Last updated: 2021-01-21
Checks: 7 0
Knit directory: TARI_2020GS/
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Unstaged changes:
Modified: analysis/01-cleanTPdata.Rmd
Modified: analysis/02-GetBLUPs.Rmd
Modified: analysis/03-CrossValidation.Rmd
Modified: analysis/04-GetGBLUPs.Rmd
Modified: analysis/05-Results.Rmd
Modified: analysis/index.Rmd
Modified: output/TARI_trials_NOT_identifiable.csv
Modified: output/maxNOHAV_byStudy.csv
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/convertDCas20_5629_ToVCF.Rmd) and HTML (docs/convertDCas20_5629_ToVCF.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | abaf52a | wolfemd | 2020-12-23 | Build site. |
| Rmd | fae176a | wolfemd | 2020-12-23 | Publish the first set of analyses and files for TARI 2020 GS. |
cd /home/jj332_cas/marnin/TARI_2020GS#' @dartvcfInput input name and path of "vcf" file from DArT
#' @dartcountsInput input name and path of counts file from DArT
#' @outName output path and name
#' @nskipvcf number of "VCF" rows to skip on read-in
#' @nskipcounts number of "counts file" rows to skip on read in
#' @ncores number of cores to use, could be VERY memory intensive
#' @dartVars chr vector, column names that _are not_ sample IDs in the read-counts file. I use this internally to assign the sampleIDs in the VCF file
library(tidyverse); library(magrittr)
dartvcfInput<-here::here("data/Report-DCas20-5629","Report_5629_VCF_Ref_Version6.txt")
dartcountsInput<-here::here("data/Report-DCas20-5629","Report_5629_Counts_Ref_Version6.csv")
outName<-here::here("data/Report-DCas20-5629","DCas20_5629")
nskipvcf<-2
nskipcounts<-3
ncores<-10Start manual. Check that the files read in according to previous code. Adjust code if necessary. Make a function and apply it to the input files.
vcf<-read.table(dartvcfInput,
stringsAsFactors = F,skip = nskipvcf, header = T, sep = "\t", comment.char = "")
readCounts<-read.csv(dartcountsInput, stringsAsFactors = F,header = T,skip=nskipcounts)
dim(vcf)
# [1] 13603 3157
dim(readCounts)
# [1] 27206 3191
#
#
# # Initial look at names....
colnames(readCounts)[1:100]
# [1] "AlleleID" "CloneID"
# [3] "ClusterTempIndex" "AlleleSequence"
# [5] "TrimmedSequence" "TrimmedSequence_plus_Strand"
# [7] "Short" "Lowcomplexity"
# [9] "Chrom_Cassava_v61" "ChromPos_Cassava_v61"
# [11] "SNP_ChromPos_Cassava_v61" "AlnCnt_Cassava_v61"
# [13] "AlnEvalue_Cassava_v61" "Strand_Cassava_v61"
# [15] "SeqDiff_Cassava_v61" "ClusterConsensusSequence"
# [17] "ClusterSize" "AlleleSeqDist"
# [19] "SNP" "SnpPosition"
# [21] "CallRate" "OneRatioRef"
# [23] "OneRatioSnp" "FreqHomRef"
# [25] "FreqHomSnp" "FreqHets"
# [27] "PICRef" "PICSnp"
# [29] "AvgPIC" "AvgCountRef"
# [31] "AvgCountSnp" "RatioAvgCountRefAvgCountSnp"
# [33] "FreqHetsMinusFreqMinHom" "AlleleCountsCorrelation"
# [35] "aggregateTagsTotal" "DerivedCorrMinusSeedCorr"
# [37] "RepRef" "RepSNP"
# [39] "RepAvg" "PicRepRef"
# [41] "PicRepSNP" "TotalPicRepRefTest"
# [43] "TotalPicRepSnpTest" "C1GS_A01...TZMRK180001"
# [45] "C1GS_B01...TZMRK180001" "C1GS_C01...TZMRK180001"
# [47] "C1GS_D01...TZMRK180001" "C1GS_E01...TZMRK180001"
# [49] "C1GS_F01...TZMRK180001" "C1GS_G01...TZMRK180001"
# [51] "C1GS_H01...TZMRK180001" "C1GS_A02...TZMRK180001"
# [53] "C1GS_B02...TZMRK180001" "C1GS_C02...TZMRK180001"
# [55] "C1GS_D02...TZMRK180001" "C1GS_E02...TZMRK180001"
# [57] "C1GS_F02...TZMRK180001" "C1GS_G02...TZMRK180001"
colnames(vcf)[1:30]
# [1] "X.CHROM" "POS" "ID"
# [4] "REF" "ALT" "QUAL"
# [7] "FILTER" "INFO" "FORMAT"
# [10] "C1GS_A01...TZMRK180001" "C1GS_A10...TZMRK180001" "C1GS_A11...TZMRK180001"
# [13] "C1GS_A02...TZMRK180001" "C1GS_A03...TZMRK180001" "C1GS_A04...TZMRK180001"
# [16] "C1GS_A05...TZMRK180001" "C1GS_A06...TZMRK180001" "C1GS_A07...TZMRK180001"
# [19] "C1GS_A08...TZMRK180001" "C1GS_A09...TZMRK180001" "C1GS_B01...TZMRK180001"
# [22] "C1GS_B10...TZMRK180001" "C1GS_B11...TZMRK180001" "C1GS_B12...TZMRK180001"
# [25] "C1GS_B02...TZMRK180001" "C1GS_B03...TZMRK180001" "C1GS_B04...TZMRK180001"
# [28] "C1GS_B05...TZMRK180001" "C1GS_B06...TZMRK180001" "C1GS_B07...TZMRK180001"
# rm(vcf,readCounts); gc()Available and sourced from code/ subdirectory: convertDart2vcf.R.
source(here::here("code/","convertDart2vcf.R"))
convertDart2vcf(dartvcfInput,dartcountsInput,outName,
nskipvcf=2,nskipcounts=3,ncores)Split the genome-wide VCF into per-chromosome VCFs for imputation.
require(furrr); options(mc.cores=18); plan(multiprocess)
source(here::here("code","imputationFunctions.R"))
vcfIn<-here::here("data/Report-DCas20-5629","DCas20_5629.vcf.gz")
filters<-"--minDP 4 --maxDP 50" # because using GT not PL for impute (Beagle5)
outPath<-here::here("data/Report-DCas20-5629")
outSuffix<-"DCas20_5629"
future_map(1:18,
~splitVCFbyChr(Chr=.,
vcfIn=vcfIn,filters=filters,
outPath=outPath,outSuffix=outSuffix))
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 rstudioapi_0.13 whisker_0.4 knitr_1.30
[5] magrittr_2.0.1 R6_2.5.0 rlang_0.4.10 stringr_1.4.0
[9] tools_4.0.2 xfun_0.20 git2r_0.28.0 htmltools_0.5.1
[13] ellipsis_0.3.1 rprojroot_2.0.2 yaml_2.2.1 digest_0.6.27
[17] tibble_3.0.5 lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1
[21] vctrs_0.3.6 promises_1.1.1 fs_1.5.0 glue_1.4.2
[25] evaluate_0.14 rmarkdown_2.6 stringi_1.5.3 compiler_4.0.2
[29] pillar_1.4.7 httpuv_1.5.5 pkgconfig_2.0.3