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PRSice.R
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PRSice.R
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#!/usr/bin/env Rscript
#
# Environment stuff: This will allow us to locate the cpp file correctly
#
# For window users, the reason why PRSice doesn't work with window is due to
# area flagged with WINDOW PEOPLE. If you can go around that, then you can
# make PRSice work (though I have not debug PRSice executable in window)
# Remove annoying messages ------------------------------------------------
#options(error = quote({dump.frames(to.file=TRUE); q()}))
In_Regression <-
DEC <-
Coef <-
CI.L <-
CI.U <-
Group <-
Threshold <-
R2 <-
print.p <-
R <-
P <-
value <-
Phenotype <- Set <- PRS.R2 <- LCI <- UCI <- quant.ref <- NULL
r.version <- "2.3.3"
# Help Messages --------------------------------------
help_message <-
"usage: Rscript PRSice.R [options] <-b base_file> <-t target_file> <--prsice prsice_location>\n
\nRequired:\n
--prsice Location of the PRSice binary\n
--dir Location to install ggplot. Only require if ggplot\n
is not installed\n
\nBase File:\n
--base-info Base INFO score filtering. Format should be\n
<Column name>:<Threshold>. SNPs with info \n
score less than <Threshold> will be ignored\n
Column name default: INFO\n
Threshold default: 0.9\n
--base-maf Base MAF filtering. Format should be\n
<Column name>:<Threshold>. SNPs with maf\n
less than <Threshold> will be ignored. An\n
additional column can also be added (e.g.\n
also filter MAF for cases), using the\n
following format:\n
<Column name>:<Threshold>,<Column name>:<Threshold>\n
--beta Whether the test statistic is in the form of \n
BETA or OR. If set, test statistic is assume\n
to be in the form of BETA. Mutually exclusive\n
from --or \n
--bp Column header containing the SNP coordinate\n
Default: BP\n
--chr Column header containing the chromosome\n
Default: CHR\n
--index If set, assume the INDEX instead of NAME for\n
the corresponding columns are provided. Index\n
should be 0-based (start counting from 0)\n
--no-default Remove all default options. If set, PRSice\n
will not set any default column name and you\n
must manually provide all required columns\n
(--snp, --stat, --A1, --pvalue)\n
--or Whether the test statistic is in the form of \n
BETA or OR. If set, test statistic is assume\n
to be in the form of OR. Mutually exclusive \n
from --beta \n
--pvalue | -p Column header containing the p-value\n
Default: P\n
--snp Column header containing the SNP ID\n
Default: SNP\n
--stat Column header containing the summary statistic\n
If --beta is set, default as BETA. Otherwise,\n
will search for OR or BETA from the header\n
of the base file\n
\nTarget File:\n
--binary-target Indicate whether the target phenotype\n
is binary or not. Either T or F should be\n
provided where T represent a binary phenotype.\n
For multiple phenotypes, the input should be\n
separated by comma without space. \n
Default: T if --beta and F if --beta is not\n
--info Filter SNPs based on info score. Only used\n
for imputed target\n
--keep File containing the sample(s) to be extracted from\n
the target file. First column should be FID and\n
the second column should be IID. If --ignore-fid is\n
set, first column should be IID\n
Mutually exclusive from --remove\n
--maf Filter SNPs based on minor allele frequency (MAF)\n
--nonfounders Keep the nonfounders in the analysis\n
Note: They will still be excluded from LD calculation\n
--pheno | -f Phenotype file containing the phenotype(s).\n
First column must be FID of the samples and\n
the second column must be IID of the samples.\n
When --ignore-fid is set, first column must\n
be the IID of the samples.\n
Must contain a header if --pheno-col is\n
specified\n
--pheno-col | -F Headers of phenotypes to be included from the\n
phenotype file\n
--prevalence | -k Prevalence of all binary trait. If provided\n
will adjust the ascertainment bias of the R2.\n
Note that when multiple binary trait is found,\n
prevalence information must be provided for\n
all of them\n
--remove File containing the sample(s) to be removed from\n
the target file. First column should be FID and\n
the second column should be IID. If --ignore-fid is\n
set, first column should be IID\n
Mutually exclusive from --keep\n
--target | -t Target genotype file. Currently support\n
both BGEN and binary PLINK format. For \n
multiple chromosome input, simply substitute\n
the chromosome number with #. PRSice will\n
automatically replace # with 1-22\n
For binary plink format, you can also specify\n
a seperate fam file by <prefix>,<fam file>\n
--target-list File containing prefix of target genotype\n
files. Similar to --target but allow more \n
flexibility. Do not support external fam file\n
at the moment\n
--type File type of the target file. Support bed \n
(binary plink) and bgen format. Default: bed\n
\nDosage:\n
--allow-inter Allow the generate of intermediate file. This will\n
speed up PRSice when using dosage data as clumping\n
reference and for hard coding PRS calculation\n
--dose-thres Translate any SNPs with highest genotype probability\n
less than this threshold to missing call\n
--hard-thres A hardcall is saved when the distance to the nearest\n
hardcall is less than the hardcall threshold.\n
Otherwise a missing code is saved\n
Default is: 0.1\n
--hard Use hard coding instead of dosage for PRS construction.\n
Default is to use dosage instead of hard coding\n
\nClumping:\n
--clump-kb The distance for clumping in kb\n
Default: 250kb (1mb for PRSet)\n
--clump-r2 The R2 threshold for clumping\n
Default: 0.1\n
--clump-p The p-value threshold use for clumping.\n
Default: 1\n
--ld | -L LD reference file. Use for LD calculation. If not\n
provided, will use the post-filtered target genotype\n
for LD calculation. Support multiple chromosome input\n
Please see --target for more information\n
--ld-dose-thres Translate any SNPs with highest genotype probability\n
less than this threshold to missing call\n
--ld-geno Filter SNPs based on genotype missingness\n
--ld-hard-thres A hardcall is saved when the distance to the nearest\n
hardcall is less than the hardcall threshold.\n
Otherwise a missing code is saved\n
Default is: 0.1\n
--ld-info Filter SNPs based on info score. Only used\n
for imputed LD reference\n
--ld-keep File containing the sample(s) to be extracted from\n
the LD reference file. First column should be FID and\n
the second column should be IID. If --ignore-fid is\n
set, first column should be IID\n
Mutually exclusive from --ld-remove\n
No effect if --ld was not provided\n
--ld-list File containing prefix of LD reference files.\n
Similar to --ld but allow more \n
flexibility. Do not support external fam file\n
at the moment\n
--ld-maf Filter SNPs based on minor allele frequency\n
--ld-remove File containing the sample(s) to be removed from\n
the LD reference file. First column should be FID and\n
the second column should be IID. If --ignore-fid is\n
set, first column should be IID\n
Mutually exclusive from --ld-keep\n
--ld-type File type of the LD file. Support bed (binary plink)\n
and bgen format. Default: bed\n
--no-clump Stop PRSice from performing clumping\n
--proxy Proxy threshold for index SNP to be considered\n
as part of the region represented by the clumped\n
SNP(s). e.g. --proxy 0.8 means the index SNP will\n
represent region of any clumped SNP(s) that has a\n
R2>=0.8 even if the index SNP does not physically\n
locate within the region\n
\nCovariate:\n
--cov | -C Covariate file. First column should be FID and \n
the second column should be IID. If --ignore-fid\n
is set, first column should be IID\n
--cov-col | -c Header of covariates. If not provided, will use\n
all variables in the covariate file. By adding\n
@ in front of the string, any numbers within [\n
and ] will be parsed. E.g. @PC[1-3] will be\n
read as PC1,PC2,PC3. Discontinuous input are also\n
supported: @cov[1.3-5] will be parsed as \n
cov1,cov3,cov4,cov5\n
--cov-factor Header of categorical covariate(s). Dummy variable\n
will be automatically generated. Any items in\n
--cov-factor must also be found in --cov-col\n
Also accept continuous input (start with @).\n
\nP-value Thresholding:\n
--bar-levels Level of barchart to be plotted. When --fastscore\n
is set, PRSice will only calculate the PRS for \n
threshold within the bar level. Levels should be\n
comma separated without space\n
--fastscore Only calculate threshold stated in --bar-levels\n
--no-full By default, PRSice will include the full model, \n
i.e. p-value threshold = 1. Setting this flag will\n
disable that behaviour\n
--interval | -i The step size of the threshold. Default: 0.00005\n
--lower | -l The starting p-value threshold. Default: 5e-8\n
--model Genetic model use for regression. The genetic\n
encoding is based on the base data where the\n
encoding represent number of the coding allele\n
Available models include:\n
add - Additive model, code as 0/1/2 (default)\n
dom - Dominant model, code as 0/1/1\n
rec - Recessive model, code as 0/0/1\n
het - Heterozygous only model, code as 0/1/0\n
--missing Method to handle missing genotypes. By default, \n
final scores are averages of valid per-allele \n
scores with missing genotypes contribute an amount\n
proportional to imputed allele frequency. To throw\n
out missing observations instead (decreasing the\n
denominator in the final average when this happens),\n
use the 'SET_ZERO' modifier. Alternatively,\n
you can use the 'CENTER' modifier to shift all scores\n
to mean zero. \n
--no-regress Do not perform the regression analysis and simply\n
output all PRS.\n
--score Method to calculate the polygenic score.\n
Available methods include:\n
avg - Take the average effect size (default)\n
std - Standardize the effect size \n
con-std - Standardize the effect size using mean \n
and sd derived from control samples\n
sum - Direct summation of the effect size \n
--upper | -u The final p-value threshold. Default: 0.5\n
\nPRSet:\n
--background String to indicate a background file. This string\n
should have the format of Name:Type where type can be\n
bed - 0-based range with 3 column. Chr Start End\n
range - 1-based range with 3 column. Chr Start End\n
gene - A file contain a column of gene name\n
--bed | -B Bed file containing the selected regions.\n
Name of bed file will be used as the region\n
identifier. WARNING: Bed file is 0-based\n
--feature Feature(s) to be included from the gtf file.\n
Default: exon,CDS,gene,protein_coding.\n
--full-back Use the whole genome as background for competitive\n
p-value calculation\n
--gtf | -g GTF file containing gene boundaries. Required\n
when --msigdb is used\n
--msigdb | -m MSIGDB file containing the pathway information.\n
Require the gtf file\n
--snp-set Provide a SNP set file containing the snp set(s).\n
Two different file format is allowed:\n
SNP list format - A file containing a single\n
column of SNP ID. Name of the\n
set will be the file name or\n
can be provided using \n
--snp-set File:Name\n
MSigDB format - Each row represent a single SNP \n
set with the first column \n
containing the name of the SNP\n
set.\n
--wind-3 Add N base(s) to the 3' region of each feature(s) \n
--wind-5 Add N base(s) to the 5' region of each feature(s) \n
\nPlotting:\n
--bar-col-high Colour of the most predicting threshold\n
Default: firebrick\n
--bar-col-lower Colour of the poorest predicting threshold\n
Default: dodgerblue\n
--bar-col-p Change the colour of bar to p-value threshold\n
instead of the association with phenotype\n
--bar-palatte Colour palatte to be used for bar plotting when\n
--bar_col_p is set. Default: YlOrRd\n
--device Select different plotting devices. You can choose\n
any plotting devices supported by base R.\n
Default: png\n
--multi-plot Plot the top N phenotype / gene set in a\n
summary plot\n
--plot When set, will only perform plotting.\n
--plot-set Define the gene set to be plot. Default: Base\n
--quantile | -q Number of quantiles to plot. No quantile plot\n
will be generated when this is not provided.\n
--quant-break Quantile groupings for plotting the strata plot\n
--quant-extract | -e File containing sample ID to be plot on a separated\n
quantile e.g. extra quantile containing only \n
schizophrenia samples. Must contain IID. Should\n
contain FID if --ignore-fid isn't set.\n
--quant-ref Reference quantile for quantile plot\n
--scatter-r2 y-axis of the high resolution scatter plot should be R2\n
\nMisc:\n
--all-score Output PRS for ALL threshold. WARNING: This\n
will generate a huge file\n
--chr-id Try to construct an RS ID for SNP based on its\n
chromosome, coordinate, effective allele and \n
non-effective allele.\n
e.g. c:L-aBd is translated to: \n
<chr>:<coordinate>-<effective><noneffective>d\n
This is always true for target file, whereas for\n
base file, this is only used if the RS ID \n
wasn't provided\n
--exclude File contains SNPs to be excluded from the\n
analysis\n
--extract File contains SNPs to be included in the \n
analysis\n
--id-delim This parameter causes sample IDs to be parsed as\n
<FID><delimiter><IID>; the default delimiter\n
is '_'. \n
--ignore-fid Ignore FID for all input. When this is set,\n
first column of all file will be assume to\n
be IID instead of FID\n
--keep-ambig Keep ambiguous SNPs. Only use this option\n
if you are certain that the base and target\n
has the same A1 and A2 alleles\n
--logit-perm When performing permutation, still use logistic\n
regression instead of linear regression. This\n
will substantially slow down PRSice\n
--memory Maximum memory usage allowed (in Mb). PRSice will try\n
its best to honor this setting\n
--non-cumulate Calculate non-cumulative PRS. PRS will be reset\n
to 0 for each new P-value threshold instead of\n
adding up\n
--out | -o Prefix for all file output\n
--perm Number of permutation to perform. This swill\n
generate the empirical p-value. Recommend to\n
use value larger than 10,000\n
--print-snp Print all SNPs that remains in the analysis \n
after clumping is performed. For PRSet, Y \n
indicate the SNPs falls within the gene set \n
of interest and N otherwise. If only PRSice \n
is performed, a single \"gene set\" called \n
\"Base\" will be presented with all entries\n
marked as Y\n
--seed | -s Seed used for permutation. If not provided,\n
system time will be used as seed. When same\n
seed and same input is provided, same result\n
can be generated\n
--thread | -n Number of thread use\n
--use-ref-maf When specified, missingness imputation will be\n
performed based on the reference samples\n
--ultra Ultra aggressive memory usage. When this is enabled\n
PRSice and PRSet will try to load all genotypes into\n
memory after clumping is performed. This should\n
drastically speed up PRSice and PRSet at the expense\n
of higher memory consumption.\n
Has no effect for dosage score\n
--x-range Range of SNPs to be excluded from the whole\n
analysis. It can either be a single bed file\n
or a comma seperated list of range. Range must\n
be in the format of chr:start-end or chr:coordinate\n
--help | -h Display this help message\n"
# Library handling --------------------------------------------------------
if (!exists('startsWith', mode = 'function')) {
startsWith <- function(x, prefix) {
return(substring(x, 1, nchar(prefix)) == prefix)
}
}
libraries <-
c("ggplot2",
"data.table",
"optparse",
"methods",
"tools",
"grDevices",
"RColorBrewer")
found.library.dir <- FALSE
argv <- commandArgs(trailingOnly = TRUE)
dir.arg.idx <- grep("--dir",argv)
no.install <- length(grep("--no-install", argv)) > 0
if (length(dir.arg.idx) != 0) {
dir.arg.idx <- dir.arg.idx + 1
found.library.dir <- TRUE
}
# INSTALL_PACKAGE: Functions for automatically install all required packages
InstalledPackage <- function(package) {
available <- suppressMessages(suppressWarnings(
sapply(
package,
require,
quietly = TRUE,
character.only = TRUE,
warn.conflicts = FALSE
)
))
missing <- package[!available]
if (length(missing) > 0)
return(FALSE)
return(TRUE)
}
CRANChoosen <- function()
{
return(getOption("repos")["CRAN"] != "@CRAN@")
}
UsePackage <- function(package, dir, no.install)
{
if (!InstalledPackage(package))
{
dir.create(file.path(dir, "lib"), showWarnings = FALSE)
.libPaths(c(.libPaths(), paste(dir, "/lib", sep = "")))
if (!InstalledPackage(package) & !no.install) {
if (is.na(dir)) {
writeLines("WARNING: dir not provided, cannot install the required packages")
return(FALSE)
} else{
writeLines(paste(
"Trying to install ",
package,
" in ",
dir,
"/lib",
sep = ""
))
}
suppressMessages(suppressWarnings(
install.packages(
package,
lib = paste(dir, "/lib", sep = ""),
repos = "http://cran.rstudio.com/",
quiet = T
)
))
}
if (!InstalledPackage(package))
return(FALSE)
}
return(TRUE)
}
use.data.table <- T
use.ggplot <- T
for (library in libraries)
{
package.directory <- "."
if (found.library.dir) {
package.directory <- argv[dir.arg.idx]
}
if (!UsePackage(library, package.directory, no.install))
{
if (library == "data.table") {
use.data.table <- F
writeLines("Cannot install data.table, will fall back and use read.table instead")
writeLines("Note: It will be slower when reading large files")
} else if (library == "ggplot2") {
use.ggplot <- F
writeLines("Cannot install ggplot2, will fall back and native plotting devices")
writeLines("Note: The legends will be uglier")
} else{
stop("Error: ", library, " cannot be load nor install!")
}
}
}
# Command line arguments --------------------------------------------------
# We don't type the help message here. Will just directly use the usage information from c++
# See the Help Messages section for more information
option_list <- list(
# Base file
make_option(c("--A1"), type = "character"),
make_option(c("--A2"), type = "character"),
make_option(c("--a1"), type = "character"),
make_option(c("--a2"), type = "character"),
make_option(c("-b", "--base"), type = "character"),
make_option(c("--base-info"), type = "character", dest = "base_info"),
make_option(c("--base-maf"), type = "character", dest = "base_maf"),
make_option(c("--beta"), action = "store_true"),
make_option(c("--bp"), type = "character"),
make_option(c("--chr"), type = "character"),
make_option(c("--index"), action = "store_true"),
make_option(c("--no-default"), action = "store_true", dest = "no_default"),
make_option(c("--or"), action = "store_true"),
make_option(c("-p", "--pvalue"), type = "character"),
make_option(c("--snp"), type = "character"),
make_option(c("--stat"), type = "character"),
# Target file
make_option(c("--binary-target"), type = "character", dest = "binary_target"),
make_option(c("--geno"), type = "numeric"),
make_option(c("--info"), type = "numeric"),
make_option(c("--keep"), type = "character"),
make_option(c("--maf"), type = "numeric"),
make_option(c("--nonfounders"), action = "store_true", dest = "nonfounders"),
make_option(c("--pheno-col"), type = "character", dest = "pheno_col"),
make_option(c("--pheno-file"), type = "character", dest = "pheno_file"),
make_option(c("-f", "--pheno"), type = "character", dest = "pheno_file"),
make_option(c("-k", "--prevalence"), type = "character"),
make_option(c("--remove"), type = "character"),
make_option(c("-t", "--target"), type = "character"),
make_option(c("--target-list"), type = "character", dest = "target_list"),
make_option(c("--type"), type = "character"),
# Dosage
make_option(c("--allow-inter"), action = "store_true", dest = "allow_inter"),
make_option(c("--hard-thres"), type = "numeric", dest = "hard_thres"),
make_option(c("--dose-thres"), type = "numeric", dest = "dose_thres"),
make_option(c("--hard"), action = "store_true"),
# Clumping
make_option(c("--clump-kb"), type = "character", dest = "clump_kb"),
make_option(c("--clump-r2"), type = "numeric", dest = "clump_r2"),
make_option(c("--clump-p"), type = "numeric", dest = "clump_p"),
make_option(c("-L", "--ld"), type = "character"),
make_option(c("--ld-list"), type = "character", dest = "ld_list"),
make_option(c("--ld-geno"), type = "numeric", dest = "ld_geno"),
make_option(c("--ld-info"), type = "numeric", dest = "ld_info"),
make_option(c("--ld-hard-thres"), type = "numeric", dest = "ld_hard_thres"),
make_option(c("--ld-dose-thres"), type = "numeric", dest = "ld_dose_thres"),
make_option(c("--ld-keep"), type = "character", dest = "ld_keep"),
make_option(c("--ld-maf"), type = "numeric", dest = "ld_maf"),
make_option(c("--ld-remove"), type = "character", dest = "ld_remove"),
make_option(c("--ld-type"), type = "character", dest = "ld_type"),
make_option(c("--no-clump"), action = "store_true", dest = "no_clump"),
make_option(c("--proxy"), type = "numeric"),
# Covariates
make_option(c("-c", "--cov-col"), type = "character", dest = "cov_col"),
make_option(c("--cov-file"), type = "character", dest = "cov_file"),
make_option(c("-C", "--cov"), type = "character", dest = "cov_file"),
make_option(c("--cov-factor"), type = "character", dest = "cov_factor"),
# P-thresholding
make_option(
c("--bar-levels"),
type = "character",
dest = "bar_levels"
),
make_option(c("--fastscore"), action = "store_true"),
make_option(c("--no-full"), action = "store_true"),
make_option(c("-i", "--interval"), type = "numeric"),
make_option(c("-l", "--lower"), type = "numeric"),
make_option(c("--model"), type = "character"),
make_option(c("--missing"), type = "character"),
make_option(c("--no-regress"), action = "store_true", dest = "no_regress"),
make_option(c("--score"), type = "character"),
make_option(c("-u", "--upper"), type = "numeric"),
# PRSet
make_option(c("-B", "--bed"), type = "character"),
make_option(c("--background"), type = "character"),
make_option(c("--feature"), type = "character"),
make_option(c("--full-back"), action = "store_true", dest = "full_back"),
make_option(c("-g", "--gtf"), type = "character"),
make_option(c("-m", "--msigdb"), type = "character"),
make_option(c("--set-perm"), type = "numeric", dest = "set_perm"),
make_option(c("--wind-5"), type = "character", dest = "wind_5"),
make_option(c("--wind-3"), type = "character", dest = "wind_3"),
make_option(c("--snp-set"), type = "character", dest = "snp_set"),
# Misc
make_option(c("--all-score"), action = "store_true", dest = "all_score"),
make_option(c("--ultra"), action = "store_true"),
make_option(c("--chr-id"), type = "character", dest = "chr_id"),
make_option(c("--exclude"), type = "character"),
make_option(c("--extract"), type = "character"),
make_option(c("--enable-mmap"), action = "store_true", dest = "enable_mmap"),
make_option(c("--ignore-fid"), action = "store_true", dest = "ignore_fid"),
make_option(c("--id-delim"), type = "character"),
make_option(c("--logit-perm"), action = "store_true", dest = "logit_perm"),
make_option(c("--keep-ambig"), action = "store_true", dest = "keep_ambig"),
make_option(c("--flip-ambig"), action = "store_true", dest = "flip_ambig"),
make_option(c("--memory"), type = "character", dest="memory"),
make_option(c("-o", "--out"), type = "character", default = "PRSice"),
make_option(c("--perm"), type = "numeric"),
make_option(c("-s", "--seed"), type = "numeric"),
make_option(c("--print-snp"), action = "store_true", dest = "print_snp"),
make_option(c("--non-cumulate"), action = "store_true", dest = "non_cumulate"),
make_option(c("-n", "--thread"), type = "numeric"),
make_option(c( "--num-auto"), type = "numeric"),
make_option(c("--use-ref-maf"), action = "store_true", dest = "use_ref_maf"),
make_option(c("--x-range"), type = "character", dest = "x_range"),
#R Specified options
make_option(c("--plot"), action = "store_true"),
make_option(c("--quantile", "-q"), type = "numeric"),
make_option(c("--quant-break"), type = "character", dest = "quant_break"),
make_option(c("--multi-plot"), type = "numeric", dest = "multi_plot"),
make_option(c("--plot-set"),
type = "character",
dest = "plot_set",
default = "Base"),
make_option(c("--quant-pheno"), action = "store_true", dest = "quant_pheno"),
make_option(c("--quant-extract", "-e"), type = "character", dest = "quant_extract"),
make_option("--quant-ref", type = "numeric", dest = "quant_ref"),
make_option("--scatter-r2",
action = "store_true",
default = F,
dest = "scatter_r2"),
make_option("--bar-col-p",
action = "store_true",
default = F,
dest = "bar_col_p"),
make_option("--bar-col-low",
type = "character",
default = "dodgerblue",
dest = "bar_col_low"),
make_option("--bar-col-high",
type = "character",
default = "firebrick",
dest = "bar_col_high"),
make_option("--bar-palatte",
type = "character",
default = "YlOrRd",
dest = "bar_palatte"),
make_option("--prsice", type = "character"),
make_option("--device", type = "character", default = "png"),
make_option("--dir", type = "character")
)
# We want to know if user used --help, if they do, we will print the help message ourselves
capture <- commandArgs(trailingOnly = TRUE)
help <- (sum(c("--help", "-h") %in% capture) >= 1)
has_c <- (sum(c("--prsice") %in% capture) >= 1)
if (help) {
cat(help_message)
quit()
}
# If help is not invoked, we can start processing the input
argv <- parse_args(OptionParser(option_list = option_list))
# Exclude the non-C++ parameters
not_cpp <- c(
"help",
"plot",
"quantile",
"quant-extract",
"intermediate",
"quant-ref",
"quant-pheno",
"quant-break",
"scatter-r2",
"bar-col-p",
"bar-col-low",
"bar-col-high",
"bar-palatte",
"device",
"prsice",
"multi-plot",
"plot-set",
"dir",
"no-install"
)
# For backward compatibility. We want to remove the --cov-header in the future
if (is.null(argv$cov_col) && !is.null(argv$cov_header))
{
argv$cov_col = argv$cov_header
}
# Check help messages --------------------------------------------------
get_os <- function() {
sysinf <- Sys.info()
if (!is.null(sysinf)) {
os <- sysinf['sysname']
if (os == 'Darwin')
os <- "osx"
} else {
## mystery machine
os <- .Platform$OS.type
if (grepl("^darwin", R.version$os))
os <- "osx"
if (grepl("linux-gnu", R.version$os))
os <- "linux"
}
tolower(os)
}
# CALL_PRSICE: Call the cpp PRSice if required
# To ensure the excutable is set correctly
# For window, we might not be able to start an executable by simply adding ./
# therefore Window people will need to be careful with their parameter input
# Function to check if the argument was used
provided <- function(name, argv) {
return(name %in% names(argv))
}
os <- get_os()
if (provided("prsice", argv)) {
if (!startsWith(argv$prsice, "/") &&
!startsWith(argv$prsice, ".") && os != "windows") {
argv$prsice = paste("./", argv$prsice, sep = "")
}
}
# Running PRSice ----------------------------------------------------------
# We don't bother to check if the input is correct, the parameter should be checked by the c++ program
add_command <- function(input) {
if (length(input) == 1) {
if (is.na(input)) {
return(NA)
} else{
return(input)
}
} else{
return(paste(input, collapse = ","))
}
}
command <- ""
argv_c <- argv
names(argv_c) <- gsub("_", "-", names(argv))
flags <-
c(
"all-score",
"allow-inter",
"beta",
"fastscore",
"ignore-fid",
"index",
"keep-ambig",
"flip-ambig",
"logit-perm",
"no-clump",
"no-default",
"no-full",
"no-mt",
"no-regress",
"no-x",
"no-xy",
"no-y",
"non-cumulate",
"or",
"print-snp",
"use-ref-maf",
"ultra"
)
# Skip PRSice core function if only plotting is requirec
if (!provided("plot", argv)) {
for (i in names(argv_c)) {
# only need special processing for flags and specific inputs
if (i == "id-delim") {
if (!is.na(i)) {
command = paste(command, " --", i, " \"", argv_c[[i]], "\"", sep = "")
}
} else if (i %in% flags) {
if (argv_c[[i]])
command = paste(command, " --", i, sep = "")
} else if (i %in% not_cpp) {
# ignore
} else{
temp = add_command(argv_c[[i]])
if (!is.na(temp)) {
command = paste(command, " --", i, " ", temp, sep = "")
}
}
}
if (nchar(command) == 0) {
cat(help_message)
quit()
}
if (provided("prsice", argv_c)) {
ret <- system2(argv_c$prsice, command)
if (ret != 0 || provided(help, argv)) {
stop()
}
} else{
stop("Cannot run PRSice without the PRSice binary file")
}
}
writeLines("Begin plotting");
writeLines(paste0("Current Rscript version = ",r.version))
# Helper functions --------------------------------------------------------
# allow older version of ggplot2 to work
expand_scale <- function(mult = 0, add = 0) {
stopifnot(is.numeric(mult) && is.numeric(add))
stopifnot((length(mult) %in% 1:2) && (length(add) %in% 1:2))
mult <- rep(mult, length.out = 2)
add <- rep(add, length.out = 2)
c(mult[1], add[1], mult[2], add[2])
}
max_length <- function(x) {
info <- strsplit(as.character(x), split = "\n")[[1]]
max(sapply(info, nchar))
}
str_wrap <- function(x) {
lapply(strwrap(x, width = 25, simplify = FALSE), paste, collapse = "\n")
}
shorten_label <- function(x) {
lab <-
paste(strsplit(paste(
strsplit(as.character(x), split = "\\.")[[1]], collapse = " "
), split = "_")[[1]], collapse = " ")
return(str_wrap(lab)[[1]])
}
# Quantile functions ------------------------------------------------------
get_quantile <- function(x, num.quant, quant.ref){
quant <- as.numeric(cut(x,
breaks = unique(quantile(
x, probs = seq(0, 1, 1 / num.quant)
)),
include.lowest = T))
if(is.na(quant.ref) | is.null(quant.ref)){
quant.ref <- ceiling(num.quant / 2)
}
quant <- factor(quant, levels = c(quant.ref, seq(min(quant), max(quant), 1)[-quant.ref]))
return(quant)
}
set_uneven_quant <- function(quant.cutoff, ref.cutoff, num.quant, prs, quant.index){
quant <- get_quantile(prs, num.quant, 1)
quant <- factor(quant,1:num.quant)
quant.cut <- sort(as.numeric(strsplit(quant.cutoff, split=",")[[1]]))
if((!is.null(ref.cutoff) & sum(ref.cutoff == quant.cut)==0) | num.quant < max(quant.cut)){
stop(
"Invalid quant-break. quant-break must be smaller than total number of quantiles and quant-ref must be one of the quant-break"
)
}
prev.name <- 0
ref.level <- NULL
for(i in 1:length(quant.cut)){
up.bound <- max(which(suppressWarnings(as.numeric(levels(quant))) <= quant.cut[i]))
cur.name <- levels(quant)[up.bound]
name.level <- paste0("(",prev.name, ",", cur.name, "]")
if(prev.name==0){
name.level <- paste0("[",prev.name, ",", cur.name, "]")
}
if(!is.null(ref.cutoff)) {
if (quant.cut[i] == ref.cutoff) {
ref.level <- name.level
}
}
range <- i:up.bound
levels(quant)[range] <- rep(name.level, length(range))
prev.name <- cur.name
}
if(is.null(ref.level)){
writeLines("=======================================")
writeLines("Warning: Cannot find required reference level, will use the middle level as reference")
writeLines("=======================================")
ref.level <- levels(quant)[ceiling(length(quant.cut)/2)]
}
ref.index <- which(levels(quant)==ref.level)
quant.index <- c(ref.index,c(1:length(levels(quant)))[-ref.index])
quant<- relevel(quant, ref=ref.level)
return(list(quant, quant.index))
}
# Determine Default -------------------------------------------------------
# Bar level default -------------------------------------------------------
if(!provided("bar_levels", argv)){
if(!provided("msigdb", argv) & !provided("gtf", argv) & !provided("bed", argv)) {
# Not PRSet
argv$bar_levels <- paste(0.001, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, sep=",")
if (!provided("no_full", argv)) {
argv$bar_levels <- paste(argv$bar_levels, 1, sep=",")
}
} else if (!provided("fastscore", argv) &
!provided("lower", argv) &
!provided("upper", argv) & !provided("interval", argv)) {
# This is prset, no thresholding unless user use parameters related to thresholding
argv$bar_levels <- "1"
}else{
argv$bar_levels <- paste(0.001, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, sep=",")
if (!provided("no_full", argv)) {
argv$bar_levels <- paste(argv$bar_levels, 1, sep=",")
}
}
}
# Base check --------------------------------------------------------------
# We need to check the base when both --beta, --or and --binary-target isn't provided
# as PRSice will try to determine the binary-target flag with those input
base.beta <- provided("beta", argv)
base.or <- provided("or", argv)
if(!base.beta &
!base.or &
!provided("binary_target", argv)) {
if (!provided("base", argv)) {
stop(
"Warning: Without base file, we cannot determine if the summary statistic is beta or OR, which is used to determine if the target phenotype is binary or not. Please use --binary-target to specify the correct target phenotype type."
)
}
zz <- gzfile(argv$base)
base_header <- readLines(zz, n = 1)
close(zz)
or <- length(grep("or", base_header, ignore.case = TRUE)) == 1
beta <- length(grep("beta", base_header, ignore.case = TRUE)) == 1
if (or & beta) {
stop(
"Both OR and BETA detected. Cannot determine which one should be used. Please use --beta or --binary-target"
)
}
if (beta) {
base.beta <- T
base.or <- F
} else if (or) {
base.or <- T
base.beta <- F
}
else {
stop("Do not detect either BETA or OR. Please ensure your base input is correct")
}
}
# Determine default of binary-target ----------------------------------------
get_binary_vector <- function(input){
# Assume the input is correct. If not, then user is likely using --plot
vec <- strsplit(input, split=",")[[1]]
binary.vec <- NULL
for(i in vec){
if(endsWith(toupper(i), "F") |endsWith(toupper(i), "FALSE")){
count <- as.numeric(gsub("F", "", gsub("FALSE","", i, ignore.case=T), ignore.case=T))
if(!is.na(count)){
binary.vec <- c(binary.vec, rep(F, count))
}else{
binary.vec <- c(binary.vec, F)
}
}else if(endsWith(toupper(i), "T") |endsWith(toupper(i), "TRUE")){
count <- as.numeric(gsub("T", "", gsub("TRUE","", i, ignore.case=T), ignore.case=T))
if(!is.na(count)){
binary.vec <- c(binary.vec, rep(T,count))
}else{
binary.vec <- c(binary.vec, T)
}
}
}
return(binary.vec)
}
binary.vector <- NULL
if(!provided("binary_target", argv)){
num.pheno <- 1
if(provided("pheno_col", argv)){
num.pheno <- length(strsplit(argv$pheno_col, split=",")[[1]])
}
if(base.beta){
binary.vector <- rep(F, num.pheno)
}else if(base.or){
binary.vector <- rep(T, num.pheno)
}else{
stop("Error: Fatal logic error from Sam when parsing binary target status")
}
}else{
# Try to parse binary_target into vector of TF
binary.vector <- get_binary_vector(argv$binary_target)
}
# Sanity check for binary-target
if(provided("pheno_col", argv) ){
pheno_length <- length(strsplit(argv$pheno_col, split=",")[[1]])
binary_length <- length(binary.vector)
if(pheno_length==0 & binary_length==1){
# This is ok
}else if(pheno_length!=binary_length){
stop("Error: Number of target phenotypes doesn't match information of binary target! You must indicate whether the phenotype is binary using --binary-target\n")
}
}
# Plottings ---------------------------------------------------------------
# Standard Theme for all plots
theme_sam <- NULL
if(use.ggplot){
theme_sam <- theme_classic()+theme(axis.title=element_text(face="bold", size=18),
axis.text=element_text(size=14),
legend.title=element_text(face="bold", size=18),
legend.text=element_text(size=14),
axis.text.x=element_text(angle=45, hjust=1)
)
}
# Quantile Plots----------------------------------------------------------
call_quantile <-
function(pheno.merge,
covariance,
prefix,
num_quant,
quant.index,
pheno.as.quant,
use.residual,
use.ggplot,
binary,
extract,
device,
uneven) {
if (!pheno.as.quant) {
family <- gaussian
if (binary) {
family <- binomial
}
if(!is.null(covariance)){
pheno.merge <- merge(pheno.merge, covariance)
}
independent.variables <-
c("quantile", "Pheno", colnames(covariance[, !colnames(covariance) %in% c("FID", "IID")]))
reg <-
summary(glm(Pheno ~ ., family, data = pheno.merge[, independent.variables]))
coef.quantiles <- (reg$coefficients[1:num_quant, 1])
ci <- (1.96 * reg$coefficients[1:num_quant, 2])