From bb26f951f9a100f00bc794424022e90b4726aa0e Mon Sep 17 00:00:00 2001 From: zenalapp Date: Mon, 20 Nov 2023 16:41:13 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20duke-mal?= =?UTF-8?q?aria-collaboratory/bistro@319a4a82aa54c7103f2e62ce9757aa46b7bd9?= =?UTF-8?q?31a=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pkgdown.yml | 2 +- reference/bistro.html | 4 ++-- reference/calc_log10_lrs.html | 4 ++-- reference/calc_one_log10_lr.html | 4 ++-- reference/check_bistro_inputs.html | 2 +- search.json | 2 +- 6 files changed, 9 insertions(+), 9 deletions(-) diff --git a/pkgdown.yml b/pkgdown.yml index b355c27..db7f9aa 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -4,7 +4,7 @@ pkgdown_sha: ~ articles: bistro: bistro.html step-by-step: step-by-step.html -last_built: 2023-11-20T16:19Z +last_built: 2023-11-20T16:40Z urls: reference: https://duke-malaria-collaboratory.github.io/bistro/reference article: https://duke-malaria-collaboratory.github.io/bistro/articles diff --git a/reference/bistro.html b/reference/bistro.html index a059f24..1bceb67 100644 --- a/reference/bistro.html +++ b/reference/bistro.html @@ -88,7 +88,7 @@

Usage model_fw_stutt = FALSE, difftol = 1, threads = 4, - seed = 1, + seed = NULL, time_limit = 3, return_lrs = FALSE ) @@ -189,7 +189,7 @@

Argumentseuroformix::contLikSearch() -argument. Default: 1

+argument. Default: NULL (no seed)

time_limit
diff --git a/reference/calc_log10_lrs.html b/reference/calc_log10_lrs.html index 316aedb..8fb6c2f 100644 --- a/reference/calc_log10_lrs.html +++ b/reference/calc_log10_lrs.html @@ -82,7 +82,7 @@

Usage model_fw_stutt = FALSE, difftol = 1, threads = 4, - seed = 1, + seed = NULL, time_limit = 3, check_inputs = TRUE ) @@ -164,7 +164,7 @@

Argumentseuroformix::contLikSearch() -argument. Default: 1

+argument. Default: NULL (no seed)

time_limit
diff --git a/reference/calc_one_log10_lr.html b/reference/calc_one_log10_lr.html index 7218d3b..705e4ae 100644 --- a/reference/calc_one_log10_lr.html +++ b/reference/calc_one_log10_lr.html @@ -73,7 +73,7 @@

Usage model_fw_stutt = FALSE, difftol = 1, threads = 4, - seed = 1, + seed = NULL, time_limit = 3 ) @@ -152,7 +152,7 @@

Argumentseuroformix::contLikSearch() -argument. Default: 1

+argument. Default: NULL (no seed)

time_limit
diff --git a/reference/check_bistro_inputs.html b/reference/check_bistro_inputs.html index f8946f8..c38cb0e 100644 --- a/reference/check_bistro_inputs.html +++ b/reference/check_bistro_inputs.html @@ -177,7 +177,7 @@

Argumentseuroformix::contLikSearch() -argument. Default: 1

+argument. Default: NULL (no seed)

time_limit
diff --git a/search.json b/search.json index 86fd595..c9da8e8 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://duke-malaria-collaboratory.github.io/bistro/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 bistro authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"the-bistro-algorithm","dir":"Articles","previous_headings":"","what":"The bistro algorithm","title":"Introduction to using bistro","text":"bistro algorithm, R package, function within said R package. employs methods forensics identify matches bloodmeals people bit using short tandem repeat (STR) profiles human blood freshly fed bloodmeals people. can used match multi-source bloodmeals bloodmeals incomplete STR profiles. Note can use matching STR profiles research purposes, much recommend using forensic purposes. details algorithm, please refer bistro manuscript (add link ’s posted). core part algorithm contLikSearch() function euroformix package, used calculate log10 likelihood ratios (log10LRs). numerator log10LR likelihood person bitten vector denominator likelihood someone else bitten vector. information euroformix: - Manuscript: EuroForMix: open source software based continuous model evaluate STR DNA profiles mixture contributors artefacts - GitHub - Website explains GUI bistro uses log10LRs identify bloodmeal-human matches using per-bloodmeal dynamic threshold approach.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"running-bistro-in-parallel","dir":"Articles","previous_headings":"","what":"Running bistro in parallel","title":"Introduction to using bistro","text":"First, want run bistro many samples prefer run parallel, potentially cluster, check template bistro Snakemake pipeline.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"installing-the-bistro-r-package","dir":"Articles","previous_headings":"","what":"Installing the bistro R package","title":"Introduction to using bistro","text":"install bistro, run following commands: run errors, try following: Windows: install Rtools Mac: install Xcode Mac App store","code":"install.packages(\"remotes\") # install remotes if needed remotes::install_github(\"duke-malaria-collaboratory/bistro\")"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"getting-started-with-bistro","dir":"Articles","previous_headings":"","what":"Getting started with bistro","title":"Introduction to using bistro","text":"important function bistro bistro(). document take required optional inputs, well output. summary, must provide: Bloodmeal STR profiles Human STR profiles STR genotyping kit name allele peak height threshold (relative fluorescence units, RFUs) bloodmeal STR profiles function outputs matches bloodmeal-human pair. follow along vignette, first load bistro: Note load bistro, also load euroformix bistro depends euroformix package.","code":"library(bistro) #> Loading required package: euroformix"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"required-inputs","dir":"Articles","previous_headings":"","what":"Required inputs","title":"Introduction to using bistro","text":"run bistro() using example data, let’s learn required inputs. Two required, one optional often recommended, datasets required run bistro(): bloodmeal human STR profiles required, human population allele frequencies optional. two required inputs STR genotyping kit name bloodmeal STR allele peak height threshold.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"bloodmeal-str-profiles-bloodmeal_profiles","dir":"Articles","previous_headings":"Required inputs","what":"Bloodmeal STR profiles (bloodmeal_profiles)","title":"Introduction to using bistro","text":"bloodmeal STR profiles dataset one row per allele bloodmeal marker, include allele peak height. Homozygous markers included (.e. one row). must four columns named SampleName, Marker, Allele, Height. SampleName column hold character values. example included bistro: example dataset, 4 different bloodmeals:","code":"head(bloodmeal_profiles) #> SampleName Marker Allele Height #> 1 evid1 AMEL X 2136 #> 2 evid1 AMEL Y 1015 #> 3 evid1 D10S1248 13 1856 #> 4 evid1 D10S1248 14 155 #> 5 evid1 D10S1248 15 1045 #> 6 evid1 D12S391 18 297 unique(bloodmeal_profiles$SampleName) #> [1] \"evid1\" \"evid2\" \"evid3\" \"evid4\""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"human-str-profiles-human_profiles","dir":"Articles","previous_headings":"Required inputs","what":"Human STR profiles (human_profiles)","title":"Introduction to using bistro","text":"human STR profiles dataset takes similar format bloodmeal STR profiles dataset - one row per allele human marker, require peak heights. , homozygous markers included . dataset must three columns named SampleName, Marker, Allele. SampleName column hold character values. example: example dataset, 3 different human profiles:","code":"head(human_profiles) #> SampleName Marker Allele #> 1 00-JP0001-14_20142342311_NO-3241 AMEL X #> 2 00-JP0001-14_20142342311_NO-3241 AMEL Y #> 3 00-JP0001-14_20142342311_NO-3241 D10S1248 12 #> 4 00-JP0001-14_20142342311_NO-3241 D10S1248 13 #> 5 00-JP0001-14_20142342311_NO-3241 D12S391 17 #> 6 00-JP0001-14_20142342311_NO-3241 D12S391 18 unique(human_profiles$SampleName) #> [1] \"00-JP0001-14_20142342311_NO-3241\" \"P1\" #> [3] \"P2\""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"str-genotyping-kit-kit","dir":"Articles","previous_headings":"Required inputs","what":"STR genotyping kit (kit)","title":"Introduction to using bistro","text":"bistro requires input STR genotyping kit name. euroformix package includes kit parameters 23 common STR genotyping kits. also includes handy function list available kits: kit used genotype samples available defaults, modify kit.txt within euroformix package appropriate kit parameters. required parameters can usually found kit manufacturer’s documentation website.","code":"getKit() #> [1] \"testkit\" \"ESX16\" \"ESX17\" \"ESX17Fast\" #> [5] \"ESI17Fast\" \"Fusion\" \"Fusion 6C\" \"SGMPlus\" #> [9] \"Identifiler\" \"NGM\" \"NGMSElect\" \"NGMDetect\" #> [13] \"GlobalFiler\" \"PowerPlex16\" \"PowerPlex21\" \"24plex\" #> [17] \"ESSPlex\" \"ESSplexPlus\" \"ESSplexSEPlus\" \"ESSplexSEQS\" #> [21] \"ForenSeq\" \"VFP\" \"MiniFiler\""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"bloodmeal-allele-peak-threshold-peak_thresh","dir":"Articles","previous_headings":"Required inputs","what":"Bloodmeal allele peak threshold (peak_thresh)","title":"Introduction to using bistro","text":"bistro() also requires bloodmeal allele peak threshold (RFUs) provided. peaks heights threshold removed matching. prior filtering performed bloodmeal profiles based peak height, number equal greater threshold.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"human-population-allele-frequencies-pop_allele_freqs","dir":"Articles","previous_headings":"Required inputs","what":"Human population allele frequencies (pop_allele_freqs)","title":"Introduction to using bistro","text":"Human population frequencies allele locus may supplied. prefer, population allele frequencies can computed human STR profiles (see details). Note latter option suggested small set human reference profiles used. euroformix website provides publicly available population frequency datasets. Additionally, can get United States population allele frequencies many alleles NIST. like input population allele frequencies, dataset contain one column STR marker one row allele. alleles listed first column dataset column named “Allele”. entries marker-allele combination population allele frequency NA allele exist marker. example loci PowerPlex ESX 17 System (ESX17) kit.","code":"head(pop_allele_freqs) #> Allele D3S1358 TH01 D21S11 D18S51 D10S1248 D1S1656 D2S1338 #> 1 5.0 NA 0.002598441 NA NA NA NA NA #> 2 6.0 NA 0.209274435 NA NA NA NA NA #> 3 6.3 NA NA NA NA NA NA NA #> 4 7.0 NA 0.212472516 NA 0.0008984726 NA NA NA #> 5 8.0 NA 0.083649810 NA NA NA NA NA #> 6 8.2 NA NA NA NA NA NA NA #> D16S539 D22S1045 VWA D8S1179 FGA D2S441 D12S391 D19S433 SE33 #> 1 NA NA NA NA NA NA NA NA NA #> 2 0.0008992806 NA NA NA NA NA NA NA NA #> 3 NA NA NA NA NA NA NA NA NA #> 4 NA NA NA NA NA NA NA NA NA #> 5 0.0086930456 NA NA 0.01130226 NA NA NA NA NA #> 6 NA NA NA NA NA NA NA NA 0.002506945"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"running-bistro","dir":"Articles","previous_headings":"","what":"Running bistro()","title":"Introduction to using bistro","text":"Now enough background run bistro(), let’s test using example data included package: ’ll notice bunch messages print screen: One markers (AMEL) kit pop_allele_freqs. 6 peaks 200 RFU threshold removed, filtering one bloodmeals peaks remaining threshold. Notification log10LRs (log10 likelihood ratios) calculated bloodmeal-human pair (4 bloodmeals 3 humans). can take , progress also printed . Notification matches identified. Let’s take look output: output contains 8 columns: bloodmeal_id: bloodmeal sample name. est_noc: estimated number contributors (NOCs) bloodmeal. equals one, ’s predicted single-source bloodmeal; greater one, ’s predicted multi-source bloodmeal. Note sources mean humans . locus_count: number loci STR-typed bloodmeal. match: whether bloodmeal STR profile matched human database (yes ). human_id: human match bloodmeal (NA match). log10_lr: log10 likelihood ratio bloodmeal-human match (NA match). numerator log10LR likelihood person bitten bloodmeal denominator likelihood someone else bitten bloodmeal. notes: bloodmeal doesn’t match (see details). thresh_low: log10LR threshold match made. important thing notice , even though 4 bloodmeals, 5 rows output dataset. , bloodmeal appear least dataset, multiple rows present bloodmeals match multiple people (one row per match). example, , evid1 matches 2 different people (P1 P2) Please keep mind performing data analysis. notes column provides information match: passed filters: match. > min NOC matches: matches expected resolved. can happen closely related people human reference dataset. shared alleles: people human reference dataset alleles overlapped alleles bloodmeal. log10LRs < 1.5: none log10LRs high enough considered match. euroformix error: euroformix::contLikSearch() function run successfully. often happens incomplete bloodmeal profiles (.e. markers succesfully STR-typed). peaks threshold: bloodmeal STR peaks peak height threshold. timed : log-likelihood took long calculate. want give time, can modify time_limit argument (see ).","code":"bistro_output <- bistro( bloodmeal_profiles = bloodmeal_profiles, human_profiles = human_profiles, pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200 ) #> 1/17 markers in kit but not in pop_allele_freqs: AMEL #> Formatting bloodmeal profiles #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold #> Formatting human profiles #> Markers being used: D10S1248, D12S391, D16S539, D18S51, D19S433, D1S1656, D21S11, D22S1045, D2S1338, D2S441, D3S1358, D8S1179, FGA, SE33, TH01, VWA #> Calculating log10LRs #> # bloodmeal ids: 3 #> # human ids: 3 #> Bloodmeal id 1/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 2/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 3/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Identifying matches bistro_output #> # A tibble: 4 × 8 #> bloodmeal_id locus_count est_noc match human_id log10_lr notes thresh_low #> #> 1 evid1 16 2 yes P1 21.8 passed al… 9.5 #> 2 evid1 16 2 yes P2 10.3 passed al… 9.5 #> 3 evid2 1 2 no NA NA all log10… NA #> 4 evid3 8 1 no NA NA all log10… NA"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"using-computed-population-allele-frequencies","dir":"Articles","previous_headings":"","what":"Using computed population allele frequencies","title":"Introduction to using bistro","text":"want population allele frequencies computed inputted human reference profiles, must set calc_allele_freqs = TRUE: Note , use builtin dataset example , recommend using method human dataset 3 people. example may make sense relatively comprehensive set human reference profiles study site.","code":"bistro( bloodmeal_profiles = bloodmeal_profiles, human_profiles = human_profiles, calc_allele_freqs = TRUE, kit = \"ESX17\", peak_thresh = 200 )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"running-bistro-on-a-subset-of-samples","dir":"Articles","previous_headings":"","what":"Running bistro() on a subset of samples","title":"Introduction to using bistro","text":"bistro() can take long time run, especially many samples closely related reference population. Therefore, running samples, highly recommend testing small subset samples make sure everything working expect. , can use bloodmeal_ids human_ids arguments tell bistro() ids want compare: can also useful like parallelize bistro(), example using workflow manager Snakemake NextFlow. ’d like try , template Snakemake pipeline running bistro. highly recommend planning run many samples.","code":"bistro( bloodmeal_profiles = bloodmeal_profiles, human_profiles = human_profiles, pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200, bloodmeal_ids = c(\"evid1\", \"evid2\"), human_ids = \"P1\" )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"other-arguments","dir":"Articles","previous_headings":"","what":"Other arguments","title":"Introduction to using bistro","text":"arguments can modify running bistro() include: Used bistro: rm_twins: whether remove identical STR profiles likely twins prior identifying matches. Note matches people resolved (default: TRUE) time_limit: longest amount time allowed calculate log-likelihood one bloodmeal-human pair, minutes (default: 3) Used euroformix::contLikSearch() calculating log-likelihoods: rm_markers: markers use calculating LRs. default, AMEL removed standard use calculating LRs sex-specific marker model_degrad: whether model peak degradation (default: TRUE) model_bw_stutt model_fw_stutt: whether model peak backward forward stutter (default: FALSE) difftol: difference tolerance log-likelihood across 2 iterations (default: 1) threads: number threads use computing log-likelihoods (default: 4) seed: seed reproducible results (default: 1) return_lr: whether return bloodmeal-human log10LRs. useful want investigate matches perform static threshold matching (see ) (default: FALSE)","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"other-matching-algorithms","dir":"Articles","previous_headings":"","what":"Other matching algorithms","title":"Introduction to using bistro","text":"also implemented matching algorithms including: match_exact(): Exact matching markers alleles match_similarity(): Similarity matching based highest similarity value humans reference database match_static_thresh(): Matching based static log10LR threshold. algorithms take similar inputs bistro(), case match_static_thresh(), bistro() output. Look upcoming manuscript comparison methods.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"identifying-bloodmeals-derived-from-the-same-host-without-a-reference-human-database","dir":"Articles","previous_headings":"","what":"Identifying bloodmeals derived from the same host without a reference human database","title":"Introduction to using bistro","text":"don’t reference database human samples still like identify bloodmeals derived host, can use create_db_from_bloodmeals() function, input human database bistro() function. see works, first load example data bistro manuscript: create human database samples, can run create_db_from_bloodmeals(), creates human profiles complete single-source bloodmeal profiles: can run bistro() usual:","code":"samples <- readr::read_csv(\"https://raw.githubusercontent.com/duke-malaria-collaboratory/bistro_validation/main/data/provedit/provedit_samples_mass200thresh.csv\") hu_db <- create_db_from_bloodmeals(samples, \"identifiler\", 200) bistro(samples, hu_db, \"identifiler\", 200, bloodmeal_ids = \"A02_RD14-0003-23d3a-0.2IP-Q1.2_001.10sec.fsa\", human_ids = paste0(\"H\", 20:24), calc_allele_freqs = TRUE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"running-bistro-step-by-step","dir":"Articles","previous_headings":"","what":"Running bistro step-by-step","title":"Introduction to using bistro","text":"’re interested learning run sub-functions bistro() individually, head vignette(\"step--step\").","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/step-by-step.html","id":"calculate-human-population-allele-frequencies","dir":"Articles","previous_headings":"","what":"Calculate human population allele frequencies","title":"Run bistro step-by-step","text":"needed, can first calculate population allele frequencies human profile database: use built-population allele frequencies since accurate computing allele frequencies three example profiles human_profiles.","code":"pop_freqs_computed <- calc_allele_freqs(human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/step-by-step.html","id":"prepare-input-str-profiles","dir":"Articles","previous_headings":"","what":"Prepare input STR profiles","title":"Run bistro step-by-step","text":"calculating log10LRs, must ensure bloodmeal human profiles prepared correctly. can using prep_bloodmeal_profiles() prep_human_profiles(), respectively. functions: Removes markers user want use Removes duplicate rows ensure homozygous alleles included prep_bloodmeal_profiles() also optionally filters peakes user-defined threshold prep_human_profiles() also optionally removes identical twins (resolved)","code":"bm_profs <- prep_bloodmeal_profiles(bloodmeal_profiles, peak_thresh = 200) #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold hu_profs <- prep_human_profiles(human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/step-by-step.html","id":"calculate-log10lrs-for-bloodmeal-human-pairs","dir":"Articles","previous_headings":"","what":"Calculate log10LRs for bloodmeal-human pairs","title":"Run bistro step-by-step","text":"Next, can use prepared profiles compute log10LRs bloodmeal-human pair (subset pairs). Note identify matches, log10LRs bloodmeal human profiles reference database. example compute log10LRs one bloodmeal humans database:","code":"log10_lrs <- calc_log10_lrs(bm_profs, hu_profs, bloodmeal_ids = \"evid1\", pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200 ) #> 1/17 markers in kit but not in bloodmeal_profiles: AMEL #> 1/17 markers in kit but not in human_profiles: AMEL #> # bloodmeal ids: 1 #> # human ids: 3 #> Bloodmeal id 1/1 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/step-by-step.html","id":"identify-bloodmeal-human-matches","dir":"Articles","previous_headings":"","what":"Identify bloodmeal-human matches","title":"Run bistro step-by-step","text":"Next, can identify matches bloodmeal:","code":"matches <- identify_matches(log10_lrs)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Zena Lapp. Author, maintainer. Christine Markwalter. Author.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lapp Z, Markwalter C (2023). bistro: Bloodmeal identification STR overlap. R package version 0.1.1, https://github.com/duke-malaria-collaboratory/bistro, https://duke-malaria-collaboratory.github.io/bistro/.","code":"@Manual{, title = {bistro: Bloodmeal identification by STR overlap}, author = {Zena Lapp and Christine Markwalter}, year = {2023}, note = {R package version 0.1.1, https://github.com/duke-malaria-collaboratory/bistro}, url = {https://duke-malaria-collaboratory.github.io/bistro/}, }"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/index.html","id":"bistro-bloodmeal-identification-by-str-overlap-","dir":"","previous_headings":"","what":"Bloodmeal identification by STR overlap","title":"Bloodmeal identification by STR overlap","text":"Functions allow identification matches bloodmeals people bit using short tandem repeat (STR) profiles human blood freshly fed bloodmeals people.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Bloodmeal identification by STR overlap","text":"can install bistro like :","code":"# install.packages(\"remotes\") remotes::install_github(\"duke-malaria-collaboratory/bistro\")"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/index.html","id":"dependencies","dir":"","previous_headings":"","what":"Dependencies:","title":"Bloodmeal identification by STR overlap","text":"Depends: R (>= 4.0.0), euroformix (>= 4.0.7) Imports: codetools (>= 0.2.19), dplyr (>= 1.1.3), R.utils (>= 2.12.2), stringr (>= 1.5.0), tibble (>= 3.2.1), tidyr (>= 1.3.0) Suggests: knitr (>= 1.43), readr (>= 2.1.4), rmarkdown (>= 2.24), testthat (>= 3.0.0)","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Bloodmeal identification by STR overlap","text":"Check vignette information.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bistro.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify human contributors to a bloodmeal using STR profiles — bistro","title":"Identify human contributors to a bloodmeal using STR profiles — bistro","text":"Identifies matches bloodmeal STR profiles database human STR profiles. euroformix::contLikSearch() function used calculate log10 likelihood ratios (log10_lrs) used identify human contributors bloodmeal. details present , see vignette('bistro').","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bistro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify human contributors to a bloodmeal using STR profiles — bistro","text":"","code":"bistro( bloodmeal_profiles, human_profiles, kit, peak_thresh, pop_allele_freqs = NULL, calc_allele_freqs = FALSE, bloodmeal_ids = NULL, human_ids = NULL, rm_twins = TRUE, rm_markers = c(\"AMEL\"), model_degrad = TRUE, model_bw_stutt = FALSE, model_fw_stutt = FALSE, difftol = 1, threads = 4, seed = 1, time_limit = 3, return_lrs = FALSE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bistro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify human contributors to a bloodmeal using STR profiles — bistro","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles. calc_allele_freqs boolean indicating whether calculate allele frequencies human_profiles. FALSE, pop_allele_freqs input required. Default: FALSE bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL rm_twins boolean indicating whether remove likely twins (identical STR profiles) human database prior identifying matches. Default: TRUE rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. model_degrad boolean indicating whether model peak degradation. Used modelDegrad argument euroformix::contLikSearch(). Default: TRUE model_bw_stutt boolean indicating whether model peak backward stutter. Used modelBWstutt argument euroformix::contLikSearch(). Default: FALSE model_fw_stutt boolean indicating whether model peak forward stutter. Used modelFWstutt argument euroformix::contLikSearch(). Default: FALSE difftol Tolerance difference log likelihoods across 2 iterations. euroformix::contLikSearch() argument. Default: 1 threads Number threads use calculating log10_lrs. euroformix::contLikSearch() argument. Default: 4 seed Seed calculating log10_lrs. euroformix::contLikSearch() argument. Default: 1 time_limit Time limit minutes run euroformix::contLikSearch() function 1 bloodmeal-human pair. Default: 3 return_lrs boolean indicating whether return log10LRs bloodmeal-human pairs. Default: FALSE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bistro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify human contributors to a bloodmeal using STR profiles — bistro","text":"Tibble matches bloodmeal-human pairs including columns listed . Note multiple matches found bloodmeal, included separate rows. bloodmeal_id: bloodmeal id locus_count: number loci successfully typed bloodmeal est_noc: estimated number contributors bloodmeal match: whether match identified given bloodmeal (yes ) human_id: match, human id (NA otherwise) log10_lr: match, log10 likelihood ratio (NA otherwise) notes: bloodmeal match return_lrs = TRUE, named list length 2 returned: matches - tibble described lrs - log10LRs bloodmeal-human pair including columns described additional column: efm_noc, number contributors used input euroformix, min(est_noc, 3).","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bistro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify human contributors to a bloodmeal using STR profiles — bistro","text":"","code":"bistro(bloodmeal_profiles, human_profiles, pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200 ) #> 1/17 markers in kit but not in pop_allele_freqs: AMEL #> Formatting bloodmeal profiles #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold #> Formatting human profiles #> Markers being used: D10S1248, D12S391, D16S539, D18S51, D19S433, D1S1656, D21S11, D22S1045, D2S1338, D2S441, D3S1358, D8S1179, FGA, SE33, TH01, VWA #> Calculating log10LRs #> # bloodmeal ids: 3 #> # human ids: 3 #> Bloodmeal id 1/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 2/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 3/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Identifying matches #> # A tibble: 4 × 8 #> bloodmeal_id locus_count est_noc match human_id log10_lr notes thresh_low #> #> 1 evid1 16 2 yes P1 21.8 passed al… 9.5 #> 2 evid1 16 2 yes P2 10.3 passed al… 9.5 #> 3 evid2 1 2 no NA NA all log10… NA #> 4 evid3 8 1 no NA NA all log10… NA"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bloodmeal_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Bloodmeal STR profiles — bloodmeal_profiles","title":"Bloodmeal STR profiles — bloodmeal_profiles","text":"Example \"bloodmeal\" STR profiles EuroForMix","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bloodmeal_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bloodmeal STR profiles — bloodmeal_profiles","text":"","code":"bloodmeal_profiles"},{"path":[]},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bloodmeal_profiles.html","id":"bloodmeal-profiles","dir":"Reference","previous_headings":"","what":"bloodmeal_profiles","title":"Bloodmeal STR profiles — bloodmeal_profiles","text":"data frame 69 rows 4 columns: SampleName Human sample identifier Marker STR marker Allele STR allele Height STR peak height","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bloodmeal_profiles.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bloodmeal STR profiles — bloodmeal_profiles","text":"http://euroformix.com/","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_allele_freqs.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate allele frequencies — calc_allele_freqs","title":"Calculate allele frequencies — calc_allele_freqs","text":"Calculate allele frequencies (generally human) population.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_allele_freqs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate allele frequencies — calc_allele_freqs","text":"","code":"calc_allele_freqs(human_profiles, rm_markers = NULL, check_inputs = TRUE)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_allele_freqs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate allele frequencies — calc_allele_freqs","text":"human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. check_inputs boolean indicating whether check inputs function. Default: TRUE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_allele_freqs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate allele frequencies — calc_allele_freqs","text":"tibble first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_allele_freqs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate allele frequencies — calc_allele_freqs","text":"","code":"calc_allele_freqs(human_profiles) #> # A tibble: 32 × 18 #> Allele AMEL D10S1248 D12S391 D16S539 D18S51 D19S433 D1S1656 D21S11 D22S1045 #> #> 1 X 0.6 NA NA NA NA NA NA NA NA #> 2 Y 0.4 NA NA NA NA NA NA NA NA #> 3 12 NA 0.2 NA 0.167 NA NA 0.167 NA NA #> 4 13 NA 0.6 NA NA 0.167 0.4 NA NA NA #> 5 17 NA NA 0.167 NA 0.333 NA NA NA NA #> 6 18 NA NA 0.333 NA NA NA NA NA NA #> 7 10 NA NA NA 0.333 NA NA NA NA NA #> 8 11 NA NA NA 0.333 0.167 NA 0.167 NA NA #> 9 14 NA NA NA NA 0.167 0.4 0.167 NA NA #> 10 29 NA NA NA NA NA NA NA 0.5 NA #> # ℹ 22 more rows #> # ℹ 8 more variables: D2S1338 , D2S441 , D3S1358 , #> # D8S1179 , FGA , SE33 , TH01 , VWA "},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_log10_lrs.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","title":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","text":"Note function preprocess bloodmeal human profile data. like preprocess way performed internally bistro() function, must run prep_bloodmeal_profiles() prep_human_profiles() first.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_log10_lrs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","text":"","code":"calc_log10_lrs( bloodmeal_profiles, human_profiles, pop_allele_freqs, kit, peak_thresh, bloodmeal_ids = NULL, human_ids = NULL, model_degrad = TRUE, model_bw_stutt = FALSE, model_fw_stutt = FALSE, difftol = 1, threads = 4, seed = 1, time_limit = 3, check_inputs = TRUE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_log10_lrs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL model_degrad boolean indicating whether model peak degradation. Used modelDegrad argument euroformix::contLikSearch(). Default: TRUE model_bw_stutt boolean indicating whether model peak backward stutter. Used modelBWstutt argument euroformix::contLikSearch(). Default: FALSE model_fw_stutt boolean indicating whether model peak forward stutter. Used modelFWstutt argument euroformix::contLikSearch(). Default: FALSE difftol Tolerance difference log likelihoods across 2 iterations. euroformix::contLikSearch() argument. Default: 1 threads Number threads use calculating log10_lrs. euroformix::contLikSearch() argument. Default: 4 seed Seed calculating log10_lrs. euroformix::contLikSearch() argument. Default: 1 time_limit Time limit minutes run euroformix::contLikSearch() function 1 bloodmeal-human pair. Default: 3 check_inputs boolean indicating whether check inputs function. Default: TRUE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_log10_lrs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","text":"tibble output bistro(), except match column every bloodmeal-human pair calculated log10_lr included. additional column efm_noc number contributors used euroformix::contLikSearch(). maximum value 3.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_log10_lrs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","text":"","code":"bm_profs <- prep_bloodmeal_profiles(bloodmeal_profiles, peak_thresh = 200) #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold hu_profs <- prep_human_profiles(human_profiles) log10_lrs <- calc_log10_lrs(bm_profs, hu_profs, bloodmeal_ids = \"evid1\", pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200 ) #> 1/17 markers in kit but not in bloodmeal_profiles: AMEL #> 1/17 markers in kit but not in human_profiles: AMEL #> # bloodmeal ids: 1 #> # human ids: 3 #> Bloodmeal id 1/1 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_one_log10_lr.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate log10_lr for one bloodmeal-human pair — calc_one_log10_lr","title":"Calculate log10_lr for one bloodmeal-human pair — calc_one_log10_lr","text":"Calculate log10_lr one bloodmeal-human pair","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_one_log10_lr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate log10_lr for one bloodmeal-human pair — calc_one_log10_lr","text":"","code":"calc_one_log10_lr( bloodmeal_profiles, bloodmeal_id, human_profiles, human_id, pop_allele_freqs, kit, peak_thresh, model_degrad = TRUE, model_bw_stutt = FALSE, model_fw_stutt = FALSE, difftol = 1, threads = 4, seed = 1, time_limit = 3 )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_one_log10_lr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate log10_lr for one bloodmeal-human pair — calc_one_log10_lr","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. bloodmeal_id Bloodmeal id SampleName column bloodmeal_profiles compute log10_lr human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. human_id Human id SampleName column human_profiles compute log10_lr pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). model_degrad boolean indicating whether model peak degradation. Used modelDegrad argument euroformix::contLikSearch(). Default: TRUE model_bw_stutt boolean indicating whether model peak backward stutter. Used modelBWstutt argument euroformix::contLikSearch(). Default: FALSE model_fw_stutt boolean indicating whether model peak forward stutter. Used modelFWstutt argument euroformix::contLikSearch(). Default: FALSE difftol Tolerance difference log likelihoods across 2 iterations. euroformix::contLikSearch() argument. Default: 1 threads Number threads use calculating log10_lrs. euroformix::contLikSearch() argument. Default: 4 seed Seed calculating log10_lrs. euroformix::contLikSearch() argument. Default: 1 time_limit Time limit minutes run euroformix::contLikSearch() function 1 bloodmeal-human pair. Default: 3","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_one_log10_lr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate log10_lr for one bloodmeal-human pair — calc_one_log10_lr","text":"tibble log10_lr bloodmeal-human pair including bloodmeal_id, human_id, locus_count (number STR loci used matching), est_noc (estimated number contributors), efm_noc (number contributors used euroformix), log10_lr (log10 likelihood ratio), notes","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_bistro_inputs.html","id":null,"dir":"Reference","previous_headings":"","what":"Check bistro inputs — check_bistro_inputs","title":"Check bistro inputs — check_bistro_inputs","text":"Check bistro inputs","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_bistro_inputs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check bistro inputs — check_bistro_inputs","text":"","code":"check_bistro_inputs( bloodmeal_profiles, human_profiles, kit, peak_thresh, pop_allele_freqs, calc_allele_freqs, bloodmeal_ids, human_ids, rm_twins, rm_markers, model_degrad, model_bw_stutt, model_fw_stutt, difftol, threads, seed, time_limit, return_lrs )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_bistro_inputs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check bistro inputs — check_bistro_inputs","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles. calc_allele_freqs boolean indicating whether calculate allele frequencies human_profiles. FALSE, pop_allele_freqs input required. Default: FALSE bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL rm_twins boolean indicating whether remove likely twins (identical STR profiles) human database prior identifying matches. Default: TRUE rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. model_degrad boolean indicating whether model peak degradation. Used modelDegrad argument euroformix::contLikSearch(). Default: TRUE model_bw_stutt boolean indicating whether model peak backward stutter. Used modelBWstutt argument euroformix::contLikSearch(). Default: FALSE model_fw_stutt boolean indicating whether model peak forward stutter. Used modelFWstutt argument euroformix::contLikSearch(). Default: FALSE difftol Tolerance difference log likelihoods across 2 iterations. euroformix::contLikSearch() argument. Default: 1 threads Number threads use calculating log10_lrs. euroformix::contLikSearch() argument. Default: 4 seed Seed calculating log10_lrs. euroformix::contLikSearch() argument. Default: 1 time_limit Time limit minutes run euroformix::contLikSearch() function 1 bloodmeal-human pair. Default: 3 return_lrs boolean indicating whether return log10LRs bloodmeal-human pairs. Default: FALSE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_bistro_inputs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check bistro inputs — check_bistro_inputs","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_calc_allele_freqs.html","id":null,"dir":"Reference","previous_headings":"","what":"Check calc_allele_freqs — check_calc_allele_freqs","title":"Check calc_allele_freqs — check_calc_allele_freqs","text":"Check calc_allele_freqs","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_calc_allele_freqs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check calc_allele_freqs — check_calc_allele_freqs","text":"","code":"check_calc_allele_freqs(calc_allele_freqs)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_calc_allele_freqs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check calc_allele_freqs — check_calc_allele_freqs","text":"calc_allele_freqs boolean indicating whether calculate allele frequencies human_profiles. FALSE, pop_allele_freqs input required. Default: FALSE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_calc_allele_freqs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check calc_allele_freqs — check_calc_allele_freqs","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_colnames.html","id":null,"dir":"Reference","previous_headings":"","what":"Check column names — check_colnames","title":"Check column names — check_colnames","text":"Check column names","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_colnames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check column names — check_colnames","text":"","code":"check_colnames(df, expected_colnames)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_colnames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check column names — check_colnames","text":"df Dataframe check expected_colnames Column names required resent","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_colnames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check column names — check_colnames","text":"Nothing error","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_create_db_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input to `create_db_from_bloodmeals()`` — check_create_db_input","title":"Check input to `create_db_from_bloodmeals()`` — check_create_db_input","text":"Check input `create_db_from_bloodmeals()``","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_create_db_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input to `create_db_from_bloodmeals()`` — check_create_db_input","text":"","code":"check_create_db_input(bloodmeal_profiles, kit, peak_thresh, rm_markers)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_create_db_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input to `create_db_from_bloodmeals()`` — check_create_db_input","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_create_db_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input to `create_db_from_bloodmeals()`` — check_create_db_input","text":"number alleles complete profile","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_heights.html","id":null,"dir":"Reference","previous_headings":"","what":"Check peak_thresh — check_heights","title":"Check peak_thresh — check_heights","text":"Check peak_thresh","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_heights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check peak_thresh — check_heights","text":"","code":"check_heights(heights, peak_thresh)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_heights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check peak_thresh — check_heights","text":"heights vector peak heights peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch().","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_heights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check peak_thresh — check_heights","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_ids.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input ids — check_ids","title":"Check input ids — check_ids","text":"Check input ids","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_ids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input ids — check_ids","text":"","code":"check_ids(vec, vec_name)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_ids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input ids — check_ids","text":"vec vector check vec_name vector name","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_ids.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input ids — check_ids","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_if_allele_freqs.html","id":null,"dir":"Reference","previous_headings":"","what":"Check calc_if_allele_freqs — check_if_allele_freqs","title":"Check calc_if_allele_freqs — check_if_allele_freqs","text":"Check calc_if_allele_freqs","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_if_allele_freqs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check calc_if_allele_freqs — check_if_allele_freqs","text":"","code":"check_if_allele_freqs(pop_allele_freqs, calc_allele_freqs, kit_df)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_if_allele_freqs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check calc_if_allele_freqs — check_if_allele_freqs","text":"pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles. calc_allele_freqs boolean indicating whether calculate allele frequencies human_profiles. FALSE, pop_allele_freqs input required. Default: FALSE kit_df Kit dataframe euroformix::getKit()","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_if_allele_freqs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check calc_if_allele_freqs — check_if_allele_freqs","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_bool.html","id":null,"dir":"Reference","previous_headings":"","what":"Check is boolean — check_is_bool","title":"Check is boolean — check_is_bool","text":"Check boolean","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_bool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check is boolean — check_is_bool","text":"","code":"check_is_bool(vec, vec_name)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_bool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check is boolean — check_is_bool","text":"vec vector check vec_name vector name","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_bool.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check is boolean — check_is_bool","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Check is numeric — check_is_numeric","title":"Check is numeric — check_is_numeric","text":"Check numeric","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check is numeric — check_is_numeric","text":"","code":"check_is_numeric(vec, vec_name, pos = FALSE)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check is numeric — check_is_numeric","text":"vec vector check vec_name vector name pos whether number must positive","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check is numeric — check_is_numeric","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_kit.html","id":null,"dir":"Reference","previous_headings":"","what":"Check kit — check_kit","title":"Check kit — check_kit","text":"Check kit","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_kit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check kit — check_kit","text":"","code":"check_kit(kit)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_kit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check kit — check_kit","text":"kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_kit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check kit — check_kit","text":"Error kit exist, dataframe kit otherwise","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_peak_thresh.html","id":null,"dir":"Reference","previous_headings":"","what":"Check peak_thresh — check_peak_thresh","title":"Check peak_thresh — check_peak_thresh","text":"Check peak_thresh","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_peak_thresh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check peak_thresh — check_peak_thresh","text":"","code":"check_peak_thresh(peak_thresh)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_peak_thresh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check peak_thresh — check_peak_thresh","text":"peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch().","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_peak_thresh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check peak_thresh — check_peak_thresh","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_pkg_version.html","id":null,"dir":"Reference","previous_headings":"","what":"Check package version — check_pkg_version","title":"Check package version — check_pkg_version","text":"Check package version","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_pkg_version.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check package version — check_pkg_version","text":"","code":"check_pkg_version(pkg, curr_version, version)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_pkg_version.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check package version — check_pkg_version","text":"pkg package test curr_version current package version version required package version","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_pkg_version.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check package version — check_pkg_version","text":"nothing error package version old","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_setdiff_markers.html","id":null,"dir":"Reference","previous_headings":"","what":"Check what markers are present — check_setdiff_markers","title":"Check what markers are present — check_setdiff_markers","text":"Check markers present","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_setdiff_markers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check what markers are present — check_setdiff_markers","text":"","code":"check_setdiff_markers(markers1, markers2, markers1_name, markers2_name)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_setdiff_markers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check what markers are present — check_setdiff_markers","text":"markers1 first vector check markers2 second vector check markers1_name first vector name markers2_name second vector name","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_setdiff_markers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check what markers are present — check_setdiff_markers","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/create_db_from_bloodmeals.html","id":null,"dir":"Reference","previous_headings":"","what":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","title":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","text":"Create human profile database bloodmeal profiles","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/create_db_from_bloodmeals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","text":"","code":"create_db_from_bloodmeals( bloodmeal_profiles, kit, peak_thresh, rm_markers = c(\"AMEL\") )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/create_db_from_bloodmeals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/create_db_from_bloodmeals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","text":"Human database created complete single-source bloodmeals. Complete defined number markers kit minus number markers rm_markers.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/create_db_from_bloodmeals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","text":"","code":"if (FALSE) { # load example data path_to_data <- paste0( \"https://raw.githubusercontent.com/duke-malaria-collaboratory/\", \"bistro_validation/main/data/provedit/provedit_samples_mass200thresh.csv\" ) samples <- readr::read_csv(path_to_data) create_db_from_bloodmeals(samples, kit = \"identifiler\", peak_thresh = 200) }"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/filter_peaks.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter peak heights that are under threshold — filter_peaks","title":"Filter peak heights that are under threshold — filter_peaks","text":"Filter peak heights threshold","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/filter_peaks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter peak heights that are under threshold — filter_peaks","text":"","code":"filter_peaks(bloodmeal_profiles, peak_thresh)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/filter_peaks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter peak heights that are under threshold — filter_peaks","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch().","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/filter_peaks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter peak heights that are under threshold — filter_peaks","text":"Filtered dataframe","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_allele_freqs.html","id":null,"dir":"Reference","previous_headings":"","what":"Format population allele frequencies — format_allele_freqs","title":"Format population allele frequencies — format_allele_freqs","text":"Format population allele frequencies input euroformix contLikSearch() function.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_allele_freqs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format population allele frequencies — format_allele_freqs","text":"","code":"format_allele_freqs(pop_allele_freqs)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_allele_freqs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format population allele frequencies — format_allele_freqs","text":"pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_allele_freqs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format population allele frequencies — format_allele_freqs","text":"list allele frequencies population format required input euroformix contLikSearch() function","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_bloodmeal_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Format bloodmeal STR profiles — format_bloodmeal_profiles","title":"Format bloodmeal STR profiles — format_bloodmeal_profiles","text":"Format bloodmeal STR profiles input euroformix contLikSearch() function.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_bloodmeal_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format bloodmeal STR profiles — format_bloodmeal_profiles","text":"","code":"format_bloodmeal_profiles(bloodmeal_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_bloodmeal_profiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format bloodmeal STR profiles — format_bloodmeal_profiles","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_bloodmeal_profiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format bloodmeal STR profiles — format_bloodmeal_profiles","text":"list lists including alleles sample format required input euroformix contLikSearch() function.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_human_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Format human STR profiles — format_human_profiles","title":"Format human STR profiles — format_human_profiles","text":"Format human STR profiles input euroformix contLikSearch() function.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_human_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format human STR profiles — format_human_profiles","text":"","code":"format_human_profiles(human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_human_profiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format human STR profiles — format_human_profiles","text":"human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_human_profiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format human STR profiles — format_human_profiles","text":"list lists including alleles sample format required input euroformix contLikSearch() function","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_bloodmeal_human_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify matches between bloodmeal and human STR profiles\nbased on similarity values — get_bloodmeal_human_similarity","title":"Identify matches between bloodmeal and human STR profiles\nbased on similarity values — get_bloodmeal_human_similarity","text":"Identify matches bloodmeal human STR profiles based similarity values","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_bloodmeal_human_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify matches between bloodmeal and human STR profiles\nbased on similarity values — get_bloodmeal_human_similarity","text":"","code":"get_bloodmeal_human_similarity(bloodmeal_profiles, human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_bloodmeal_human_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify matches between bloodmeal and human STR profiles\nbased on similarity values — get_bloodmeal_human_similarity","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_bloodmeal_human_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify matches between bloodmeal and human STR profiles\nbased on similarity values — get_bloodmeal_human_similarity","text":"dataframe two columns: bloodmeal_id: bloodmeal ID human_id: human ID match, \"match\" match","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_human_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Get pairwise human STR profile similarities — get_human_similarity","title":"Get pairwise human STR profile similarities — get_human_similarity","text":"Get pairwise human STR profile similarities","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_human_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get pairwise human STR profile similarities — get_human_similarity","text":"","code":"get_human_similarity(human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_human_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get pairwise human STR profile similarities — get_human_similarity","text":"human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_human_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get pairwise human STR profile similarities — get_human_similarity","text":"Dataframe three columns: hu1: human ID 1 hu2: human ID 2 similarity: similarity value (# exact locus matches / # loci)","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/human_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Human reference database — human_profiles","title":"Human reference database — human_profiles","text":"Example human STR profiles EuroForMix","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/human_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Human reference database — human_profiles","text":"","code":"human_profiles"},{"path":[]},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/human_profiles.html","id":"human-profiles","dir":"Reference","previous_headings":"","what":"human_profiles","title":"Human reference database — human_profiles","text":"data frame 96 rows 3 columns: SampleName Human sample identifier Marker STR marker Allele STR allele","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/human_profiles.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Human reference database — human_profiles","text":"http://euroformix.com/","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_matches.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify matches for multiple bloodmeal-human pairs — identify_matches","title":"Identify matches for multiple bloodmeal-human pairs — identify_matches","text":"Identify matches multiple bloodmeal-human pairs","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_matches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify matches for multiple bloodmeal-human pairs — identify_matches","text":"","code":"identify_matches(log10_lrs, bloodmeal_ids = NULL, check_inputs = TRUE)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_matches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify matches for multiple bloodmeal-human pairs — identify_matches","text":"log10_lrs Output calc_log10_lrs() bistro return_lrs = TRUE (lrs: second element list) bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL check_inputs boolean indicating whether check inputs function. Default: TRUE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_matches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify matches for multiple bloodmeal-human pairs — identify_matches","text":"tibble output bistro().","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_matches.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify matches for multiple bloodmeal-human pairs — identify_matches","text":"","code":"bm_profs <- prep_bloodmeal_profiles(bloodmeal_profiles, peak_thresh = 200) #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold hu_profs <- prep_human_profiles(human_profiles) log10_lrs <- calc_log10_lrs(bm_profs, hu_profs, bloodmeal_ids = \"evid1\", pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200 ) #> 1/17 markers in kit but not in bloodmeal_profiles: AMEL #> 1/17 markers in kit but not in human_profiles: AMEL #> # bloodmeal ids: 1 #> # human ids: 3 #> Bloodmeal id 1/1 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 matches <- identify_matches(log10_lrs)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_one_match_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify matches between a bloodmeal and human references — identify_one_match_set","title":"Identify matches between a bloodmeal and human references — identify_one_match_set","text":"Identify matches bloodmeal human references","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_one_match_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify matches between a bloodmeal and human references — identify_one_match_set","text":"","code":"identify_one_match_set(log10_lrs, bloodmeal_id)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_one_match_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify matches between a bloodmeal and human references — identify_one_match_set","text":"log10_lrs output calc_log10_lrs() bloodmeal_id Bloodmeal id SampleName column bloodmeal_profiles compute log10_lr","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_one_match_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify matches between a bloodmeal and human references — identify_one_match_set","text":"tibble matches bloodmeal-human pairs including bloodmeal_id, locus_count (number STR loci used matching), est_noc (estimated number contributors), match, human_id (match), log10_lr (log10 likelihood ratio), notes","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_exact.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify exact matches between bloodmeal and human STR profiles — match_exact","title":"Identify exact matches between bloodmeal and human STR profiles — match_exact","text":"Match exact STR profiles bloodmeals humans. Note bloodmeal peak height threshold optional used filtering. Also note rm_twins = FALSE, match twin result multiple rows returned bloodmeal.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_exact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify exact matches between bloodmeal and human STR profiles — match_exact","text":"","code":"match_exact( bloodmeal_profiles, human_profiles, bloodmeal_ids = NULL, human_ids = NULL, peak_thresh = NULL, rm_twins = TRUE, rm_markers = NULL )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_exact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify exact matches between bloodmeal and human STR profiles — match_exact","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). rm_twins boolean indicating whether remove likely twins (identical STR profiles) human database prior identifying matches. Default: TRUE rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_exact.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify exact matches between bloodmeal and human STR profiles — match_exact","text":"Dataframe three columns: bloodmeal_id: bloodmeal ID human_id: human ID match (NA) match: whether match identified (yes )","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_exact.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify exact matches between bloodmeal and human STR profiles — match_exact","text":"","code":"match_exact(bloodmeal_profiles, human_profiles) #> # A tibble: 4 × 3 #> bloodmeal_id human_id match #> #> 1 evid1 NA no #> 2 evid2 NA no #> 3 evid3 NA no #> 4 evid4 NA no"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","title":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","text":"Match STR profiles bloodmeals humans based threshold similar human-human pair. Twins included computing threshold. Note bloodmeal peak height threshold optional used filtering. Also note rm_twins = FALSE, match twin result multiple rows returned bloodmeal.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","text":"","code":"match_similarity( bloodmeal_profiles, human_profiles, bloodmeal_ids = NULL, human_ids = NULL, peak_thresh = NULL, rm_twins = TRUE, rm_markers = NULL, return_similarities = FALSE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). rm_twins boolean indicating whether remove likely twins (identical STR profiles) human database prior identifying matches. Default: TRUE rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. return_similarities boolean indicating whether return human-human bloodmeal-human. Default: FALSE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","text":"Dataframe three columns: bloodmeal_id: bloodmeal ID human_id: human ID match (NA) match: whether match identified (yes ) similarity: similarity value match found return_similarities = TRUE, named list length 4 returned: matches: dataframe described max_hu_hu_similarity: maximum human-human similarity (threshold used matching) hu_hu_similarities: human-human similarity values, bm_hu_similarities: bloodmeal-human similarities profiles identical alleles least one marker","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","text":"","code":"match_similarity(bloodmeal_profiles, human_profiles) #> Calculating human-human similarities #> Maximum similarity between people: 0.117647058823529 #> Calculating bloodmeal-human similarities #> Identifying matches #> # A tibble: 4 × 4 #> bloodmeal_id human_id match similarity #> #> 1 evid1 P1 yes 0.294 #> 2 evid3 NA no 0.0588 #> 3 evid2 NA no NA #> 4 evid4 NA no NA"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_static_thresh.html","id":null,"dir":"Reference","previous_headings":"","what":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","title":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","text":"Match bloodmeals humans based log10LR static threshold","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_static_thresh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","text":"","code":"match_static_thresh(log10_lrs, thresh)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_static_thresh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","text":"log10_lrs Output calc_log10_lrs() bistro return_lrs = TRUE (lrs: second element list) thresh log10LR threshold matching. bloodmeal-human pairs log10LR ≥ thresh considered match.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_static_thresh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","text":"Dataframe including columns similar bistro() output.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_static_thresh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","text":"","code":"bistro_output <- bistro(bloodmeal_profiles, human_profiles, pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200, return_lrs = TRUE ) #> 1/17 markers in kit but not in pop_allele_freqs: AMEL #> Formatting bloodmeal profiles #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold #> Formatting human profiles #> Markers being used: D10S1248, D12S391, D16S539, D18S51, D19S433, D1S1656, D21S11, D22S1045, D2S1338, D2S441, D3S1358, D8S1179, FGA, SE33, TH01, VWA #> Calculating log10LRs #> # bloodmeal ids: 3 #> # human ids: 3 #> Bloodmeal id 1/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 2/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 3/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Identifying matches match_static_thresh(bistro_output$lrs, 10) #> # A tibble: 4 × 6 #> bloodmeal_id locus_count est_noc match human_id log10_lr #> #> 1 evid1 16 2 yes P1 21.8 #> 2 evid1 16 2 yes P2 10.3 #> 3 evid2 1 2 no NA NA #> 4 evid3 8 1 no NA NA"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/pop_allele_freqs.html","id":null,"dir":"Reference","previous_headings":"","what":"Human population allele frequencies — pop_allele_freqs","title":"Human population allele frequencies — pop_allele_freqs","text":"Example population allele frequencies EuroForMix (ESX17_Norway)","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/pop_allele_freqs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Human population allele frequencies — pop_allele_freqs","text":"","code":"pop_allele_freqs"},{"path":[]},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/pop_allele_freqs.html","id":"pop-allele-freqs-a-data-frame-with-rows-and-columns-the","dir":"Reference","previous_headings":"","what":"pop_allele_freqs A data frame with 76 rows and 17 columns. The","title":"Human population allele frequencies — pop_allele_freqs","text":"first column STR allele following columns frequency allele different markers.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/pop_allele_freqs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Human population allele frequencies — pop_allele_freqs","text":"http://euroformix.com/","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_bloodmeal_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","title":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","text":"Removes duplicates peaks threshold, subsets ids","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_bloodmeal_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","text":"","code":"prep_bloodmeal_profiles( bloodmeal_profiles, bloodmeal_ids = NULL, peak_thresh = NULL, rm_markers = c(\"AMEL\"), check_heights = TRUE, check_inputs = TRUE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_bloodmeal_profiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. check_heights boolean indicating whether check peak heights threshold. Default: TRUE check_inputs boolean indicating whether check inputs function. Default: TRUE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_bloodmeal_profiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","text":"Dataframe preprocessed bloodmeal profiles","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_bloodmeal_profiles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","text":"","code":"prep_bloodmeal_profiles(bloodmeal_profiles, peak_thresh = 200) #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold #> # A tibble: 60 × 4 #> SampleName Marker Allele Height #> #> 1 evid1 D10S1248 13 1856 #> 2 evid1 D10S1248 15 1045 #> 3 evid1 D12S391 18 297 #> 4 evid1 D12S391 18.3 1446 #> 5 evid1 D12S391 19 751 #> 6 evid1 D12S391 22 1370 #> 7 evid1 D16S539 10 312 #> 8 evid1 D16S539 11 743 #> 9 evid1 D16S539 12 619 #> 10 evid1 D16S539 9 217 #> # ℹ 50 more rows"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_human_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess human profiles — prep_human_profiles","title":"Preprocess human profiles — prep_human_profiles","text":"Removes duplicates optionally twins, subsets ids","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_human_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess human profiles — prep_human_profiles","text":"","code":"prep_human_profiles( human_profiles, human_ids = NULL, rm_twins = TRUE, rm_markers = c(\"AMEL\"), check_inputs = TRUE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_human_profiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess human profiles — prep_human_profiles","text":"human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL rm_twins boolean indicating whether remove likely twins (identical STR profiles) human database prior identifying matches. Default: TRUE rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. check_inputs boolean indicating whether check inputs function. Default: TRUE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_human_profiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocess human profiles — prep_human_profiles","text":"Dataframe preprocessed human profiles","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_human_profiles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocess human profiles — prep_human_profiles","text":"","code":"prep_human_profiles(human_profiles) #> # A tibble: 91 × 3 #> SampleName Marker Allele #> #> 1 00-JP0001-14_20142342311_NO-3241 D10S1248 12 #> 2 00-JP0001-14_20142342311_NO-3241 D10S1248 13 #> 3 00-JP0001-14_20142342311_NO-3241 D12S391 17 #> 4 00-JP0001-14_20142342311_NO-3241 D12S391 18 #> 5 00-JP0001-14_20142342311_NO-3241 D16S539 10 #> 6 00-JP0001-14_20142342311_NO-3241 D16S539 11 #> 7 00-JP0001-14_20142342311_NO-3241 D18S51 13 #> 8 00-JP0001-14_20142342311_NO-3241 D18S51 17 #> 9 00-JP0001-14_20142342311_NO-3241 D19S433 13 #> 10 00-JP0001-14_20142342311_NO-3241 D19S433 14 #> # ℹ 81 more rows"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_dups.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove duplicate rows with warning — rm_dups","title":"Remove duplicate rows with warning — rm_dups","text":"Remove duplicate rows warning","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_dups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove duplicate rows with warning — rm_dups","text":"","code":"rm_dups(df)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_dups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove duplicate rows with warning — rm_dups","text":"df Dataframe remove duplicate rows","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_dups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove duplicate rows with warning — rm_dups","text":"Un-duplicated dataframe","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_markers.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove markers — rm_markers","title":"Remove markers — rm_markers","text":"Remove markers","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_markers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove markers — rm_markers","text":"","code":"rm_markers(profiles, markers)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_markers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove markers — rm_markers","text":"df Dataframe remove markers","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_markers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove markers — rm_markers","text":"Dataframe without markers","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_twins.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove identical STR profiles — rm_twins","title":"Remove identical STR profiles — rm_twins","text":"Remove identical STR profiles","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_twins.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove identical STR profiles — rm_twins","text":"","code":"rm_twins(human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_twins.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove identical STR profiles — rm_twins","text":"human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_twins.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove identical STR profiles — rm_twins","text":"Data frame twins removed","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/subset_ids.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset to ids present in the dataset — subset_ids","title":"Subset to ids present in the dataset — subset_ids","text":"Subset ids present dataset","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/subset_ids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset to ids present in the dataset — subset_ids","text":"","code":"subset_ids(ids, vec, vec_name)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/subset_ids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset to ids present in the dataset — subset_ids","text":"ids ids check vec vec vector ids vec_name name vector","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/subset_ids.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset to ids present in the dataset — subset_ids","text":"list IDs present","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/news/index.html","id":"bistro-011","dir":"Changelog","previous_headings":"","what":"bistro 0.1.1","title":"bistro 0.1.1","text":"Add DOI","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/news/index.html","id":"bistro-010","dir":"Changelog","previous_headings":"","what":"bistro 0.1.0","title":"bistro 0.1.0","text":"Initial package development. bistro() match_exact() match_similarity() match_static_thresh() Includes introductory vignette.","code":""}] +[{"path":"https://duke-malaria-collaboratory.github.io/bistro/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 bistro authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"the-bistro-algorithm","dir":"Articles","previous_headings":"","what":"The bistro algorithm","title":"Introduction to using bistro","text":"bistro algorithm, R package, function within said R package. employs methods forensics identify matches bloodmeals people bit using short tandem repeat (STR) profiles human blood freshly fed bloodmeals people. can used match multi-source bloodmeals bloodmeals incomplete STR profiles. Note can use matching STR profiles research purposes, much recommend using forensic purposes. details algorithm, please refer bistro manuscript (add link ’s posted). core part algorithm contLikSearch() function euroformix package, used calculate log10 likelihood ratios (log10LRs). numerator log10LR likelihood person bitten vector denominator likelihood someone else bitten vector. information euroformix: - Manuscript: EuroForMix: open source software based continuous model evaluate STR DNA profiles mixture contributors artefacts - GitHub - Website explains GUI bistro uses log10LRs identify bloodmeal-human matches using per-bloodmeal dynamic threshold approach.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"running-bistro-in-parallel","dir":"Articles","previous_headings":"","what":"Running bistro in parallel","title":"Introduction to using bistro","text":"First, want run bistro many samples prefer run parallel, potentially cluster, check template bistro Snakemake pipeline.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"installing-the-bistro-r-package","dir":"Articles","previous_headings":"","what":"Installing the bistro R package","title":"Introduction to using bistro","text":"install bistro, run following commands: run errors, try following: Windows: install Rtools Mac: install Xcode Mac App store","code":"install.packages(\"remotes\") # install remotes if needed remotes::install_github(\"duke-malaria-collaboratory/bistro\")"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"getting-started-with-bistro","dir":"Articles","previous_headings":"","what":"Getting started with bistro","title":"Introduction to using bistro","text":"important function bistro bistro(). document take required optional inputs, well output. summary, must provide: Bloodmeal STR profiles Human STR profiles STR genotyping kit name allele peak height threshold (relative fluorescence units, RFUs) bloodmeal STR profiles function outputs matches bloodmeal-human pair. follow along vignette, first load bistro: Note load bistro, also load euroformix bistro depends euroformix package.","code":"library(bistro) #> Loading required package: euroformix"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"required-inputs","dir":"Articles","previous_headings":"","what":"Required inputs","title":"Introduction to using bistro","text":"run bistro() using example data, let’s learn required inputs. Two required, one optional often recommended, datasets required run bistro(): bloodmeal human STR profiles required, human population allele frequencies optional. two required inputs STR genotyping kit name bloodmeal STR allele peak height threshold.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"bloodmeal-str-profiles-bloodmeal_profiles","dir":"Articles","previous_headings":"Required inputs","what":"Bloodmeal STR profiles (bloodmeal_profiles)","title":"Introduction to using bistro","text":"bloodmeal STR profiles dataset one row per allele bloodmeal marker, include allele peak height. Homozygous markers included (.e. one row). must four columns named SampleName, Marker, Allele, Height. SampleName column hold character values. example included bistro: example dataset, 4 different bloodmeals:","code":"head(bloodmeal_profiles) #> SampleName Marker Allele Height #> 1 evid1 AMEL X 2136 #> 2 evid1 AMEL Y 1015 #> 3 evid1 D10S1248 13 1856 #> 4 evid1 D10S1248 14 155 #> 5 evid1 D10S1248 15 1045 #> 6 evid1 D12S391 18 297 unique(bloodmeal_profiles$SampleName) #> [1] \"evid1\" \"evid2\" \"evid3\" \"evid4\""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"human-str-profiles-human_profiles","dir":"Articles","previous_headings":"Required inputs","what":"Human STR profiles (human_profiles)","title":"Introduction to using bistro","text":"human STR profiles dataset takes similar format bloodmeal STR profiles dataset - one row per allele human marker, require peak heights. , homozygous markers included . dataset must three columns named SampleName, Marker, Allele. SampleName column hold character values. example: example dataset, 3 different human profiles:","code":"head(human_profiles) #> SampleName Marker Allele #> 1 00-JP0001-14_20142342311_NO-3241 AMEL X #> 2 00-JP0001-14_20142342311_NO-3241 AMEL Y #> 3 00-JP0001-14_20142342311_NO-3241 D10S1248 12 #> 4 00-JP0001-14_20142342311_NO-3241 D10S1248 13 #> 5 00-JP0001-14_20142342311_NO-3241 D12S391 17 #> 6 00-JP0001-14_20142342311_NO-3241 D12S391 18 unique(human_profiles$SampleName) #> [1] \"00-JP0001-14_20142342311_NO-3241\" \"P1\" #> [3] \"P2\""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"str-genotyping-kit-kit","dir":"Articles","previous_headings":"Required inputs","what":"STR genotyping kit (kit)","title":"Introduction to using bistro","text":"bistro requires input STR genotyping kit name. euroformix package includes kit parameters 23 common STR genotyping kits. also includes handy function list available kits: kit used genotype samples available defaults, modify kit.txt within euroformix package appropriate kit parameters. required parameters can usually found kit manufacturer’s documentation website.","code":"getKit() #> [1] \"testkit\" \"ESX16\" \"ESX17\" \"ESX17Fast\" #> [5] \"ESI17Fast\" \"Fusion\" \"Fusion 6C\" \"SGMPlus\" #> [9] \"Identifiler\" \"NGM\" \"NGMSElect\" \"NGMDetect\" #> [13] \"GlobalFiler\" \"PowerPlex16\" \"PowerPlex21\" \"24plex\" #> [17] \"ESSPlex\" \"ESSplexPlus\" \"ESSplexSEPlus\" \"ESSplexSEQS\" #> [21] \"ForenSeq\" \"VFP\" \"MiniFiler\""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"bloodmeal-allele-peak-threshold-peak_thresh","dir":"Articles","previous_headings":"Required inputs","what":"Bloodmeal allele peak threshold (peak_thresh)","title":"Introduction to using bistro","text":"bistro() also requires bloodmeal allele peak threshold (RFUs) provided. peaks heights threshold removed matching. prior filtering performed bloodmeal profiles based peak height, number equal greater threshold.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"human-population-allele-frequencies-pop_allele_freqs","dir":"Articles","previous_headings":"Required inputs","what":"Human population allele frequencies (pop_allele_freqs)","title":"Introduction to using bistro","text":"Human population frequencies allele locus may supplied. prefer, population allele frequencies can computed human STR profiles (see details). Note latter option suggested small set human reference profiles used. euroformix website provides publicly available population frequency datasets. Additionally, can get United States population allele frequencies many alleles NIST. like input population allele frequencies, dataset contain one column STR marker one row allele. alleles listed first column dataset column named “Allele”. entries marker-allele combination population allele frequency NA allele exist marker. example loci PowerPlex ESX 17 System (ESX17) kit.","code":"head(pop_allele_freqs) #> Allele D3S1358 TH01 D21S11 D18S51 D10S1248 D1S1656 D2S1338 #> 1 5.0 NA 0.002598441 NA NA NA NA NA #> 2 6.0 NA 0.209274435 NA NA NA NA NA #> 3 6.3 NA NA NA NA NA NA NA #> 4 7.0 NA 0.212472516 NA 0.0008984726 NA NA NA #> 5 8.0 NA 0.083649810 NA NA NA NA NA #> 6 8.2 NA NA NA NA NA NA NA #> D16S539 D22S1045 VWA D8S1179 FGA D2S441 D12S391 D19S433 SE33 #> 1 NA NA NA NA NA NA NA NA NA #> 2 0.0008992806 NA NA NA NA NA NA NA NA #> 3 NA NA NA NA NA NA NA NA NA #> 4 NA NA NA NA NA NA NA NA NA #> 5 0.0086930456 NA NA 0.01130226 NA NA NA NA NA #> 6 NA NA NA NA NA NA NA NA 0.002506945"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"running-bistro","dir":"Articles","previous_headings":"","what":"Running bistro()","title":"Introduction to using bistro","text":"Now enough background run bistro(), let’s test using example data included package: ’ll notice bunch messages print screen: One markers (AMEL) kit pop_allele_freqs. 6 peaks 200 RFU threshold removed, filtering one bloodmeals peaks remaining threshold. Notification log10LRs (log10 likelihood ratios) calculated bloodmeal-human pair (4 bloodmeals 3 humans). can take , progress also printed . Notification matches identified. Let’s take look output: output contains 8 columns: bloodmeal_id: bloodmeal sample name. est_noc: estimated number contributors (NOCs) bloodmeal. equals one, ’s predicted single-source bloodmeal; greater one, ’s predicted multi-source bloodmeal. Note sources mean humans . locus_count: number loci STR-typed bloodmeal. match: whether bloodmeal STR profile matched human database (yes ). human_id: human match bloodmeal (NA match). log10_lr: log10 likelihood ratio bloodmeal-human match (NA match). numerator log10LR likelihood person bitten bloodmeal denominator likelihood someone else bitten bloodmeal. notes: bloodmeal doesn’t match (see details). thresh_low: log10LR threshold match made. important thing notice , even though 4 bloodmeals, 5 rows output dataset. , bloodmeal appear least dataset, multiple rows present bloodmeals match multiple people (one row per match). example, , evid1 matches 2 different people (P1 P2) Please keep mind performing data analysis. notes column provides information match: passed filters: match. > min NOC matches: matches expected resolved. can happen closely related people human reference dataset. shared alleles: people human reference dataset alleles overlapped alleles bloodmeal. log10LRs < 1.5: none log10LRs high enough considered match. euroformix error: euroformix::contLikSearch() function run successfully. often happens incomplete bloodmeal profiles (.e. markers succesfully STR-typed). peaks threshold: bloodmeal STR peaks peak height threshold. timed : log-likelihood took long calculate. want give time, can modify time_limit argument (see ).","code":"bistro_output <- bistro( bloodmeal_profiles = bloodmeal_profiles, human_profiles = human_profiles, pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200 ) #> 1/17 markers in kit but not in pop_allele_freqs: AMEL #> Formatting bloodmeal profiles #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold #> Formatting human profiles #> Markers being used: D10S1248, D12S391, D16S539, D18S51, D19S433, D1S1656, D21S11, D22S1045, D2S1338, D2S441, D3S1358, D8S1179, FGA, SE33, TH01, VWA #> Calculating log10LRs #> # bloodmeal ids: 3 #> # human ids: 3 #> Bloodmeal id 1/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 2/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 3/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Identifying matches bistro_output #> # A tibble: 4 × 8 #> bloodmeal_id locus_count est_noc match human_id log10_lr notes thresh_low #> #> 1 evid1 16 2 yes P1 21.8 passed al… 9.5 #> 2 evid1 16 2 yes P2 10.3 passed al… 9.5 #> 3 evid2 1 2 no NA NA all log10… NA #> 4 evid3 8 1 no NA NA all log10… NA"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"using-computed-population-allele-frequencies","dir":"Articles","previous_headings":"","what":"Using computed population allele frequencies","title":"Introduction to using bistro","text":"want population allele frequencies computed inputted human reference profiles, must set calc_allele_freqs = TRUE: Note , use builtin dataset example , recommend using method human dataset 3 people. example may make sense relatively comprehensive set human reference profiles study site.","code":"bistro( bloodmeal_profiles = bloodmeal_profiles, human_profiles = human_profiles, calc_allele_freqs = TRUE, kit = \"ESX17\", peak_thresh = 200 )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"running-bistro-on-a-subset-of-samples","dir":"Articles","previous_headings":"","what":"Running bistro() on a subset of samples","title":"Introduction to using bistro","text":"bistro() can take long time run, especially many samples closely related reference population. Therefore, running samples, highly recommend testing small subset samples make sure everything working expect. , can use bloodmeal_ids human_ids arguments tell bistro() ids want compare: can also useful like parallelize bistro(), example using workflow manager Snakemake NextFlow. ’d like try , template Snakemake pipeline running bistro. highly recommend planning run many samples.","code":"bistro( bloodmeal_profiles = bloodmeal_profiles, human_profiles = human_profiles, pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200, bloodmeal_ids = c(\"evid1\", \"evid2\"), human_ids = \"P1\" )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"other-arguments","dir":"Articles","previous_headings":"","what":"Other arguments","title":"Introduction to using bistro","text":"arguments can modify running bistro() include: Used bistro: rm_twins: whether remove identical STR profiles likely twins prior identifying matches. Note matches people resolved (default: TRUE) time_limit: longest amount time allowed calculate log-likelihood one bloodmeal-human pair, minutes (default: 3) Used euroformix::contLikSearch() calculating log-likelihoods: rm_markers: markers use calculating LRs. default, AMEL removed standard use calculating LRs sex-specific marker model_degrad: whether model peak degradation (default: TRUE) model_bw_stutt model_fw_stutt: whether model peak backward forward stutter (default: FALSE) difftol: difference tolerance log-likelihood across 2 iterations (default: 1) threads: number threads use computing log-likelihoods (default: 4) seed: seed reproducible results (default: 1) return_lr: whether return bloodmeal-human log10LRs. useful want investigate matches perform static threshold matching (see ) (default: FALSE)","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"other-matching-algorithms","dir":"Articles","previous_headings":"","what":"Other matching algorithms","title":"Introduction to using bistro","text":"also implemented matching algorithms including: match_exact(): Exact matching markers alleles match_similarity(): Similarity matching based highest similarity value humans reference database match_static_thresh(): Matching based static log10LR threshold. algorithms take similar inputs bistro(), case match_static_thresh(), bistro() output. Look upcoming manuscript comparison methods.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"identifying-bloodmeals-derived-from-the-same-host-without-a-reference-human-database","dir":"Articles","previous_headings":"","what":"Identifying bloodmeals derived from the same host without a reference human database","title":"Introduction to using bistro","text":"don’t reference database human samples still like identify bloodmeals derived host, can use create_db_from_bloodmeals() function, input human database bistro() function. see works, first load example data bistro manuscript: create human database samples, can run create_db_from_bloodmeals(), creates human profiles complete single-source bloodmeal profiles: can run bistro() usual:","code":"samples <- readr::read_csv(\"https://raw.githubusercontent.com/duke-malaria-collaboratory/bistro_validation/main/data/provedit/provedit_samples_mass200thresh.csv\") hu_db <- create_db_from_bloodmeals(samples, \"identifiler\", 200) bistro(samples, hu_db, \"identifiler\", 200, bloodmeal_ids = \"A02_RD14-0003-23d3a-0.2IP-Q1.2_001.10sec.fsa\", human_ids = paste0(\"H\", 20:24), calc_allele_freqs = TRUE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/bistro.html","id":"running-bistro-step-by-step","dir":"Articles","previous_headings":"","what":"Running bistro step-by-step","title":"Introduction to using bistro","text":"’re interested learning run sub-functions bistro() individually, head vignette(\"step--step\").","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/step-by-step.html","id":"calculate-human-population-allele-frequencies","dir":"Articles","previous_headings":"","what":"Calculate human population allele frequencies","title":"Run bistro step-by-step","text":"needed, can first calculate population allele frequencies human profile database: use built-population allele frequencies since accurate computing allele frequencies three example profiles human_profiles.","code":"pop_freqs_computed <- calc_allele_freqs(human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/step-by-step.html","id":"prepare-input-str-profiles","dir":"Articles","previous_headings":"","what":"Prepare input STR profiles","title":"Run bistro step-by-step","text":"calculating log10LRs, must ensure bloodmeal human profiles prepared correctly. can using prep_bloodmeal_profiles() prep_human_profiles(), respectively. functions: Removes markers user want use Removes duplicate rows ensure homozygous alleles included prep_bloodmeal_profiles() also optionally filters peakes user-defined threshold prep_human_profiles() also optionally removes identical twins (resolved)","code":"bm_profs <- prep_bloodmeal_profiles(bloodmeal_profiles, peak_thresh = 200) #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold hu_profs <- prep_human_profiles(human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/step-by-step.html","id":"calculate-log10lrs-for-bloodmeal-human-pairs","dir":"Articles","previous_headings":"","what":"Calculate log10LRs for bloodmeal-human pairs","title":"Run bistro step-by-step","text":"Next, can use prepared profiles compute log10LRs bloodmeal-human pair (subset pairs). Note identify matches, log10LRs bloodmeal human profiles reference database. example compute log10LRs one bloodmeal humans database:","code":"log10_lrs <- calc_log10_lrs(bm_profs, hu_profs, bloodmeal_ids = \"evid1\", pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200 ) #> 1/17 markers in kit but not in bloodmeal_profiles: AMEL #> 1/17 markers in kit but not in human_profiles: AMEL #> # bloodmeal ids: 1 #> # human ids: 3 #> Bloodmeal id 1/1 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/articles/step-by-step.html","id":"identify-bloodmeal-human-matches","dir":"Articles","previous_headings":"","what":"Identify bloodmeal-human matches","title":"Run bistro step-by-step","text":"Next, can identify matches bloodmeal:","code":"matches <- identify_matches(log10_lrs)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Zena Lapp. Author, maintainer. Christine Markwalter. Author.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lapp Z, Markwalter C (2023). bistro: Bloodmeal identification STR overlap. R package version 0.1.1, https://github.com/duke-malaria-collaboratory/bistro, https://duke-malaria-collaboratory.github.io/bistro/.","code":"@Manual{, title = {bistro: Bloodmeal identification by STR overlap}, author = {Zena Lapp and Christine Markwalter}, year = {2023}, note = {R package version 0.1.1, https://github.com/duke-malaria-collaboratory/bistro}, url = {https://duke-malaria-collaboratory.github.io/bistro/}, }"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/index.html","id":"bistro-bloodmeal-identification-by-str-overlap-","dir":"","previous_headings":"","what":"Bloodmeal identification by STR overlap","title":"Bloodmeal identification by STR overlap","text":"Functions allow identification matches bloodmeals people bit using short tandem repeat (STR) profiles human blood freshly fed bloodmeals people.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Bloodmeal identification by STR overlap","text":"can install bistro like :","code":"# install.packages(\"remotes\") remotes::install_github(\"duke-malaria-collaboratory/bistro\")"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/index.html","id":"dependencies","dir":"","previous_headings":"","what":"Dependencies:","title":"Bloodmeal identification by STR overlap","text":"Depends: R (>= 4.0.0), euroformix (>= 4.0.7) Imports: codetools (>= 0.2.19), dplyr (>= 1.1.3), R.utils (>= 2.12.2), stringr (>= 1.5.0), tibble (>= 3.2.1), tidyr (>= 1.3.0) Suggests: knitr (>= 1.43), readr (>= 2.1.4), rmarkdown (>= 2.24), testthat (>= 3.0.0)","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Bloodmeal identification by STR overlap","text":"Check vignette information.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bistro.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify human contributors to a bloodmeal using STR profiles — bistro","title":"Identify human contributors to a bloodmeal using STR profiles — bistro","text":"Identifies matches bloodmeal STR profiles database human STR profiles. euroformix::contLikSearch() function used calculate log10 likelihood ratios (log10_lrs) used identify human contributors bloodmeal. details present , see vignette('bistro').","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bistro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify human contributors to a bloodmeal using STR profiles — bistro","text":"","code":"bistro( bloodmeal_profiles, human_profiles, kit, peak_thresh, pop_allele_freqs = NULL, calc_allele_freqs = FALSE, bloodmeal_ids = NULL, human_ids = NULL, rm_twins = TRUE, rm_markers = c(\"AMEL\"), model_degrad = TRUE, model_bw_stutt = FALSE, model_fw_stutt = FALSE, difftol = 1, threads = 4, seed = NULL, time_limit = 3, return_lrs = FALSE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bistro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify human contributors to a bloodmeal using STR profiles — bistro","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles. calc_allele_freqs boolean indicating whether calculate allele frequencies human_profiles. FALSE, pop_allele_freqs input required. Default: FALSE bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL rm_twins boolean indicating whether remove likely twins (identical STR profiles) human database prior identifying matches. Default: TRUE rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. model_degrad boolean indicating whether model peak degradation. Used modelDegrad argument euroformix::contLikSearch(). Default: TRUE model_bw_stutt boolean indicating whether model peak backward stutter. Used modelBWstutt argument euroformix::contLikSearch(). Default: FALSE model_fw_stutt boolean indicating whether model peak forward stutter. Used modelFWstutt argument euroformix::contLikSearch(). Default: FALSE difftol Tolerance difference log likelihoods across 2 iterations. euroformix::contLikSearch() argument. Default: 1 threads Number threads use calculating log10_lrs. euroformix::contLikSearch() argument. Default: 4 seed Seed calculating log10_lrs. euroformix::contLikSearch() argument. Default: NULL (seed) time_limit Time limit minutes run euroformix::contLikSearch() function 1 bloodmeal-human pair. Default: 3 return_lrs boolean indicating whether return log10LRs bloodmeal-human pairs. Default: FALSE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bistro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify human contributors to a bloodmeal using STR profiles — bistro","text":"Tibble matches bloodmeal-human pairs including columns listed . Note multiple matches found bloodmeal, included separate rows. bloodmeal_id: bloodmeal id locus_count: number loci successfully typed bloodmeal est_noc: estimated number contributors bloodmeal match: whether match identified given bloodmeal (yes ) human_id: match, human id (NA otherwise) log10_lr: match, log10 likelihood ratio (NA otherwise) notes: bloodmeal match return_lrs = TRUE, named list length 2 returned: matches - tibble described lrs - log10LRs bloodmeal-human pair including columns described additional column: efm_noc, number contributors used input euroformix, min(est_noc, 3).","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bistro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify human contributors to a bloodmeal using STR profiles — bistro","text":"","code":"bistro(bloodmeal_profiles, human_profiles, pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200 ) #> 1/17 markers in kit but not in pop_allele_freqs: AMEL #> Formatting bloodmeal profiles #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold #> Formatting human profiles #> Markers being used: D10S1248, D12S391, D16S539, D18S51, D19S433, D1S1656, D21S11, D22S1045, D2S1338, D2S441, D3S1358, D8S1179, FGA, SE33, TH01, VWA #> Calculating log10LRs #> # bloodmeal ids: 3 #> # human ids: 3 #> Bloodmeal id 1/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 2/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 3/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Identifying matches #> # A tibble: 4 × 8 #> bloodmeal_id locus_count est_noc match human_id log10_lr notes thresh_low #> #> 1 evid1 16 2 yes P1 21.8 passed al… 9.5 #> 2 evid1 16 2 yes P2 10.3 passed al… 9.5 #> 3 evid2 1 2 no NA NA all log10… NA #> 4 evid3 8 1 no NA NA all log10… NA"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bloodmeal_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Bloodmeal STR profiles — bloodmeal_profiles","title":"Bloodmeal STR profiles — bloodmeal_profiles","text":"Example \"bloodmeal\" STR profiles EuroForMix","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bloodmeal_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bloodmeal STR profiles — bloodmeal_profiles","text":"","code":"bloodmeal_profiles"},{"path":[]},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bloodmeal_profiles.html","id":"bloodmeal-profiles","dir":"Reference","previous_headings":"","what":"bloodmeal_profiles","title":"Bloodmeal STR profiles — bloodmeal_profiles","text":"data frame 69 rows 4 columns: SampleName Human sample identifier Marker STR marker Allele STR allele Height STR peak height","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/bloodmeal_profiles.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bloodmeal STR profiles — bloodmeal_profiles","text":"http://euroformix.com/","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_allele_freqs.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate allele frequencies — calc_allele_freqs","title":"Calculate allele frequencies — calc_allele_freqs","text":"Calculate allele frequencies (generally human) population.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_allele_freqs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate allele frequencies — calc_allele_freqs","text":"","code":"calc_allele_freqs(human_profiles, rm_markers = NULL, check_inputs = TRUE)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_allele_freqs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate allele frequencies — calc_allele_freqs","text":"human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. check_inputs boolean indicating whether check inputs function. Default: TRUE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_allele_freqs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate allele frequencies — calc_allele_freqs","text":"tibble first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_allele_freqs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate allele frequencies — calc_allele_freqs","text":"","code":"calc_allele_freqs(human_profiles) #> # A tibble: 32 × 18 #> Allele AMEL D10S1248 D12S391 D16S539 D18S51 D19S433 D1S1656 D21S11 D22S1045 #> #> 1 X 0.6 NA NA NA NA NA NA NA NA #> 2 Y 0.4 NA NA NA NA NA NA NA NA #> 3 12 NA 0.2 NA 0.167 NA NA 0.167 NA NA #> 4 13 NA 0.6 NA NA 0.167 0.4 NA NA NA #> 5 17 NA NA 0.167 NA 0.333 NA NA NA NA #> 6 18 NA NA 0.333 NA NA NA NA NA NA #> 7 10 NA NA NA 0.333 NA NA NA NA NA #> 8 11 NA NA NA 0.333 0.167 NA 0.167 NA NA #> 9 14 NA NA NA NA 0.167 0.4 0.167 NA NA #> 10 29 NA NA NA NA NA NA NA 0.5 NA #> # ℹ 22 more rows #> # ℹ 8 more variables: D2S1338 , D2S441 , D3S1358 , #> # D8S1179 , FGA , SE33 , TH01 , VWA "},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_log10_lrs.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","title":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","text":"Note function preprocess bloodmeal human profile data. like preprocess way performed internally bistro() function, must run prep_bloodmeal_profiles() prep_human_profiles() first.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_log10_lrs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","text":"","code":"calc_log10_lrs( bloodmeal_profiles, human_profiles, pop_allele_freqs, kit, peak_thresh, bloodmeal_ids = NULL, human_ids = NULL, model_degrad = TRUE, model_bw_stutt = FALSE, model_fw_stutt = FALSE, difftol = 1, threads = 4, seed = NULL, time_limit = 3, check_inputs = TRUE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_log10_lrs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL model_degrad boolean indicating whether model peak degradation. Used modelDegrad argument euroformix::contLikSearch(). Default: TRUE model_bw_stutt boolean indicating whether model peak backward stutter. Used modelBWstutt argument euroformix::contLikSearch(). Default: FALSE model_fw_stutt boolean indicating whether model peak forward stutter. Used modelFWstutt argument euroformix::contLikSearch(). Default: FALSE difftol Tolerance difference log likelihoods across 2 iterations. euroformix::contLikSearch() argument. Default: 1 threads Number threads use calculating log10_lrs. euroformix::contLikSearch() argument. Default: 4 seed Seed calculating log10_lrs. euroformix::contLikSearch() argument. Default: NULL (seed) time_limit Time limit minutes run euroformix::contLikSearch() function 1 bloodmeal-human pair. Default: 3 check_inputs boolean indicating whether check inputs function. Default: TRUE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_log10_lrs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","text":"tibble output bistro(), except match column every bloodmeal-human pair calculated log10_lr included. additional column efm_noc number contributors used euroformix::contLikSearch(). maximum value 3.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_log10_lrs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate log10_lrs for multiple bloodmeal-human pairs — calc_log10_lrs","text":"","code":"bm_profs <- prep_bloodmeal_profiles(bloodmeal_profiles, peak_thresh = 200) #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold hu_profs <- prep_human_profiles(human_profiles) log10_lrs <- calc_log10_lrs(bm_profs, hu_profs, bloodmeal_ids = \"evid1\", pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200 ) #> 1/17 markers in kit but not in bloodmeal_profiles: AMEL #> 1/17 markers in kit but not in human_profiles: AMEL #> # bloodmeal ids: 1 #> # human ids: 3 #> Bloodmeal id 1/1 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_one_log10_lr.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate log10_lr for one bloodmeal-human pair — calc_one_log10_lr","title":"Calculate log10_lr for one bloodmeal-human pair — calc_one_log10_lr","text":"Calculate log10_lr one bloodmeal-human pair","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_one_log10_lr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate log10_lr for one bloodmeal-human pair — calc_one_log10_lr","text":"","code":"calc_one_log10_lr( bloodmeal_profiles, bloodmeal_id, human_profiles, human_id, pop_allele_freqs, kit, peak_thresh, model_degrad = TRUE, model_bw_stutt = FALSE, model_fw_stutt = FALSE, difftol = 1, threads = 4, seed = NULL, time_limit = 3 )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_one_log10_lr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate log10_lr for one bloodmeal-human pair — calc_one_log10_lr","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. bloodmeal_id Bloodmeal id SampleName column bloodmeal_profiles compute log10_lr human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. human_id Human id SampleName column human_profiles compute log10_lr pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). model_degrad boolean indicating whether model peak degradation. Used modelDegrad argument euroformix::contLikSearch(). Default: TRUE model_bw_stutt boolean indicating whether model peak backward stutter. Used modelBWstutt argument euroformix::contLikSearch(). Default: FALSE model_fw_stutt boolean indicating whether model peak forward stutter. Used modelFWstutt argument euroformix::contLikSearch(). Default: FALSE difftol Tolerance difference log likelihoods across 2 iterations. euroformix::contLikSearch() argument. Default: 1 threads Number threads use calculating log10_lrs. euroformix::contLikSearch() argument. Default: 4 seed Seed calculating log10_lrs. euroformix::contLikSearch() argument. Default: NULL (seed) time_limit Time limit minutes run euroformix::contLikSearch() function 1 bloodmeal-human pair. Default: 3","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/calc_one_log10_lr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate log10_lr for one bloodmeal-human pair — calc_one_log10_lr","text":"tibble log10_lr bloodmeal-human pair including bloodmeal_id, human_id, locus_count (number STR loci used matching), est_noc (estimated number contributors), efm_noc (number contributors used euroformix), log10_lr (log10 likelihood ratio), notes","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_bistro_inputs.html","id":null,"dir":"Reference","previous_headings":"","what":"Check bistro inputs — check_bistro_inputs","title":"Check bistro inputs — check_bistro_inputs","text":"Check bistro inputs","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_bistro_inputs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check bistro inputs — check_bistro_inputs","text":"","code":"check_bistro_inputs( bloodmeal_profiles, human_profiles, kit, peak_thresh, pop_allele_freqs, calc_allele_freqs, bloodmeal_ids, human_ids, rm_twins, rm_markers, model_degrad, model_bw_stutt, model_fw_stutt, difftol, threads, seed, time_limit, return_lrs )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_bistro_inputs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check bistro inputs — check_bistro_inputs","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles. calc_allele_freqs boolean indicating whether calculate allele frequencies human_profiles. FALSE, pop_allele_freqs input required. Default: FALSE bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL rm_twins boolean indicating whether remove likely twins (identical STR profiles) human database prior identifying matches. Default: TRUE rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. model_degrad boolean indicating whether model peak degradation. Used modelDegrad argument euroformix::contLikSearch(). Default: TRUE model_bw_stutt boolean indicating whether model peak backward stutter. Used modelBWstutt argument euroformix::contLikSearch(). Default: FALSE model_fw_stutt boolean indicating whether model peak forward stutter. Used modelFWstutt argument euroformix::contLikSearch(). Default: FALSE difftol Tolerance difference log likelihoods across 2 iterations. euroformix::contLikSearch() argument. Default: 1 threads Number threads use calculating log10_lrs. euroformix::contLikSearch() argument. Default: 4 seed Seed calculating log10_lrs. euroformix::contLikSearch() argument. Default: NULL (seed) time_limit Time limit minutes run euroformix::contLikSearch() function 1 bloodmeal-human pair. Default: 3 return_lrs boolean indicating whether return log10LRs bloodmeal-human pairs. Default: FALSE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_bistro_inputs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check bistro inputs — check_bistro_inputs","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_calc_allele_freqs.html","id":null,"dir":"Reference","previous_headings":"","what":"Check calc_allele_freqs — check_calc_allele_freqs","title":"Check calc_allele_freqs — check_calc_allele_freqs","text":"Check calc_allele_freqs","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_calc_allele_freqs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check calc_allele_freqs — check_calc_allele_freqs","text":"","code":"check_calc_allele_freqs(calc_allele_freqs)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_calc_allele_freqs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check calc_allele_freqs — check_calc_allele_freqs","text":"calc_allele_freqs boolean indicating whether calculate allele frequencies human_profiles. FALSE, pop_allele_freqs input required. Default: FALSE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_calc_allele_freqs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check calc_allele_freqs — check_calc_allele_freqs","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_colnames.html","id":null,"dir":"Reference","previous_headings":"","what":"Check column names — check_colnames","title":"Check column names — check_colnames","text":"Check column names","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_colnames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check column names — check_colnames","text":"","code":"check_colnames(df, expected_colnames)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_colnames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check column names — check_colnames","text":"df Dataframe check expected_colnames Column names required resent","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_colnames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check column names — check_colnames","text":"Nothing error","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_create_db_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input to `create_db_from_bloodmeals()`` — check_create_db_input","title":"Check input to `create_db_from_bloodmeals()`` — check_create_db_input","text":"Check input `create_db_from_bloodmeals()``","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_create_db_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input to `create_db_from_bloodmeals()`` — check_create_db_input","text":"","code":"check_create_db_input(bloodmeal_profiles, kit, peak_thresh, rm_markers)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_create_db_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input to `create_db_from_bloodmeals()`` — check_create_db_input","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_create_db_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input to `create_db_from_bloodmeals()`` — check_create_db_input","text":"number alleles complete profile","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_heights.html","id":null,"dir":"Reference","previous_headings":"","what":"Check peak_thresh — check_heights","title":"Check peak_thresh — check_heights","text":"Check peak_thresh","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_heights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check peak_thresh — check_heights","text":"","code":"check_heights(heights, peak_thresh)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_heights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check peak_thresh — check_heights","text":"heights vector peak heights peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch().","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_heights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check peak_thresh — check_heights","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_ids.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input ids — check_ids","title":"Check input ids — check_ids","text":"Check input ids","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_ids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input ids — check_ids","text":"","code":"check_ids(vec, vec_name)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_ids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input ids — check_ids","text":"vec vector check vec_name vector name","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_ids.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input ids — check_ids","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_if_allele_freqs.html","id":null,"dir":"Reference","previous_headings":"","what":"Check calc_if_allele_freqs — check_if_allele_freqs","title":"Check calc_if_allele_freqs — check_if_allele_freqs","text":"Check calc_if_allele_freqs","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_if_allele_freqs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check calc_if_allele_freqs — check_if_allele_freqs","text":"","code":"check_if_allele_freqs(pop_allele_freqs, calc_allele_freqs, kit_df)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_if_allele_freqs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check calc_if_allele_freqs — check_if_allele_freqs","text":"pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles. calc_allele_freqs boolean indicating whether calculate allele frequencies human_profiles. FALSE, pop_allele_freqs input required. Default: FALSE kit_df Kit dataframe euroformix::getKit()","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_if_allele_freqs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check calc_if_allele_freqs — check_if_allele_freqs","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_bool.html","id":null,"dir":"Reference","previous_headings":"","what":"Check is boolean — check_is_bool","title":"Check is boolean — check_is_bool","text":"Check boolean","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_bool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check is boolean — check_is_bool","text":"","code":"check_is_bool(vec, vec_name)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_bool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check is boolean — check_is_bool","text":"vec vector check vec_name vector name","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_bool.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check is boolean — check_is_bool","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Check is numeric — check_is_numeric","title":"Check is numeric — check_is_numeric","text":"Check numeric","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check is numeric — check_is_numeric","text":"","code":"check_is_numeric(vec, vec_name, pos = FALSE)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check is numeric — check_is_numeric","text":"vec vector check vec_name vector name pos whether number must positive","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_is_numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check is numeric — check_is_numeric","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_kit.html","id":null,"dir":"Reference","previous_headings":"","what":"Check kit — check_kit","title":"Check kit — check_kit","text":"Check kit","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_kit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check kit — check_kit","text":"","code":"check_kit(kit)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_kit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check kit — check_kit","text":"kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_kit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check kit — check_kit","text":"Error kit exist, dataframe kit otherwise","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_peak_thresh.html","id":null,"dir":"Reference","previous_headings":"","what":"Check peak_thresh — check_peak_thresh","title":"Check peak_thresh — check_peak_thresh","text":"Check peak_thresh","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_peak_thresh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check peak_thresh — check_peak_thresh","text":"","code":"check_peak_thresh(peak_thresh)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_peak_thresh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check peak_thresh — check_peak_thresh","text":"peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch().","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_peak_thresh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check peak_thresh — check_peak_thresh","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_pkg_version.html","id":null,"dir":"Reference","previous_headings":"","what":"Check package version — check_pkg_version","title":"Check package version — check_pkg_version","text":"Check package version","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_pkg_version.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check package version — check_pkg_version","text":"","code":"check_pkg_version(pkg, curr_version, version)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_pkg_version.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check package version — check_pkg_version","text":"pkg package test curr_version current package version version required package version","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_pkg_version.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check package version — check_pkg_version","text":"nothing error package version old","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_setdiff_markers.html","id":null,"dir":"Reference","previous_headings":"","what":"Check what markers are present — check_setdiff_markers","title":"Check what markers are present — check_setdiff_markers","text":"Check markers present","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_setdiff_markers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check what markers are present — check_setdiff_markers","text":"","code":"check_setdiff_markers(markers1, markers2, markers1_name, markers2_name)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_setdiff_markers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check what markers are present — check_setdiff_markers","text":"markers1 first vector check markers2 second vector check markers1_name first vector name markers2_name second vector name","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/check_setdiff_markers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check what markers are present — check_setdiff_markers","text":"Error nothing","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/create_db_from_bloodmeals.html","id":null,"dir":"Reference","previous_headings":"","what":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","title":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","text":"Create human profile database bloodmeal profiles","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/create_db_from_bloodmeals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","text":"","code":"create_db_from_bloodmeals( bloodmeal_profiles, kit, peak_thresh, rm_markers = c(\"AMEL\") )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/create_db_from_bloodmeals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. kit STR kit name euroformix. see list kits embedded euroformix use euroformix::getKit(). kit included, see vignette(\"bistro\") details include kit. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/create_db_from_bloodmeals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","text":"Human database created complete single-source bloodmeals. Complete defined number markers kit minus number markers rm_markers.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/create_db_from_bloodmeals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create human profile database from bloodmeal profiles — create_db_from_bloodmeals","text":"","code":"if (FALSE) { # load example data path_to_data <- paste0( \"https://raw.githubusercontent.com/duke-malaria-collaboratory/\", \"bistro_validation/main/data/provedit/provedit_samples_mass200thresh.csv\" ) samples <- readr::read_csv(path_to_data) create_db_from_bloodmeals(samples, kit = \"identifiler\", peak_thresh = 200) }"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/filter_peaks.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter peak heights that are under threshold — filter_peaks","title":"Filter peak heights that are under threshold — filter_peaks","text":"Filter peak heights threshold","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/filter_peaks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter peak heights that are under threshold — filter_peaks","text":"","code":"filter_peaks(bloodmeal_profiles, peak_thresh)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/filter_peaks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter peak heights that are under threshold — filter_peaks","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch().","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/filter_peaks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter peak heights that are under threshold — filter_peaks","text":"Filtered dataframe","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_allele_freqs.html","id":null,"dir":"Reference","previous_headings":"","what":"Format population allele frequencies — format_allele_freqs","title":"Format population allele frequencies — format_allele_freqs","text":"Format population allele frequencies input euroformix contLikSearch() function.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_allele_freqs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format population allele frequencies — format_allele_freqs","text":"","code":"format_allele_freqs(pop_allele_freqs)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_allele_freqs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format population allele frequencies — format_allele_freqs","text":"pop_allele_freqs Tibble data frame first column STR allele following columns frequency allele different markers. Alleles exist given marker coded NA. NULL calc_allele_freqs = TRUE, population allele frequencies calculated human_profiles.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_allele_freqs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format population allele frequencies — format_allele_freqs","text":"list allele frequencies population format required input euroformix contLikSearch() function","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_bloodmeal_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Format bloodmeal STR profiles — format_bloodmeal_profiles","title":"Format bloodmeal STR profiles — format_bloodmeal_profiles","text":"Format bloodmeal STR profiles input euroformix contLikSearch() function.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_bloodmeal_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format bloodmeal STR profiles — format_bloodmeal_profiles","text":"","code":"format_bloodmeal_profiles(bloodmeal_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_bloodmeal_profiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format bloodmeal STR profiles — format_bloodmeal_profiles","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_bloodmeal_profiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format bloodmeal STR profiles — format_bloodmeal_profiles","text":"list lists including alleles sample format required input euroformix contLikSearch() function.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_human_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Format human STR profiles — format_human_profiles","title":"Format human STR profiles — format_human_profiles","text":"Format human STR profiles input euroformix contLikSearch() function.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_human_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format human STR profiles — format_human_profiles","text":"","code":"format_human_profiles(human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_human_profiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format human STR profiles — format_human_profiles","text":"human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/format_human_profiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format human STR profiles — format_human_profiles","text":"list lists including alleles sample format required input euroformix contLikSearch() function","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_bloodmeal_human_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify matches between bloodmeal and human STR profiles\nbased on similarity values — get_bloodmeal_human_similarity","title":"Identify matches between bloodmeal and human STR profiles\nbased on similarity values — get_bloodmeal_human_similarity","text":"Identify matches bloodmeal human STR profiles based similarity values","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_bloodmeal_human_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify matches between bloodmeal and human STR profiles\nbased on similarity values — get_bloodmeal_human_similarity","text":"","code":"get_bloodmeal_human_similarity(bloodmeal_profiles, human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_bloodmeal_human_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify matches between bloodmeal and human STR profiles\nbased on similarity values — get_bloodmeal_human_similarity","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_bloodmeal_human_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify matches between bloodmeal and human STR profiles\nbased on similarity values — get_bloodmeal_human_similarity","text":"dataframe two columns: bloodmeal_id: bloodmeal ID human_id: human ID match, \"match\" match","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_human_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Get pairwise human STR profile similarities — get_human_similarity","title":"Get pairwise human STR profile similarities — get_human_similarity","text":"Get pairwise human STR profile similarities","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_human_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get pairwise human STR profile similarities — get_human_similarity","text":"","code":"get_human_similarity(human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_human_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get pairwise human STR profile similarities — get_human_similarity","text":"human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/get_human_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get pairwise human STR profile similarities — get_human_similarity","text":"Dataframe three columns: hu1: human ID 1 hu2: human ID 2 similarity: similarity value (# exact locus matches / # loci)","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/human_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Human reference database — human_profiles","title":"Human reference database — human_profiles","text":"Example human STR profiles EuroForMix","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/human_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Human reference database — human_profiles","text":"","code":"human_profiles"},{"path":[]},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/human_profiles.html","id":"human-profiles","dir":"Reference","previous_headings":"","what":"human_profiles","title":"Human reference database — human_profiles","text":"data frame 96 rows 3 columns: SampleName Human sample identifier Marker STR marker Allele STR allele","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/human_profiles.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Human reference database — human_profiles","text":"http://euroformix.com/","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_matches.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify matches for multiple bloodmeal-human pairs — identify_matches","title":"Identify matches for multiple bloodmeal-human pairs — identify_matches","text":"Identify matches multiple bloodmeal-human pairs","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_matches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify matches for multiple bloodmeal-human pairs — identify_matches","text":"","code":"identify_matches(log10_lrs, bloodmeal_ids = NULL, check_inputs = TRUE)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_matches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify matches for multiple bloodmeal-human pairs — identify_matches","text":"log10_lrs Output calc_log10_lrs() bistro return_lrs = TRUE (lrs: second element list) bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL check_inputs boolean indicating whether check inputs function. Default: TRUE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_matches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify matches for multiple bloodmeal-human pairs — identify_matches","text":"tibble output bistro().","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_matches.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify matches for multiple bloodmeal-human pairs — identify_matches","text":"","code":"bm_profs <- prep_bloodmeal_profiles(bloodmeal_profiles, peak_thresh = 200) #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold hu_profs <- prep_human_profiles(human_profiles) log10_lrs <- calc_log10_lrs(bm_profs, hu_profs, bloodmeal_ids = \"evid1\", pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200 ) #> 1/17 markers in kit but not in bloodmeal_profiles: AMEL #> 1/17 markers in kit but not in human_profiles: AMEL #> # bloodmeal ids: 1 #> # human ids: 3 #> Bloodmeal id 1/1 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 matches <- identify_matches(log10_lrs)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_one_match_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify matches between a bloodmeal and human references — identify_one_match_set","title":"Identify matches between a bloodmeal and human references — identify_one_match_set","text":"Identify matches bloodmeal human references","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_one_match_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify matches between a bloodmeal and human references — identify_one_match_set","text":"","code":"identify_one_match_set(log10_lrs, bloodmeal_id)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_one_match_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify matches between a bloodmeal and human references — identify_one_match_set","text":"log10_lrs output calc_log10_lrs() bloodmeal_id Bloodmeal id SampleName column bloodmeal_profiles compute log10_lr","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/identify_one_match_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify matches between a bloodmeal and human references — identify_one_match_set","text":"tibble matches bloodmeal-human pairs including bloodmeal_id, locus_count (number STR loci used matching), est_noc (estimated number contributors), match, human_id (match), log10_lr (log10 likelihood ratio), notes","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_exact.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify exact matches between bloodmeal and human STR profiles — match_exact","title":"Identify exact matches between bloodmeal and human STR profiles — match_exact","text":"Match exact STR profiles bloodmeals humans. Note bloodmeal peak height threshold optional used filtering. Also note rm_twins = FALSE, match twin result multiple rows returned bloodmeal.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_exact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify exact matches between bloodmeal and human STR profiles — match_exact","text":"","code":"match_exact( bloodmeal_profiles, human_profiles, bloodmeal_ids = NULL, human_ids = NULL, peak_thresh = NULL, rm_twins = TRUE, rm_markers = NULL )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_exact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify exact matches between bloodmeal and human STR profiles — match_exact","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). rm_twins boolean indicating whether remove likely twins (identical STR profiles) human database prior identifying matches. Default: TRUE rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_exact.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify exact matches between bloodmeal and human STR profiles — match_exact","text":"Dataframe three columns: bloodmeal_id: bloodmeal ID human_id: human ID match (NA) match: whether match identified (yes )","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_exact.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify exact matches between bloodmeal and human STR profiles — match_exact","text":"","code":"match_exact(bloodmeal_profiles, human_profiles) #> # A tibble: 4 × 3 #> bloodmeal_id human_id match #> #> 1 evid1 NA no #> 2 evid2 NA no #> 3 evid3 NA no #> 4 evid4 NA no"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","title":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","text":"Match STR profiles bloodmeals humans based threshold similar human-human pair. Twins included computing threshold. Note bloodmeal peak height threshold optional used filtering. Also note rm_twins = FALSE, match twin result multiple rows returned bloodmeal.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","text":"","code":"match_similarity( bloodmeal_profiles, human_profiles, bloodmeal_ids = NULL, human_ids = NULL, peak_thresh = NULL, rm_twins = TRUE, rm_markers = NULL, return_similarities = FALSE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). rm_twins boolean indicating whether remove likely twins (identical STR profiles) human database prior identifying matches. Default: TRUE rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. return_similarities boolean indicating whether return human-human bloodmeal-human. Default: FALSE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","text":"Dataframe three columns: bloodmeal_id: bloodmeal ID human_id: human ID match (NA) match: whether match identified (yes ) similarity: similarity value match found return_similarities = TRUE, named list length 4 returned: matches: dataframe described max_hu_hu_similarity: maximum human-human similarity (threshold used matching) hu_hu_similarities: human-human similarity values, bm_hu_similarities: bloodmeal-human similarities profiles identical alleles least one marker","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify similarity matches between STR profiles from bloodmeals and a human\ndatabase — match_similarity","text":"","code":"match_similarity(bloodmeal_profiles, human_profiles) #> Calculating human-human similarities #> Maximum similarity between people: 0.117647058823529 #> Calculating bloodmeal-human similarities #> Identifying matches #> # A tibble: 4 × 4 #> bloodmeal_id human_id match similarity #> #> 1 evid1 P1 yes 0.294 #> 2 evid3 NA no 0.0588 #> 3 evid2 NA no NA #> 4 evid4 NA no NA"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_static_thresh.html","id":null,"dir":"Reference","previous_headings":"","what":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","title":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","text":"Match bloodmeals humans based log10LR static threshold","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_static_thresh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","text":"","code":"match_static_thresh(log10_lrs, thresh)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_static_thresh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","text":"log10_lrs Output calc_log10_lrs() bistro return_lrs = TRUE (lrs: second element list) thresh log10LR threshold matching. bloodmeal-human pairs log10LR ≥ thresh considered match.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_static_thresh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","text":"Dataframe including columns similar bistro() output.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/match_static_thresh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match bloodmeals and humans based on log10LR static threshold — match_static_thresh","text":"","code":"bistro_output <- bistro(bloodmeal_profiles, human_profiles, pop_allele_freqs = pop_allele_freqs, kit = \"ESX17\", peak_thresh = 200, return_lrs = TRUE ) #> 1/17 markers in kit but not in pop_allele_freqs: AMEL #> Formatting bloodmeal profiles #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold #> Formatting human profiles #> Markers being used: D10S1248, D12S391, D16S539, D18S51, D19S433, D1S1656, D21S11, D22S1045, D2S1338, D2S441, D3S1358, D8S1179, FGA, SE33, TH01, VWA #> Calculating log10LRs #> # bloodmeal ids: 3 #> # human ids: 3 #> Bloodmeal id 1/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 2/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Bloodmeal id 3/3 #> Human id 1/3 #> Human id 2/3 #> Human id 3/3 #> Identifying matches match_static_thresh(bistro_output$lrs, 10) #> # A tibble: 4 × 6 #> bloodmeal_id locus_count est_noc match human_id log10_lr #> #> 1 evid1 16 2 yes P1 21.8 #> 2 evid1 16 2 yes P2 10.3 #> 3 evid2 1 2 no NA NA #> 4 evid3 8 1 no NA NA"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/pop_allele_freqs.html","id":null,"dir":"Reference","previous_headings":"","what":"Human population allele frequencies — pop_allele_freqs","title":"Human population allele frequencies — pop_allele_freqs","text":"Example population allele frequencies EuroForMix (ESX17_Norway)","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/pop_allele_freqs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Human population allele frequencies — pop_allele_freqs","text":"","code":"pop_allele_freqs"},{"path":[]},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/pop_allele_freqs.html","id":"pop-allele-freqs-a-data-frame-with-rows-and-columns-the","dir":"Reference","previous_headings":"","what":"pop_allele_freqs A data frame with 76 rows and 17 columns. The","title":"Human population allele frequencies — pop_allele_freqs","text":"first column STR allele following columns frequency allele different markers.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/pop_allele_freqs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Human population allele frequencies — pop_allele_freqs","text":"http://euroformix.com/","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_bloodmeal_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","title":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","text":"Removes duplicates peaks threshold, subsets ids","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_bloodmeal_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","text":"","code":"prep_bloodmeal_profiles( bloodmeal_profiles, bloodmeal_ids = NULL, peak_thresh = NULL, rm_markers = c(\"AMEL\"), check_heights = TRUE, check_inputs = TRUE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_bloodmeal_profiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","text":"bloodmeal_profiles Tibble data frame alleles bloodmeals reference database including 4 columns: SampleName, Marker, Allele, Height. Height must numeric coercible numeric. bloodmeal_ids Vector bloodmeal ids SampleName column bloodmeal_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL peak_thresh Allele peak height threshold RFUs. peaks threshold filtered . prior filtering performed, number equal greater number. Also used threshT argument euroformix::contLikSearch(). rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. check_heights boolean indicating whether check peak heights threshold. Default: TRUE check_inputs boolean indicating whether check inputs function. Default: TRUE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_bloodmeal_profiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","text":"Dataframe preprocessed bloodmeal profiles","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_bloodmeal_profiles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocess bloodmeal profiles — prep_bloodmeal_profiles","text":"","code":"prep_bloodmeal_profiles(bloodmeal_profiles, peak_thresh = 200) #> Removing 6 peaks under the threshold of 200 RFU. #> For 1/4 bloodmeal ids, all peaks are below the threshold #> # A tibble: 60 × 4 #> SampleName Marker Allele Height #> #> 1 evid1 D10S1248 13 1856 #> 2 evid1 D10S1248 15 1045 #> 3 evid1 D12S391 18 297 #> 4 evid1 D12S391 18.3 1446 #> 5 evid1 D12S391 19 751 #> 6 evid1 D12S391 22 1370 #> 7 evid1 D16S539 10 312 #> 8 evid1 D16S539 11 743 #> 9 evid1 D16S539 12 619 #> 10 evid1 D16S539 9 217 #> # ℹ 50 more rows"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_human_profiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess human profiles — prep_human_profiles","title":"Preprocess human profiles — prep_human_profiles","text":"Removes duplicates optionally twins, subsets ids","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_human_profiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess human profiles — prep_human_profiles","text":"","code":"prep_human_profiles( human_profiles, human_ids = NULL, rm_twins = TRUE, rm_markers = c(\"AMEL\"), check_inputs = TRUE )"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_human_profiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess human profiles — prep_human_profiles","text":"human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele. human_ids Vector human ids SampleName column human_profiles compute log10_lrs. NULL, ids input dataframe used. Default: NULL rm_twins boolean indicating whether remove likely twins (identical STR profiles) human database prior identifying matches. Default: TRUE rm_markers vector indicating markers removed prior calculating log10LRs. NULL include markers. default, bistro function AMEL removed standard include LR calculations. check_inputs boolean indicating whether check inputs function. Default: TRUE","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_human_profiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocess human profiles — prep_human_profiles","text":"Dataframe preprocessed human profiles","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/prep_human_profiles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocess human profiles — prep_human_profiles","text":"","code":"prep_human_profiles(human_profiles) #> # A tibble: 91 × 3 #> SampleName Marker Allele #> #> 1 00-JP0001-14_20142342311_NO-3241 D10S1248 12 #> 2 00-JP0001-14_20142342311_NO-3241 D10S1248 13 #> 3 00-JP0001-14_20142342311_NO-3241 D12S391 17 #> 4 00-JP0001-14_20142342311_NO-3241 D12S391 18 #> 5 00-JP0001-14_20142342311_NO-3241 D16S539 10 #> 6 00-JP0001-14_20142342311_NO-3241 D16S539 11 #> 7 00-JP0001-14_20142342311_NO-3241 D18S51 13 #> 8 00-JP0001-14_20142342311_NO-3241 D18S51 17 #> 9 00-JP0001-14_20142342311_NO-3241 D19S433 13 #> 10 00-JP0001-14_20142342311_NO-3241 D19S433 14 #> # ℹ 81 more rows"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_dups.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove duplicate rows with warning — rm_dups","title":"Remove duplicate rows with warning — rm_dups","text":"Remove duplicate rows warning","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_dups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove duplicate rows with warning — rm_dups","text":"","code":"rm_dups(df)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_dups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove duplicate rows with warning — rm_dups","text":"df Dataframe remove duplicate rows","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_dups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove duplicate rows with warning — rm_dups","text":"Un-duplicated dataframe","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_markers.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove markers — rm_markers","title":"Remove markers — rm_markers","text":"Remove markers","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_markers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove markers — rm_markers","text":"","code":"rm_markers(profiles, markers)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_markers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove markers — rm_markers","text":"df Dataframe remove markers","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_markers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove markers — rm_markers","text":"Dataframe without markers","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_twins.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove identical STR profiles — rm_twins","title":"Remove identical STR profiles — rm_twins","text":"Remove identical STR profiles","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_twins.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove identical STR profiles — rm_twins","text":"","code":"rm_twins(human_profiles)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_twins.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove identical STR profiles — rm_twins","text":"human_profiles Tibble data frame alleles humans reference database including three columns: SampleName, Marker, Allele.","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/rm_twins.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove identical STR profiles — rm_twins","text":"Data frame twins removed","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/subset_ids.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset to ids present in the dataset — subset_ids","title":"Subset to ids present in the dataset — subset_ids","text":"Subset ids present dataset","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/subset_ids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset to ids present in the dataset — subset_ids","text":"","code":"subset_ids(ids, vec, vec_name)"},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/subset_ids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset to ids present in the dataset — subset_ids","text":"ids ids check vec vec vector ids vec_name name vector","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/reference/subset_ids.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset to ids present in the dataset — subset_ids","text":"list IDs present","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/news/index.html","id":"bistro-011","dir":"Changelog","previous_headings":"","what":"bistro 0.1.1","title":"bistro 0.1.1","text":"Add DOI","code":""},{"path":"https://duke-malaria-collaboratory.github.io/bistro/news/index.html","id":"bistro-010","dir":"Changelog","previous_headings":"","what":"bistro 0.1.0","title":"bistro 0.1.0","text":"Initial package development. bistro() match_exact() match_similarity() match_static_thresh() Includes introductory vignette.","code":""}]