x, x) + assert not ps.series_eq(x, y) + assert not ps.series_eq(x, z) + + +def test_get_compression(): + assert_equal(ps.get_compression('gz'), 'gzip') + assert_equal(ps.get_compression('bz2'), 'bz2') + assert_equal(ps.get_compression('asdf'), None) + + +def test_read_cts(): + match_snps = pd.Series(['rs1', 'rs2', 'rs3']) + assert_array_equal( + ps.read_cts(os.path.join(DIR, 'parse_test/test.cts'), match_snps), [1, 2, 3]) + assert_raises(ValueError, ps.read_cts, os.path.join( + DIR, 'parse_test/test.cts'), match_snps[0:2]) + + +def test_read_sumstats(): + x = ps.sumstats( + os.path.join(DIR, 'parse_test/test.sumstats'), dropna=True, alleles=True) + assert_equal(len(x), 1) + assert_array_equal(x.SNP, 'rs1') + assert_raises(ValueError, ps.sumstats, os.path.join( + DIR, 'parse_test/test.l2.ldscore.gz')) + + +def test_frq_parser(): + x = ps.frq_parser(os.path.join(DIR, 'parse_test/test1.frq'), compression=None) + assert_array_equal(x.columns, ['SNP', 'FRQ']) + assert_array_equal(x.SNP, ['rs_' + str(i) for i in range(8)]) + assert_array_equal(x.FRQ, [.01, .1, .7, .2, .2, .2, .99, .03]) + x = ps.frq_parser(os.path.join(DIR, 'parse_test/test2.frq.gz'), compression='gzip') + assert_array_equal(x.columns, ['SNP', 'FRQ']) + assert_array_equal(x.SNP, ['rs_' + str(i) for i in range(8)]) + assert_array_equal(x.FRQ, [.01, .1, .3, .2, .2, .2, .01, .03]) class Test_ldscore(unittest.TestCase): - def test_ldscore(self): - x = ps.ldscore('test/parse_test/test') - self.assertEqual(list(x['SNP']), ['rs'+str(i) for i in range(1,23)] ) - self.assertEqual(list(x['AL2']), range(1,23) ) - self.assertEqual(list(x['BL2']), range(2,46,2) ) - -class Test_ldscore22(unittest.TestCase): - - def test_ldscore_loop(self): - x = ps.ldscore('test/parse_test/test',2) - self.assertEqual(list(x['SNP']), ['rs'+str(i) for i in range(1,3)] ) - self.assertEqual(list(x['AL2']), range(1,3) ) - self.assertEqual(list(x['BL2']), range(2,6,2) ) - + def test_ldscore(self): + x = ps.ldscore(os.path.join(DIR, 'parse_test/test')) + assert_equal(list(x['SNP']), ['rs' + str(i) for i in range(1, 23)]) + assert_equal(list(x['AL2']), range(1, 23)) + assert_equal(list(x['BL2']), range(2, 46, 2)) + + def test_ldscore_loop(self): + x = ps.ldscore(os.path.join(DIR, 'parse_test/test'), 2) + assert_equal(list(x['SNP']), ['rs' + str(i) for i in range(1, 3)]) + assert_equal(list(x['AL2']), range(1, 3)) + assert_equal(list(x['BL2']), range(2, 6, 2)) + + def test_ldscore_fromlist(self): + fh = os.path.join(DIR, 'parse_test/test') + x = ps.ldscore_fromlist([fh, fh]) + assert_array_equal(x.shape, (22, 5)) + y = ps.ldscore(os.path.join(DIR, 'parse_test/test')) + assert_array_equal(x.ix[:, 0:3], y) + assert_array_equal(x.ix[:, [0, 3, 4]], y) + assert_raises( + ValueError, ps.ldscore_fromlist, [fh, os.path.join(DIR, 'parse_test/test2')]) + class Test_M(unittest.TestCase): - - @nose.tools.raises(ValueError) - def test_bad_M(self): - x = ps.M('test/parse_test/test_bad') - - def test_M(self): - x = ps.M('test/parse_test/test') - self.assertEqual(list(x), [1000,2000,3000]) - - -class Test_M_loop(unittest.TestCase): - - def test_M_loop(self): - x = ps.M('test/parse_test/test', 2) - self.assertEqual(list(x), [3, 6]) - - -class test_snp(unittest.TestCase): - - def test_snp(self): - snp = ps.VcfSNPFile('test/vcf_test/test.snp') - print snp.IDList - assert snp.n == 6 - correct = np.array(['rs1','rs2','rs3','rs4','rs5','rs6']) - assert np.all(snp.IDList.values.reshape(6) == correct) - - @nose.tools.raises(ValueError) - def test_bad_filename(self): - ind = ps.VcfSNPFile('test/plink_test/plink.asdf') - - def test_loj(self): - snp = ps.VcfSNPFile('test/vcf_test/test.snp') - df2 = pd.DataFrame(['rs1','rs4','rs6']) - ii = snp.loj(df2) - assert np.all(ii == [0,3,5]) - - -class test_ind(unittest.TestCase): - - def test_ind(self): - ind = ps.VcfINDFile('test/vcf_test/test.ind') - print ind.IDList - assert ind.n == 5 - correct = np.array(['per1', 'per2', 'per3', 'per4', 'per5']) - assert np.all(ind.IDList.values.reshape(5) == correct) - - @nose.tools.raises(ValueError) - def test_bad_filename(self): - ind = ps.VcfINDFile('test/plink_test/plink.snp') - - -class test_fam(unittest.TestCase): - - def test_fam(self): - fam = ps.PlinkFAMFile('test/plink_test/plink.fam') - print fam.IDList - assert fam.n == 5 - correct = np.array(['per0', 'per1', 'per2', 'per3', 'per4']) - assert np.all(fam.IDList.values.reshape((5,)) == correct) - - @nose.tools.raises(ValueError) - def test_bad_filename(self): - fam = ps.PlinkFAMFile('test/plink_test/plink.bim') - - -class test_bim(unittest.TestCase): - - def test_bim(self): - bim = ps.PlinkBIMFile('test/plink_test/plink.bim') - print bim.IDList - assert bim.n == 8 - correct = np.array(['rs_0','rs_1','rs_2','rs_3','rs_4','rs_5','rs_6','rs_7']) - assert np.all(bim.IDList.values.reshape(8) == correct) - - @nose.tools.raises(ValueError) - def test_bad_filename(self): - bim = ps.PlinkBIMFile('test/plink_test/plink.fam') - - -class test_filter_file(unittest.TestCase): - - def test_filter(self): - filter = ps.FilterFile('test/vcf_test/test.ind') - print filter.IDList - assert filter.n == 5 - correct = np.array(['per1', 'per2', 'per3', 'per4', 'per5']) - assert np.all(filter.IDList.values.reshape(5) == correct) - \ No newline at end of file + + def test_bad_M(self): + assert_raises( + ValueError, ps.M, os.path.join(DIR, 'parse_test/test_bad')) + + def test_M(self): + x = ps.M(os.path.join(DIR, 'parse_test/test')) + assert_array_equal(x.shape, (1, 3)) + assert_array_equal(x, [[1000, 2000, 3000]]) + + def test_M_loop(self): + x = ps.M(os.path.join(DIR, 'parse_test/test'), 2) + assert_array_equal(x.shape, (1, 2)) + assert_array_equal(x, [[3, 6]]) + + def test_M_fromlist(self): + fh = os.path.join(DIR, 'parse_test/test') + x = ps.M_fromlist([fh, fh]) + assert_array_equal(x.shape, (1, 6)) + assert_array_equal(x, np.hstack((ps.M(fh), ps.M(fh)))) + + +class Test_Fam(unittest.TestCase): + + def test_fam(self): + fam = ps.PlinkFAMFile(os.path.join(DIR, 'plink_test/plink.fam')) + assert_equal(fam.n, 5) + correct = np.array(['per0', 'per1', 'per2', 'per3', 'per4']) + assert_array_equal(fam.IDList.values.reshape((5,)), correct) + + def test_bad_filename(self): + assert_raises( + ValueError, ps.PlinkFAMFile, os.path.join(DIR, 'plink_test/plink.bim')) + + +class Test_Bim(unittest.TestCase): + + def test_bim(self): + bim = ps.PlinkBIMFile(os.path.join(DIR, 'plink_test/plink.bim')) + assert_equal(bim.n, 8) + correct = np.array( + ['rs_0', 'rs_1', 'rs_2', 'rs_3', 'rs_4', 'rs_5', 'rs_6', 'rs_7']) + assert_array_equal(bim.IDList.values.reshape(8), correct) + + def test_bad_filename(self): + assert_raises( + ValueError, ps.PlinkBIMFile, os.path.join(DIR, 'plink_test/plink.fam')) diff --git a/test/test_regressions.py b/test/test_regressions.py new file mode 100644 index 00000000..b5c87279 --- /dev/null +++ b/test/test_regressions.py @@ -0,0 +1,341 @@ +from __future__ import division +import ldscore.regressions as reg +import unittest +import numpy as np +import nose +from numpy.testing import assert_array_equal, assert_array_almost_equal +from nose.tools import assert_raises, assert_equal +np.set_printoptions(precision=4) + + +def test_update_separators(): + ii1 = [True, True, False, True, True, False, True] + ii2 = [True, True, False, True, True, False, False] + ii3 = [False, True, False, True, True, False, False] + ii4 = [False, True, False, True, True, False, True] + ii5 = [True, True, True, True, True, True, True] + iis = map(np.array, [ii1, ii2, ii3, ii4, ii5]) + ids = np.arange(len(ii1)) + for ii in iis: + s = np.arange(np.sum(ii) + 1) + t = reg.update_separators(s, ii) + assert_equal(t[0], 0) + assert_equal(t[-1], len(ii)) + assert_array_equal(ids[ii][(s[1:-2])], ids[(t[1:-2])]) + + +def test_p_z_norm(): + est = 10 + se = 1 + p, z = reg.p_z_norm(est, se) + assert z == 10 + assert_array_almost_equal(p * 1e23, 1.523971) + se = 0 + p, z = reg.p_z_norm(est, se) + assert p == 0 + assert np.isinf(z) + + +def test_append_intercept(): + x = np.ones((5, 2)) + correct_x = np.ones((5, 3)) + assert_array_equal(reg.append_intercept(x), correct_x) + + +def test_remove_brackets(): + x = ' [] [] asdf [] ' + nose.tools.assert_equal(reg.remove_brackets(x), 'asdf') + + +class Test_h2_obs_to_liab(unittest.TestCase): + + def test_bad_data(self): + assert_raises(ValueError, reg.h2_obs_to_liab, 1, 1, 0.5) + assert_raises(ValueError, reg.h2_obs_to_liab, 1, 0.5, 1) + assert_raises(ValueError, reg.h2_obs_to_liab, 1, 0, 0.5) + assert_raises(ValueError, reg.h2_obs_to_liab, 1, 0.5, 0) + + def test_approx_scz(self): + # conversion for a balanced study of a 1% phenotype is about 1/2 + x = reg.h2_obs_to_liab(1, 0.5, 0.01) + assert_array_almost_equal(x, 0.551907298063) + + +class Test_gencov_obs_to_liab(unittest.TestCase): + + def test_qt(self): + self.assertEqual(reg.gencov_obs_to_liab(1, None, None, None, None), 1) + + def test_approx_scz(self): + x = reg.gencov_obs_to_liab(1, 0.5, None, 0.01, None) + assert_array_almost_equal(x, np.sqrt(0.551907298063)) + x = reg.gencov_obs_to_liab(1, None, 0.5, None, 0.01) + assert_array_almost_equal(x, np.sqrt(0.551907298063)) + x = reg.gencov_obs_to_liab(1, 0.5, 0.5, 0.01, 0.01) + assert_array_almost_equal(x, 0.551907298063) + + +class Test_Hsq_1D(unittest.TestCase): + + def setUp(self): + self.chisq = np.ones((4, 1)) * 4 + self.ld = np.ones((4, 1)) + self.w_ld = np.ones((4, 1)) + self.N = 9 * np.ones((4, 1)) + self.M = np.matrix((7)) + self.hsq = reg.Hsq( + self.chisq, self.ld, self.w_ld, self.N, self.M, n_blocks=3, intercept=1) + + def test_weights(self): + hsq = 0.5 + w = reg.Hsq.weights(self.ld, self.w_ld, self.N, self.M, hsq) + assert_array_equal(w.shape, self.ld.shape) + assert_array_almost_equal( + w[0, 0], 0.5 / np.square(1 + hsq * self.N / self.M)) + # test that it deals correctly with out-of-bounds h2 + assert_array_almost_equal(reg.Hsq.weights(self.ld, self.w_ld, self.N, self.M, 1), + reg.Hsq.weights(self.ld, self.w_ld, self.N, self.M, 2)) + assert_array_almost_equal(reg.Hsq.weights(self.ld, self.w_ld, self.N, self.M, 0), + reg.Hsq.weights(self.ld, self.w_ld, self.N, self.M, -1)) + + def test_summarize_chisq(self): + chisq = np.arange(100).reshape((100, 1)) + mean_chisq, lambda_gc = self.hsq._summarize_chisq(chisq) + assert_array_almost_equal(mean_chisq, 49.5) + assert_array_almost_equal(lambda_gc, 108.81512420312156) + + def test_summary(self): + # not much to test; we can at least make sure no errors at runtime + self.hsq.summary(['asdf']) + self.ld += np.arange(4).reshape((4, 1)) + self.chisq += np.arange(4).reshape((4, 1)) + hsq = reg.Hsq( + self.chisq, self.ld, self.w_ld, self.N, self.M, n_blocks=3) + hsq.summary(['asdf']) + # test ratio printout with mean chi^2 < 1 + hsq.mean_chisq = 0.5 + hsq.summary(['asdf']) + + def test_update(self): + pass + + def test_aggregate(self): + chisq = np.ones((10, 1)) * 3 / 2 + ld = np.ones((10, 1)) * 100 + N = np.ones((10, 1)) * 100000 + M = 1e7 + agg = reg.Hsq.aggregate(chisq, ld, N, M) + assert_array_almost_equal(agg, 0.5) + agg = reg.Hsq.aggregate(chisq, ld, N, M, intercept=1.5) + assert_array_almost_equal(agg, 0) + + +class Test_Coef(unittest.TestCase): + + def setUp(self): + self.hsq1 = 0.2 + self.hsq2 = 0.7 + ld = (np.abs(np.random.normal(size=800)) + 1).reshape((400, 2)) + N = np.ones((400, 1)) * 1e5 + self.M = np.ones((1, 2)) * 1e7 / 2.0 + chisq = 1 + 1e5 * (ld[:, 0] * self.hsq1 / self.M[0, 0] + + ld[:, 1] * self.hsq2 / self.M[0, 1]).reshape((400, 1)) + w_ld = np.ones_like(chisq) + self.hsq_noint = reg.Hsq( + chisq, ld, w_ld, N, self.M, n_blocks=3, intercept=1) + self.hsq_int = reg.Hsq(chisq, ld, w_ld, N, self.M, n_blocks=3) + print self.hsq_noint.summary() + print self.hsq_int.summary() + + def test_coef(self): + a = [self.hsq1 / self.M[0, 0], self.hsq2 / self.M[0, 1]] + assert_array_almost_equal(self.hsq_noint.coef, a) + assert_array_almost_equal(self.hsq_int.coef, a) + + def test_cat_hsq(self): + a = [[self.hsq1, self.hsq2]] + assert_array_almost_equal(self.hsq_noint.cat, a) + assert_array_almost_equal(self.hsq_int.cat, a) + + def test_tot(self): + a = self.hsq1 + self.hsq2 + assert_array_almost_equal(self.hsq_noint.tot, a) + assert_array_almost_equal(self.hsq_int.tot, a) + + def test_prop_hsq(self): + d = self.hsq1 + self.hsq2 + a = [[self.hsq1 / d, self.hsq2 / d]] + assert_array_almost_equal(self.hsq_noint.prop, a) + assert_array_almost_equal(self.hsq_int.prop, a) + + def test_enrichment(self): + d = self.hsq1 + self.hsq2 + a = [[self.hsq1 / (0.5 * d), self.hsq2 / (0.5 * d)]] + assert_array_almost_equal(self.hsq_noint.enrichment, a) + assert_array_almost_equal(self.hsq_int.enrichment, a) + + def test_intercept(self): + assert_array_almost_equal(self.hsq_int.intercept, 1) + assert_array_almost_equal(self.hsq_int.ratio, 0) + + +class Test_Hsq_2D(unittest.TestCase): + + def setUp(self): + self.chisq = np.ones((17, 1)) * 4 + self.ld = np.hstack( + [np.ones((17, 1)), np.arange(17).reshape((17, 1))]).reshape((17, 2)) + self.w_ld = np.ones((17, 1)) + self.N = 9 * np.ones((17, 1)) + self.M = np.matrix((7, 2)) + self.hsq = reg.Hsq( + self.chisq, self.ld, self.w_ld, self.N, self.M, n_blocks=3, intercept=1) + + def test_summary(self): + # not much to test; we can at least make sure no errors at runtime + self.hsq.summary(['asdf', 'qwer']) + self.ld += np.arange(17).reshape((17, 1)) + self.chisq += np.arange(17).reshape((17, 1)) + hsq = reg.Hsq( + self.chisq, self.ld, self.w_ld, self.N, self.M, n_blocks=3) + hsq.summary(['asdf', 'qwer']) + # test ratio printout with mean chi^2 < 1 + hsq.mean_chisq = 0.5 + hsq.summary(['asdf', 'qwer']) + + +class Test_Gencov_1D(unittest.TestCase): + + def setUp(self): + self.z1 = np.ones((4, 1)) * 4 + self.z2 = np.ones((4, 1)) + self.ld = np.ones((4, 1)) + self.w_ld = np.ones((4, 1)) + self.N1 = 9 * np.ones((4, 1)) + self.N2 = 7 * np.ones((4, 1)) + self.M = np.matrix((7)) + self.hsq1 = 0.5 + self.hsq2 = 0.6 + self.gencov = reg.Gencov(self.z1, self.z2, self.ld, self.w_ld, self.N1, self.N2, + self.M, self.hsq1, self.hsq2, 1.0, 1.0, n_blocks=3, intercept_gencov=1) + + def test_weights(self): + # check that hsq weights = gencov weights when z1 = z2 + ld = np.abs(np.random.normal(size=100)).reshape((100, 1)) + w_ld = np.abs(np.random.normal(size=100)).reshape((100, 1)) + N1 = np.abs(np.random.normal(size=100)).reshape((100, 1)) + N2 = N1 + M = 10 + h1, h2, rho_g = 0.5, 0.5, 0.5 + wg = reg.Gencov.weights( + ld, w_ld, N1, N2, M, h1, h2, rho_g, intercept_gencov=1.0) + wh = reg.Hsq.weights(ld, w_ld, N1, M, h1, intercept=1.0) + assert_array_almost_equal(wg, wh) + + def test_update(self): + pass + + def test_summary(self): + # not much to test; we can at least make sure no errors at runtime + self.gencov.summary(['asdf']) + self.ld += np.arange(4).reshape((4, 1)) + self.z1 += np.arange(4).reshape((4, 1)) + gencov = reg.Gencov(self.z1, self.z2, self.ld, self.w_ld, self.N1, self.N2, + self.M, self.hsq1, self.hsq2, 1.0, 1.0, n_blocks=3) + gencov.summary(['asdf']) + + def test_aggregate(self): + z1z2 = np.ones((10, 1)) / 2 + ld = np.ones((10, 1)) * 100 + N = np.ones((10, 1)) * 100000 + M = 1e7 + agg = reg.Gencov.aggregate(z1z2, ld, N, M) + assert_array_almost_equal(agg, 0.5) + agg = reg.Gencov.aggregate(z1z2, ld, N, M, intercept=0.5) + assert_array_almost_equal(agg, 0) + + +class Test_Gencov_2D(unittest.TestCase): + + def setUp(self): + self.ld = np.abs(np.random.normal(size=100).reshape((50, 2))) + 2 + self.z1 = np.random.normal(size=50).reshape((50, 1)) + self.z2 = np.random.normal(size=50).reshape((50, 1)) + self.w_ld = np.random.normal(size=50).reshape((50, 1)) + self.N1 = 9 * np.ones((50, 1)) + self.N2 = 7 * np.ones((50, 1)) + self.M = np.matrix((700, 222)) + self.hsq1 = 0.5 + self.hsq2 = 0.6 + self.gencov = reg.Gencov(self.z1, self.z2, self.ld, self.w_ld, self.N1, self.N2, + self.M, self.hsq1, self.hsq2, 1.0, 1.0, n_blocks=3, intercept_gencov=1) + + def test_summary(self): + # not much to test; we can at least make sure no errors at runtime + self.gencov.summary(['asdf', 'qwer']) + + def test_eq_hsq(self): + ''' + Gencov should be the same as hsq if z1 = z2, hsq + intercept_hsq are 0 and + all intermediate rg's are > 0 (because Hsq.weights lower-bounds the hsq guess at 0 + but Gencov.weights lower-bounds the rho_g guess at -1). The setup below guarantees + that all intermediate rho_g guesses will be 1 + + ''' + self.ld = np.abs(np.random.normal(size=100).reshape((50, 2))) + 2 + self.z1 = (np.sum(self.ld, axis=1) + 10).reshape((50, 1)) + gencov = reg.Gencov(self.z1, self.z1, self.ld, self.w_ld, self.N1, self.N1, + self.M, 0, 0, 0, 0, n_blocks=3, intercept_gencov=1) + hsq = reg.Hsq(np.square(self.z1), self.ld, self.w_ld, + self.N1, self.M, n_blocks=3, intercept=1) + print gencov.summary(['asdf', 'asdf']) + print + print hsq.summary(['asdf', 'asdf']) + assert_array_almost_equal(gencov.tot, hsq.tot) + assert_array_almost_equal(gencov.tot_se, hsq.tot_se) + assert_array_almost_equal(gencov.tot_cov, hsq.tot_cov) + + +class Test_RG_2D(unittest.TestCase): + + def setUp(self): + self.ld = np.abs(np.random.normal(size=100).reshape((50, 2))) + 2 + self.z1 = (np.sum(self.ld, axis=1) * 10).reshape((50, 1)) + self.w_ld = np.random.normal(size=50).reshape((50, 1)) + self.N1 = 9 * np.ones((50, 1)) + self.N2 = 7 * np.ones((50, 1)) + self.M = np.matrix((700, 222)) + self.hsq1 = 0.5 + self.hsq2 = 0.6 + self.rg = reg.RG(self.z1, -self.z1, self.ld, self.w_ld, self.N1, self.N1, + self.M, 1.0, 1.0, 0, n_blocks=20) + + def test_summary(self): + # just make sure it doesn't encounter any errors at runtime + print self.rg.summary() + print self.rg.summary(silly=True) + + def test_rg(self): + # won't be exactly 1 because the h2 values passed to Gencov aren't 0 + assert np.abs(self.rg.rg_ratio + 1) < 0.01 + + +class Test_RG_Bad(unittest.TestCase): + + def test_negative_h2(self): + ld = np.arange(50).reshape((50, 1)) + 0.1 + z1 = (1 / np.sum(ld, axis=1) * 10).reshape((50, 1)) + w_ld = np.ones((50, 1)) + N1 = 9 * np.ones((50, 1)) + M = np.matrix((-700)) + rg = reg.RG(z1, -z1, ld, w_ld, N1, N1, + M, 1.0, 1.0, 0, n_blocks=20) + assert rg._negative_hsq + # check no runtime errors when _negative_hsq is True + print rg.summary() + print rg.summary(silly=True) + assert rg.rg_ratio == 'NA' + assert rg.rg_se == 'NA' + assert rg.rg == 'NA' + assert rg.p == 'NA' + assert rg.z == 'NA' diff --git a/test/test_simulate.py b/test/test_simulate.py deleted file mode 100644 index aac09d98..00000000 --- a/test/test_simulate.py +++ /dev/null @@ -1,131 +0,0 @@ -from __future__ import division -import ldscore.simulate as sim -import unittest -import numpy as np -import nose -from nose_parameterized import parameterized as param - - -class test_rnorm(unittest.TestCase): - ### TODO add input checking - @param.expand([([1]),([20]),([30])]) - def test_uvn_shape(self, n): - '''Test that shape and mean behave correctly''' - x = sim.rnorm(n, mean=n, var= 0.001) - assert x.shape == (n,) - assert np.abs(np.mean(x) - n) < 1 - - @param.expand(([1],[-2],[0])) - def test_uvn_degenerate(self, mean): - '''If var == 0, all draws should equal the mean''' - x = sim.rnorm(100, mean=mean, var=0) - assert np.all(x == np.repeat(mean, 100)) - - @param.expand(([1],[10],[22])) - def test_uvn_var(self, var): - '''Check that the sample variance is close to the theoretical variance''' - x = sim.rnorm(100000, mean=0, var=var) - assert np.abs(np.var(x) - var) < 2 - - @param.expand(([np.array((1,2,5))], - [np.array((40,50,2))], - [np.array((-20,-44,88))], - )) - def test_mvn_shape(self, mean): - '''Test that shape and mean behave correctly for MVN''' - x = sim.rnorm(100, mean=mean, var=np.diag((0.0002,0.0003,0.0004)) ) - assert np.all(x.shape == (100,3) ) - assert np.all(np.abs(mean - np.mean(x,axis=0)) < 1) - - @param.expand(([np.diag((2,3))], - [np.array((1,0.5,0.5,1))], - [np.array((1,-0.3,-0.3,1))], - )) - def test_mvn_var(self, var): - '''Test that MVN gives approx correct variance''' - var = var.reshape((2,2)) - x = sim.rnorm(100000, mean=np.zeros(2), var=var) - print np.cov(x.T) - print np.all(np.cov(x.T) - var < 0.1) - - -class test_aggregateBeta(unittest.TestCase): - def test_aggregateBeta(self): - ldScore = [1,2,3] - beta = [1,1,1,1,1,1] - print sim.aggregateBeta(beta, ldScore) - assert np.all(np.squeeze(sim.aggregateBeta(beta, ldScore)) == [1,2,3]) - - -class test_pointNormal(unittest.TestCase): - def test_p_zero(self): - n = sim.pointNormal(0,size=100, loc=1) - assert np.all(n == np.ones(100)) - - def test_var_and_p(self): - n = sim.pointNormal(0.5,size=10000, loc=0) - assert np.abs(np.var(n) - 0.5) < 0.05 - assert np.abs(np.sum(n==0) - 5000) < 1000 - -class test_sampleSizeMetaAnalysis(unittest.TestCase): - def test_basic(self): - for i in xrange(5): - z1 = np.random.normal(size=5) - z2 = np.random.normal(size=5) - n1 = 100 - n2= 1000 - zMeta = sim.sampleSizeMetaAnalysis(n1, n2, z1, z2) - expMeta = z1*np.sqrt(100/1100) + z2*np.sqrt(1000/1100) - assert np.all(zMeta - expMeta < 0.01) - - @nose.tools.raises(ValueError) - def test_fixed_variants1(self): - sim.getFST(0,0.5) - - @nose.tools.raises(ValueError) - def test_fixed_variants2(self): - sim.getFST(1,0.5) - - @nose.tools.raises(ValueError) - def test_fixed_variants3(self): - sim.getFST(0.5,1) - - @nose.tools.raises(ValueError) - def test_fixed_variants4(self): - sim.getFST(0.5,0) - - -class test_getFST(unittest.TestCase): - def test_basic(self): - f1 = 0.5 - f2 = 0.3 - print sim.getFST(f1, f2) - 0.08 - assert np.abs(sim.getFST(f1, f2) - 0.08) < 0.0001 - -class test_bivariatePointNormal(unittest.TestCase): - def test_cor_to_cov(self): - p1 = 0.5 - p2 = 0.6 - p12 = 0.3 - var1 = 0.9 - var2 = 3 - cov1 = 0.4 - - def test_basic(self): - x = sim.bivariatePointNormal(0.5,0.6,0.3,0.9,3,0.5,size=10) - assert x.shape == (10,2) - - def test_var(self): - x = sim.bivariatePointNormal(0.5,0.6,0.3,0.45,1.8,0.2,size=10000) - assert np.abs(np.var(x[:,0]) - 0.45) < 0.2 - assert np.abs(np.var(x[:,1]) - 1.8) < 0.2 - assert np.abs(np.corrcoef(x.T)[0,1] - 0.2) < 0.2 - assert np.abs(np.sum(x[:,0]==0) - 5000) < 1000 - assert np.abs(np.sum(x[:,1]==0) - 4000) < 1000 - assert np.abs(np.sum(np.logical_and(x[:,1]!=0,x[:,0]!=0)) - 3000) < 1000 - - def test_cov_zero(self): - x = sim.bivariatePointNormal(0.5,0.6,0.3,0.45,1.8,0,size=10000) - assert np.abs(np.corrcoef(x.T)[0,1]**2 < 0.01) - assert np.abs(np.var(x[:,0]) - 0.45) < 0.2 - assert np.abs(np.var(x[:,1]) - 1.8) < 0.2 diff --git a/test/test_sumstats.py b/test/test_sumstats.py new file mode 100644 index 00000000..1b71b615 --- /dev/null +++ b/test/test_sumstats.py @@ -0,0 +1,484 @@ +from __future__ import division +import ldscore.sumstats as s +import ldscore.parse as ps +import unittest +import numpy as np +import pandas as pd +from pandas.util.testing import assert_series_equal, assert_frame_equal +from nose.tools import * +from numpy.testing import assert_array_equal, assert_array_almost_equal, assert_allclose +from nose.plugins.attrib import attr +import os +from ldsc import parser + +DIR = os.path.dirname(__file__) +N_REP = 1000 +s._N_CHR = 2 # having to mock 22 files is annoying + + +class Mock(object): + ''' + Dumb object for mocking args and log + ''' + + def __init__(self): + pass + + def log(self, x): + # pass + print x + +log = Mock() +args = Mock() +t = lambda attr: lambda obj: getattr(obj, attr, float('nan')) + + +def test_check_condnum(): + x = np.ones((2, 2)) + x[1, 1] += 1e-5 + args.invert_anyway = False + assert_raises(ValueError, s._check_ld_condnum, args, log, x) + args.invert_anyway = True + s._check_ld_condnum(args, log, x) # no error + + +def test_check_variance(): + ld = pd.DataFrame(np.vstack((np.ones(3), np.arange(3))).T) + M_annot = np.array([[1, 2]]) + M_annot, ld, novar_cols = s._check_variance(log, M_annot, ld) + assert_array_equal(M_annot.shape, (1, 1)) + assert_array_equal(M_annot, [[2]]) + assert_series_equal(ld.iloc[:, 0], pd.Series([0.0, 1, 2])) + assert_array_equal(novar_cols, [True, False]) + + +def test_align_alleles(): + beta = pd.Series(np.ones(6)) + alleles = pd.Series(['ACAC', 'TGTG', 'GTGT', 'AGCT', 'AGTC', 'TCTC']) + beta = s._align_alleles(beta, alleles) + assert_series_equal(beta, pd.Series([1.0, 1, 1, -1, 1, 1])) + + +def test_filter_bad_alleles(): + alleles = pd.Series(['ATAT', 'ATAG', 'DIID', 'ACAC']) + bad_alleles = s._filter_alleles(alleles) + print bad_alleles + assert_series_equal(bad_alleles, pd.Series([False, False, False, True])) + + +def test_read_annot(): + ref_ld_chr = None + ref_ld = os.path.join(DIR, 'annot_test/test') + overlap_matrix, M_tot = s._read_chr_split_files(ref_ld_chr, ref_ld, log, 'annot matrix', + ps.annot, frqfile=None) + assert_array_equal(overlap_matrix, [[1, 0, 0], [0, 2, 2], [0, 2, 2]]) + assert_array_equal(M_tot, 3) + + frqfile = os.path.join(DIR, 'annot_test/test1') + overlap_matrix, M_tot = s._read_chr_split_files(ref_ld_chr, ref_ld, log, 'annot matrix', + ps.annot, frqfile=frqfile) + assert_array_equal(overlap_matrix, [[1, 0, 0], [0, 1, 1], [0, 1, 1]]) + assert_array_equal(M_tot, 2) + + +def test_valid_snps(): + x = {'AC', 'AG', 'CA', 'CT', 'GA', 'GT', 'TC', 'TG'} + assert_equal(x, s.VALID_SNPS) + + +def test_bases(): + x = set(['A', 'T', 'G', 'C']) + assert_equal(x, set(s.BASES)) + + +def test_complement(): + assert_equal(s.COMPLEMENT, {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'}) + + +def test_warn_len(): + # nothing to test except that it doesn't throw an error at runtime + s._warn_length(log, [1]) + + +def test_match_alleles(): + m = {'ACAC', + 'ACCA', + 'ACGT', + 'ACTG', + 'AGAG', + 'AGCT', + 'AGGA', + 'AGTC', + 'CAAC', + 'CACA', + 'CAGT', + 'CATG', + 'CTAG', + 'CTCT', + 'CTGA', + 'CTTC', + 'GAAG', + 'GACT', + 'GAGA', + 'GATC', + 'GTAC', + 'GTCA', + 'GTGT', + 'GTTG', + 'TCAG', + 'TCCT', + 'TCGA', + 'TCTC', + 'TGAC', + 'TGCA', + 'TGGT', + 'TGTG'} + assert_equal(m, s.MATCH_ALLELES) + + +def test_flip_alleles(): + m = {'ACAC': False, + 'ACCA': True, + 'ACGT': True, + 'ACTG': False, + 'AGAG': False, + 'AGCT': True, + 'AGGA': True, + 'AGTC': False, + 'CAAC': True, + 'CACA': False, + 'CAGT': False, + 'CATG': True, + 'CTAG': True, + 'CTCT': False, + 'CTGA': False, + 'CTTC': True, + 'GAAG': True, + 'GACT': False, + 'GAGA': False, + 'GATC': True, + 'GTAC': True, + 'GTCA': False, + 'GTGT': False, + 'GTTG': True, + 'TCAG': False, + 'TCCT': True, + 'TCGA': True, + 'TCTC': False, + 'TGAC': False, + 'TGCA': True, + 'TGGT': True, + 'TGTG': False} + assert_equal(m, s.FLIP_ALLELES) + + +def test_strand_ambiguous(): + m = {'AC': False, + 'AG': False, + 'AT': True, + 'CA': False, + 'CG': True, + 'CT': False, + 'GA': False, + 'GC': True, + 'GT': False, + 'TA': True, + 'TC': False, + 'TG': False} + assert_equal(m, s.STRAND_AMBIGUOUS) + + +@attr('rg') +@attr('slow') +class Test_RG_Statistical(): + + @classmethod + def setUpClass(cls): + args = parser.parse_args('') + args.ref_ld = DIR + '/simulate_test/ldscore/twold_onefile' + args.w_ld = DIR + '/simulate_test/ldscore/w' + args.rg = ','.join( + (DIR + '/simulate_test/sumstats/' + str(i) for i in xrange(N_REP))) + args.out = DIR + '/simulate_test/1' + x = s.estimate_rg(args, log) + args.intercept_gencov = ','.join(('0' for _ in xrange(N_REP))) + args.intercept_h2 = ','.join(('1' for _ in xrange(N_REP))) + y = s.estimate_rg(args, log) + cls.rg = x + cls.rg_noint = y + + def test_rg_ratio(self): + assert_allclose(np.nanmean(map(t('rg_ratio'), self.rg)), 0, atol=0.02) + + def test_rg_ratio_noint(self): + assert_allclose( + np.nanmean(map(t('rg_ratio'), self.rg_noint)), 0, atol=0.02) + + def test_rg_se(self): + assert_allclose(np.nanmean(map(t('rg_se'), self.rg)), np.nanstd( + map(t('rg_ratio'), self.rg)), atol=0.02) + + def test_rg_se_noint(self): + assert_allclose(np.nanmean(map(t('rg_se'), self.rg_noint)), np.nanstd( + map(t('rg_ratio'), self.rg_noint)), atol=0.02) + + def test_gencov_tot(self): + assert_allclose( + np.nanmean(map(t('tot'), map(t('gencov'), self.rg))), 0, atol=0.02) + + def test_gencov_tot_noint(self): + assert_allclose( + np.nanmean(map(t('tot'), map(t('gencov'), self.rg_noint))), 0, atol=0.02) + + def test_gencov_tot_se(self): + assert_allclose(np.nanstd(map(t('tot'), map(t('gencov'), self.rg))), np.nanmean( + map(t('tot_se'), map(t('gencov'), self.rg))), atol=0.02) + + def test_gencov_tot_se_noint(self): + assert_allclose(np.nanstd(map(t('tot'), map(t('gencov'), self.rg_noint))), np.nanmean( + map(t('tot_se'), map(t('gencov'), self.rg_noint))), atol=0.02) + + def test_gencov_cat(self): + assert_allclose( + np.nanmean(map(t('cat'), map(t('gencov'), self.rg))), [0, 0], atol=0.02) + + def test_gencov_cat_noint(self): + assert_allclose( + np.nanmean(map(t('cat'), map(t('gencov'), self.rg_noint))), [0, 0], atol=0.02) + + def test_gencov_cat_se(self): + assert_allclose(np.nanstd(map(t('cat'), map(t('gencov'), self.rg))), np.nanmean( + map(t('cat_se'), map(t('gencov'), self.rg))), atol=0.02) + + def test_gencov_cat_se_noint(self): + assert_allclose(np.nanstd(map(t('cat'), map(t('gencov'), self.rg_noint))), np.nanmean( + map(t('cat_se'), map(t('gencov'), self.rg_noint))), atol=0.02) + + def test_gencov_int(self): + assert_allclose( + np.nanmean(map(t('intercept'), map(t('gencov'), self.rg))), 0, atol=0.1) + + def test_gencov_int_se(self): + assert_allclose(np.nanmean(map(t('intercept_se'), map(t('gencov'), self.rg))), np.nanstd( + map(t('intercept'), map(t('gencov'), self.rg))), atol=0.1) + + def test_hsq_int(self): + assert_allclose( + np.nanmean(map(t('intercept'), map(t('hsq2'), self.rg))), 1, atol=0.1) + + def test_hsq_int_se(self): + assert_allclose(np.nanmean(map(t('intercept_se'), map(t('hsq2'), self.rg))), np.nanstd( + map(t('intercept'), map(t('hsq2'), self.rg))), atol=0.1) + + +@attr('h2') +@attr('slow') +class Test_H2_Statistical(unittest.TestCase): + + @classmethod + def setUpClass(cls): + args = parser.parse_args('') + args.ref_ld = DIR + '/simulate_test/ldscore/twold_onefile' + args.w_ld = DIR + '/simulate_test/ldscore/w' + args.chisq_max = 99999 + h2 = [] + h2_noint = [] + for i in xrange(N_REP): + args.intercept_h2 = None + args.h2 = DIR + '/simulate_test/sumstats/' + str(i) + args.out = DIR + '/simulate_test/1' + h2.append(s.estimate_h2(args, log)) + args.intercept_h2 = 1 + h2_noint.append(s.estimate_h2(args, log)) + + cls.h2 = h2 + cls.h2_noint = h2_noint + + def test_tot(self): + assert_allclose(np.nanmean(map(t('tot'), self.h2)), 0.9, atol=0.05) + + def test_tot_noint(self): + assert_allclose( + np.nanmean(map(t('tot'), self.h2_noint)), 0.9, atol=0.05) + + def test_tot_se(self): + assert_allclose(np.nanmean(map(t('tot_se'), self.h2)), np.nanstd( + map(t('tot'), self.h2)), atol=0.05) + + def test_tot_se_noint(self): + assert_allclose(np.nanmean(map(t('tot_se'), self.h2_noint)), np.nanstd( + map(t('tot'), self.h2_noint)), atol=0.05) + + def test_cat(self): + x = np.nanmean(map(t('cat'), self.h2_noint), axis=0) + y = np.array((0.3, 0.6)).reshape(x.shape) + assert_allclose(x, y, atol=0.05) + + def test_cat_noint(self): + x = np.nanmean(map(t('cat'), self.h2_noint), axis=0) + y = np.array((0.3, 0.6)).reshape(x.shape) + assert_allclose(x, y, atol=0.05) + + def test_cat_se(self): + x = np.nanmean(map(t('cat_se'), self.h2), axis=0) + y = np.nanstd(map(t('cat'), self.h2), axis=0).reshape(x.shape) + assert_allclose(x, y, atol=0.05) + + def test_cat_se_noint(self): + x = np.nanmean(map(t('cat_se'), self.h2_noint), axis=0) + y = np.nanstd(map(t('cat'), self.h2_noint), axis=0).reshape(x.shape) + assert_allclose(x, y, atol=0.05) + + def test_coef(self): + # should be h^2/M = [[0.3, 0.9]] / M + coef = np.array(((0.3, 0.9))) / self.h2[0].M + for h in [self.h2, self.h2_noint]: + assert np.all(np.abs(np.nanmean(map(t('coef'), h), axis=0) - coef) < 1e6) + + def test_coef_se(self): + for h in [self.h2, self.h2_noint]: + assert_array_almost_equal(np.nanmean(map(t('coef_se'), h), axis=0), + np.nanstd(map(t('coef'), h), axis=0)) + + def test_prop(self): + for h in [self.h2, self.h2_noint]: + assert np.all(np.nanmean(map(t('prop'), h), axis=0) - [1/3, 2/3] < 0.02) + + def test_prop_se(self): + for h in [self.h2, self.h2_noint]: + assert np.all(np.nanmean(map(t('prop_se'), h), axis=0) - np.nanstd(map(t('prop'), h), axis=0) < 0.02) + + def test_int(self): + assert_allclose(np.nanmean(map(t('intercept'), self.h2)), 1, atol=0.1) + + def test_int_se(self): + assert_allclose(np.nanstd(map(t('intercept'), self.h2)), np.nanmean( + map(t('intercept_se'), self.h2)), atol=0.1) + + +class Test_Estimate(unittest.TestCase): + + def test_h2_M(self): # check --M works + args = parser.parse_args('') + args.ref_ld = DIR + '/simulate_test/ldscore/oneld_onefile' + args.w_ld = DIR + '/simulate_test/ldscore/w' + args.h2 = DIR + '/simulate_test/sumstats/1' + args.out = DIR + '/simulate_test/1' + args.print_cov = True # right now just check no runtime errors + args.print_delete_vals = True + x = s.estimate_h2(args, log) + args.M = str( + float(open(DIR + '/simulate_test/ldscore/oneld_onefile.l2.M_5_50').read())) + y = s.estimate_h2(args, log) + assert_array_almost_equal(x.tot, y.tot) + assert_array_almost_equal(x.tot_se, y.tot_se) + args.M = '1,2' + assert_raises(ValueError, s.estimate_h2, args, log) + args.M = 'foo_bar' + assert_raises(ValueError, s.estimate_h2, args, log) + + def test_h2_ref_ld(self): # test different ways of reading ref ld + args = parser.parse_args('') + args.ref_ld_chr = DIR + '/simulate_test/ldscore/twold_onefile' + args.w_ld = DIR + '/simulate_test/ldscore/w' + args.h2 = DIR + '/simulate_test/sumstats/555' + args.out = DIR + '/simulate_test/' + x = s.estimate_h2(args, log) + args.ref_ld = DIR + '/simulate_test/ldscore/twold_firstfile,' + \ + DIR + '/simulate_test/ldscore/twold_secondfile' + y = s.estimate_h2(args, log) + args.ref_ld_chr = DIR + '/simulate_test/ldscore/twold_firstfile,' + \ + DIR + '/simulate_test/ldscore/twold_secondfile' + z = s.estimate_h2(args, log) + assert_almost_equal(x.tot, y.tot) + assert_array_almost_equal(y.cat, z.cat) + assert_array_almost_equal(x.prop, y.prop) + assert_array_almost_equal(y.coef, z.coef) + + assert_array_almost_equal(x.tot_se, y.tot_se) + assert_array_almost_equal(y.cat_se, z.cat_se) + assert_array_almost_equal(x.prop_se, y.prop_se) + assert_array_almost_equal(y.coef_se, z.coef_se) + + # test statistical properties (constrain intercept here) + def test_rg_M(self): + args = parser.parse_args('') + args.ref_ld = DIR + '/simulate_test/ldscore/oneld_onefile' + args.w_ld = DIR + '/simulate_test/ldscore/w' + args.rg = ','.join( + [DIR + '/simulate_test/sumstats/1' for _ in xrange(2)]) + args.out = DIR + '/simulate_test/1' + x = s.estimate_rg(args, log)[0] + args.M = open( + DIR + '/simulate_test/ldscore/oneld_onefile.l2.M_5_50', 'rb').read().rstrip('\n') + y = s.estimate_rg(args, log)[0] + assert_array_almost_equal(x.rg_ratio, y.rg_ratio) + assert_array_almost_equal(x.rg_se, y.rg_se) + args.M = '1,2' + assert_raises(ValueError, s.estimate_rg, args, log) + args.M = 'foo_bar' + assert_raises(ValueError, s.estimate_rg, args, log) + + def test_rg_ref_ld(self): + args = parser.parse_args('') + args.ref_ld_chr = DIR + '/simulate_test/ldscore/twold_onefile' + args.w_ld = DIR + '/simulate_test/ldscore/w' + args.rg = ','.join( + [DIR + '/simulate_test/sumstats/1' for _ in xrange(2)]) + args.out = DIR + '/simulate_test/1' + args.print_cov = True # right now just check no runtime errors + args.print_delete_vals = True + x = s.estimate_rg(args, log)[0] + args.ref_ld = DIR + '/simulate_test/ldscore/twold_firstfile,' + \ + DIR + '/simulate_test/ldscore/twold_secondfile' + y = s.estimate_rg(args, log)[0] + args.ref_ld_chr = DIR + '/simulate_test/ldscore/twold_firstfile,' + \ + DIR + '/simulate_test/ldscore/twold_secondfile' + z = s.estimate_rg(args, log)[0] + assert_almost_equal(x.rg_ratio, y.rg_ratio) + assert_almost_equal(y.rg_jknife, z.rg_jknife) + assert_almost_equal(x.rg_se, y.rg_se) + + def test_no_check_alleles(self): + args = parser.parse_args('') + args.ref_ld = DIR + '/simulate_test/ldscore/oneld_onefile' + args.w_ld = DIR + '/simulate_test/ldscore/w' + args.rg = ','.join( + [DIR + '/simulate_test/sumstats/1' for _ in xrange(2)]) + args.out = DIR + '/simulate_test/1' + x = s.estimate_rg(args, log)[0] + args.no_check_alleles = True + y = s.estimate_rg(args, log)[0] + assert_equal(x.rg_ratio, y.rg_ratio) + assert_almost_equal(x.rg_jknife, y.rg_jknife) + assert_equal(x.rg_se, y.rg_se) + + def test_twostep_h2(self): + # make sure two step isn't going crazy + args = parser.parse_args('') + args.ref_ld = DIR + '/simulate_test/ldscore/oneld_onefile' + args.w_ld = DIR + '/simulate_test/ldscore/w' + args.h2 = DIR + '/simulate_test/sumstats/1' + args.out = DIR + '/simulate_test/1' + args.chisq_max = 9999999 + args.two_step = 999 + x = s.estimate_h2(args, log) + args.chisq_max = 9999 + args.two_step = 99999 + y = s.estimate_h2(args, log) + assert_allclose(x.tot, y.tot, atol=1e-5) + + def test_twostep_rg(self): + # make sure two step isn't going crazy + args = parser.parse_args('') + args.ref_ld_chr = DIR + '/simulate_test/ldscore/oneld_onefile' + args.w_ld = DIR + '/simulate_test/ldscore/w' + args.rg = ','.join( + [DIR + '/simulate_test/sumstats/1' for _ in xrange(2)]) + args.out = DIR + '/simulate_test/rg' + args.two_step = 999 + x = s.estimate_rg(args, log)[0] + args.two_step = 99999 + y = s.estimate_rg(args, log)[0] + assert_allclose(x.rg_ratio, y.rg_ratio, atol=1e-5) + assert_allclose(x.gencov.tot, y.gencov.tot, atol=1e-5) diff --git a/test/vcf_test/test.bin b/test/vcf_test/test.bin deleted file mode 100644 index ae0d0bf5..00000000 --- a/test/vcf_test/test.bin +++ /dev/null @@ -1 +0,0 @@ -ldscøÅ( \ No newline at end of file diff --git a/test/vcf_test/test.ind b/test/vcf_test/test.ind deleted file mode 100644 index 05b5642a..00000000 --- a/test/vcf_test/test.ind +++ /dev/null @@ -1,5 +0,0 @@ -per1 -per2 -per3 -per4 -per5 diff --git a/test/vcf_test/test.ldscore.bin b/test/vcf_test/test.ldscore.bin deleted file mode 100644 index b6720c19..00000000 --- a/test/vcf_test/test.ldscore.bin +++ /dev/null @@ -1 +0,0 @@ -ldscªª  \ No newline at end of file diff --git a/test/vcf_test/test.ldscore.ind b/test/vcf_test/test.ldscore.ind deleted file mode 100644 index 8e121a44..00000000 --- a/test/vcf_test/test.ldscore.ind +++ /dev/null @@ -1,2 +0,0 @@ -per1 -per2 diff --git a/test/vcf_test/test.ldscore.snp b/test/vcf_test/test.ldscore.snp deleted file mode 100644 index b021c14d..00000000 --- a/test/vcf_test/test.ldscore.snp +++ /dev/null @@ -1,11 +0,0 @@ -1 1 rs1 0 -1 2 rs2 0 -1 3 rs3 0 -1 4 rs4 0 -1 5 rs5 0 -1 6 rs6 0 -1 7 rs7 0 -1 8 rs8 0 -1 9 rs9 0 -1 10 rs10 0 - diff --git a/test/vcf_test/test.npy b/test/vcf_test/test.npy deleted file mode 100644 index 7f4c6eca..00000000 Binary files a/test/vcf_test/test.npy and /dev/null differ diff --git a/test/vcf_test/test.snp b/test/vcf_test/test.snp deleted file mode 100644 index 395b104f..00000000 --- a/test/vcf_test/test.snp +++ /dev/null @@ -1,6 +0,0 @@ -1 1 rs1 0 -1 2 rs2 0 -1 3 rs3 0 -1 4 rs4 0 -1 5 rs5 0 -1 6 rs6 0