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Copy pathGBS-Chip-Gmatrix.R
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GBS-Chip-Gmatrix.R
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#!/bin/echo Source me don't execute me
KGDver <- "1.2.1"
cat("KGD version:",KGDver,"\n")
if (!exists("nogenos")) nogenos <- FALSE
if (!exists("gform")) gform <- "uneak"
if (!exists("genofile")) genofile <- "HapMap.hmc.txt"
if (!exists("sampdepth.thresh")) sampdepth.thresh <- 0.01
if (!exists("snpdepth.thresh")) snpdepth.thresh <- 0.01
if (!exists("maf.thresh")) maf.thresh <- 1e-9
if (!exists("hirel.thresh")) hirel.thresh <- 0.9
if (!exists("iemm.thresh")) iemm.thresh <- 0.05
if (!exists("triallelic.thresh")) triallelic.thresh <- 0.005 # if third allele present in higher than this proportion, then discard SNP, otherwise discard 3rd & 4th allele calls (currently only for ANGSD data)
if (!exists("cex.pointsize")) cex.pointsize <- 1
if (!exists("functions.only")) functions.only <- FALSE
if (!exists("alleles.keep")) alleles.keep <- FALSE
if (!exists("outlevel")) outlevel <- 9
if (!exists("use.Rcpp")) use.Rcpp <- TRUE
if (!exists("nThreads")) nThreads <- 4 # 0 means use all available
if (!exists("negC")) negC <- "" # empty string will bypass negC checks
if (!exists("negCsettings")) negCsettings <- list()
if (!exists("QQprobpts")) QQprobpts <- c(0.5,0.8,0.9,0.95,0.99)
# function to locate Rcpp file (assume it is in the same directory as this file and this file was 'sourced')
pathToCppFile = function() {
cpp.name <- "GBS-Rcpp-functions.cpp"
this.file <- NULL
for (i in -(1:sys.nframe())) {
if (identical(sys.function(i), base::source)) {
f <- sys.frame(i)
if (!f$chdir)
this.file <- normalizePath(f$ofile)
break
}
}
if (!is.null(this.file)) {
source.dir <- dirname(this.file)
return(file.path(source.dir, cpp.name))
}
else {
# assume it is in the current working directory
return(cpp.name)
}
}
# load C++ functions
# compiling can take ~10 seconds, so we cache the file (under ~/R/RcppCache)
# it will only be recompiled when the C++ file changes or is moved
have_rcpp <- FALSE
if (require(Rcpp) & use.Rcpp) {
# RcppArmadillo is also required, but we don't need to load it here
if (is.element("RcppArmadillo", installed.packages()[,"Package"])) {
have_rcpp <- TRUE
cpp.path <- pathToCppFile()
cat("Loading C++ functions:", cpp.path, "\n")
sourceCpp(file = cpp.path, showOutput = TRUE,
cacheDir = file.path(path.expand("~"), "R", "RcppCache"))
}
}
### depth2K functions
r_depth2K <- function(depthvals) 1/2^depthvals # convert depth to K value assuming binomial
# select R or Rcpp version of depth2K depending on whether Rcpp is installed
if (have_rcpp) {
depth2K <- function(depthvals) {
# Rcpp version only works with matrix as input, so fallback to R version otherwise
if (is.matrix(depthvals)) {
result <- rcpp_depth2K(depthvals, nThreads)
} else {
result <- r_depth2K(depthvals)
}
return(result)
}
} else {
depth2K <- r_depth2K
}
# convert depth to K value assuming beta-binomial with parameters alpha=beta=alph. Inf gives binomial
r_depth2Kbb <- function(depthvals, alph=Inf) {
if (alph==Inf) 1/2^depthvals else beta(alph,depthvals+alph)/beta(alph,alph)
}
# select R or Rcpp version depending on whether Rcpp is installed
if (have_rcpp) {
depth2Kbb <- function(depthvals, alph=Inf) {
# Rcpp version only works with matrix as input, so fallback to R version otherwise
if (is.matrix(depthvals) & alph < Inf) {
if(alph < Inf) {
result <- rcpp_depth2Kbb(depthvals, nThreads, alph)
} else {
result <- depth2K(depthvals)
}
} else {
result <- r_depth2Kbb(depthvals, alph)
}
return(result)
}
} else {
depth2Kbb <- r_depth2Kbb
}
# convert depth to K value modp model. prob of seeing same allele as last time is modp (usually >= 0.5)
r_depth2Kmodp <- function(depthvals, modp=0.5 ) {
Kvals <- 0.5*modp^(depthvals-1)
Kvals[depthvals==0] <- 1
Kvals
}
# select R or Rcpp version depending on whether Rcpp is installed
if (have_rcpp) {
depth2Kmodp <- function(depthvals, modp=0.5) {
# Rcpp version only works with matrix as input, so fallback to R version otherwise
if (is.matrix(depthvals)) {
result <- rcpp_depth2Kmodp(depthvals, modp, nThreads)
} else {
result <- r_depth2Kmodp(depthvals, modp)
}
return(result)
}
} else {
depth2Kmodp <- r_depth2Kmodp
}
depth2Kchoose <- function(dmodel="bb",param) { # function to choose redefine depth2K
if (!dmodel=="modp") dmodel <- "bb"
if (missing(param) & dmodel=="bb") param <- Inf
if (missing(param) & dmodel=="modp") param <- 0.5
if (dmodel=="bb") depth2K <- function(depthvals) depth2Kbb(depthvals,alph=param)
if (dmodel=="modp") depth2K <- function(depthvals) depth2Kmodp(depthvals,modp=param)
depth2K
}
readGBS <- function(genofilefn = genofile, usedt="recommended") {
gform <- tolower(gform)
if (gform == "chip") readChip(genofilefn)
if (gform == "angsdcounts") readANGSD(genofilefn)
if (gform == "tagdigger") readTD(genofilefn)
if (gform == "vcf") read.vcf(genofilefn)
if (gform %in% c("uneak","tassel")) readTassel(genofilefn, usedt=usedt)
ndups <- sum(duplicated(seqID))
if(ndups>0) cat("Warning: ",ndups,"duplicated seqIDs, 1st one is:",seqID[duplicated(seqID)][1],"\n")
}
readTD <- function(genofilefn0 = genofile, skipcols=0) {
havedt <- require("data.table")
ghead <- scan(genofilefn0, what = "", nlines = 1, sep = ",", quote="", quiet=TRUE)
if(skipcols > 0) ghead <- ghead[-(1:skipcols)]
nsnps <<- (length(ghead) - 1)/2
SNPinfo <- read.table(text=ghead[-1],sep="_",fill=TRUE,stringsAsFactors=FALSE)
SNP_Names <<- SNPinfo[2*seq(nsnps),1]
refalleles <<- SNPinfo[2*seq(nsnps)-1,2]
altalleles <<- SNPinfo[2*seq(nsnps),2]
cat("Data file has", nsnps, "SNPs \n")
print(table(refalleles,altalleles))
isgzfile <- grepl(".gz",genofilefn0) #gz unzipping will only work on linux systems
if(nogenos) {
if(.Platform$OS.type == "unix") { # faster to cut in unix than to scan whole file for first field
if(isgzfile) seqID <<- read.table(text=system(paste("zcat",genofilefn0, "| cut -f 1 -d,"), intern = TRUE, ignore.stderr = TRUE),header=FALSE, stringsAsFactors=FALSE)[,1]
if(!isgzfile) seqID <<- read.table(text=system(paste("cut",genofilefn0, "-f 1 -d,"), intern = TRUE, ignore.stderr = TRUE),header=FALSE, stringsAsFactors=FALSE)[,1]
} else {
seqID <<- scan(genofilefn0, what="",skip=1,quote="",flush=TRUE, quiet=TRUE) # 1st column only
}
nind <<- length(seqID)
}
if(!nogenos) {
if (havedt) {
if ( (packageVersion("data.table") < "1.12" | suppressMessages(!require("R.utils"))) & isgzfile) {
genosin <- fread(paste("gunzip -c",genofilefn0),sep=",",header=TRUE,showProgress=FALSE)
} else {
genosin <- fread(genofilefn0,sep=",",header=TRUE)
}
if(skipcols > 0) genosin <- genosin[,-(1:skipcols)]
seqID <<- as.character(as.matrix(genosin[,1])[,1]) # (Allows any name for first col), also convert to vector from data.table
nind <<- length(seqID)
alleles <<- as.matrix(genosin[,-1,with=FALSE])
} else {
genosin <- scan(genofilefn0, skip = 1, sep = ",", what = c(list(seqID = ""), rep(list(0), 2*nsnps)), quote="", quiet=TRUE)
seqID <<- as.character(genosin[[1]])
nind <<- length(seqID)
alleles <<- matrix(0, nrow = nind, ncol = 2 * nsnps)
for (isnp in seq(2*nsnps)) alleles[, isnp] <<- genosin[[isnp+1]]
}
}
cat("Data file has", nind, "samples \n")
invisible(NULL)
}
readANGSD <- function(genofilefn0 = genofile) {
ghead <- scan(genofilefn0, what = "", nlines = 1, sep = "\t", quote="")
nind <<- (length(ghead) - 1) / 4
if (nind != floor(nind)) nind <<- length(ghead) / 4
if (nind != floor(nind)) print("Incorrect number of columns")
seqID <<- substr(ghead,1,nchar(ghead)-2)[seq(4,4*nind,4)]
if(nogenos) cat("Warning: No SNP info yet with gform ANGSDcounts and nogenos\n")
if(!nogenos) {
genosin <- scan(genofilefn0, skip = 1, sep = "\t", flush=TRUE, what = rep(list(integer(0)), 4*nind), quote="")
nsnps <<- length(genosin[[1]])
SNP_Names <<- paste0("SNP",formatC(seq(nsnps),width=nchar(nsnps),flag="0")) # with leading zeros
alleles4 <- matrix(aperm(array(unlist(genosin,use.names=FALSE),dim =c(nsnps,4,nind)), c(3,2,1)), nrow=nind)
# cols in sets of 4 alleles, rows are samples (temporary, until relevant alleles extracted)
acounts <- matrix(colSums(alleles4),ncol=4,byrow=TRUE)
acountord <- t(apply(acounts,1,order,decreasing=TRUE))
SNP.discard <- which(acounts[cbind(1:nsnps,acountord[,3])] / rowSums(acounts) > triallelic.thresh)
snpcols <- sort(c(seq(0,4*(nsnps-1),4)+acountord[,1],seq(0,4*(nsnps-1),4)+acountord[,2]))
alleles <<- alleles4[,snpcols]
if(length(SNP.discard) > 0) {
alleles <<- alleles[,-c(2*SNP.discard-1,2*SNP.discard)]
nsnps <<- nsnps - length(SNP.discard)
SNP_Names <<- SNP_Names[-SNP.discard]
cat(length(SNP.discard),"SNP(s) removed with 3rd allele frequency >",triallelic.thresh,"\n")
}
}
invisible(NULL)
}
read.vcf <- function(vcffile=genofile) { # uses AD then GT if either/both available
# Needs data.table and (for .gz files) R.utils
if(!require(data.table)) stop("data.table package required for read.vcf")
if(nogenos) cat("Warning: No SNP or sample info yet with gform VCF and nogenos\n")
if(!nogenos) {
vcfin <- fread(vcffile,skip="#CHROM") # skip headers until #CHROM found on a row
if(colnames(vcfin)[1]=="V1") {
cat("Correcting added first column name, remove trailing (blank?) column (ignore fread warning below)\n")
colnames(vcfin)[1:ncol(vcfin)-1] <- colnames(vcfin)[2:ncol(vcfin)]
vcfin[,ncol(vcfin)] <- NULL
}
SNP_Names <<- vcfin$ID
refalleles <<- vcfin$REF
altalleles <<- vcfin$ALT
nsnps <<- length(SNP_Names)
formatcol <- which(colnames(vcfin)=="FORMAT")
seqID <<- colnames(vcfin)[-(1:formatcol)]
nind <<- length(seqID)
chrom <<- vcfin$`#CHROM`
pos <<- vcfin$POS
if(all(SNP_Names==".")) SNP_Names <<- paste(chrom,pos,sep="_")
nfields <- 1+ lengths(regmatches(vcfin$FORMAT,gregexpr(":",vcfin$FORMAT)))
tempformats <- read.table(text=vcfin$FORMAT,sep=":",fill=TRUE,stringsAsFactors=FALSE,col.names=paste0("V",1:max(nfields)))
gthave = apply(tempformats=="GT",1,any)
gtpos = unlist(apply(tempformats=="GT",1,which))
adhave = apply(tempformats=="AD",1,any)
adpos = unlist(apply(tempformats=="AD",1,which))
genon <<- matrix(NA,nrow=nind,ncol=nsnps)
if(any(gthave)) {
cat("(Initially) reading genotypes from GT field\n")
tempgt <- read.table(text=as.matrix(vcfin[gthave,-(1:formatcol)]),sep=":",fill=TRUE,stringsAsFactors=FALSE)[matrix(c(1:(nind*sum(gthave)),rep(gtpos,nind)),ncol=2,dimnames=list(NULL,c("row","col")))]
tempgt[tempgt=="."] <- "./."
genongt <- t(matrix(rowSums(read.table(text=gsub("/","|",tempgt),sep="|",na.strings=".")),nrow=sum(gthave)))
genon[,which(gthave)] <<- genongt
}
depth <<- matrix(Inf, nrow = nind, ncol = nsnps)
if(any(adhave)) {
cat("Reading allelic depths from AD field\n")
tempad <- read.table(text=as.matrix(vcfin[adhave,-(1:formatcol)]),sep=":",fill=TRUE,stringsAsFactors=FALSE)[matrix(c(1:(nind*sum(adhave)),rep(adpos,nind)),ncol=2,dimnames=list(NULL,c("row","col")))]
tempad2 <- read.table(text=sub(".","0,0",sub(".,.","0,0",tempad,fixed=TRUE),fixed=TRUE),sep=",")
ref <- t(matrix(tempad2[,1],nrow=sum(adhave)))
alt <- t(matrix(tempad2[,2],nrow=sum(adhave)))
genonad <- trunc((2*ref/(ref+alt))-1)+1
genon[,which(adhave)] <<- genonad
alleles <<- matrix(Inf, nrow = nind, ncol = 2 * nsnps)
alleles[,seq(1, 2 * nsnps - 1, 2)[which(adhave)]] <<- ref
alleles[,seq(2, 2 * nsnps, 2)[which(adhave)]] <<- alt
depth[,which(adhave)] <<- ref + alt
p <<- calcp()
} else {
p <<- colMeans(genon, na.rm = TRUE)/2 # same as pg further down
}
depth[is.na(genon)] <<- 0
if(!any(adhave)) gform <- "chip"
}
invisible(NULL)
}
readChip <- function(genofilefn0 = genofile) {
ghead <- scan(genofilefn0,what="",nlines=1,sep=",", quote="", quiet=TRUE)
SNP_Names <<- ghead[-1]
nsnps <<- length(SNP_Names)
cat("Data file has", nsnps, "SNPs \n")
if(nogenos) cat("Warning: No sample info yet with gform chip data and nogenos\n")
if(!nogenos) {
genost <- scan(genofilefn0,what="",skip=1,sep=",", quote="", quiet=TRUE) # read as text ... easier to pull out elements than list of nsnps+1
snpnums <- ((1:length(genost))-1) %% (nsnps+1)
genon <<- matrix(as.numeric(genost[which(snpnums !=0)]) ,ncol=nsnps,byrow=TRUE)
genon[genon >2 | genon <0] <- NA
seqID <<- genost[which(snpnums ==0)]
nind <<- length(seqID)
cat("Data file has", nind, "samples \n")
rm(genost)
depth <<- matrix(Inf, nrow = nind, ncol = nsnps)
depth[is.na(genon)] <<- 0
p <<- colMeans(genon, na.rm = TRUE)/2 # same as pg further down
}
invisible(NULL)
}
readTassel <- function(genofilefn0 = genofile, usedt="recommended") {
havedt <- FALSE # problem with read.table(text= when > 2bill
if(usedt=="always") havedt <- require("data.table")
gsep <- switch(gform, uneak = "|", tassel = ",")
ghead <- scan(genofilefn0, what = "", nlines = 1, sep = "\t", quote="", quiet=TRUE)
nind <<- length(ghead) - switch(gform, uneak = 6, tassel = 2)
cat("Data file has", nind, "samples \n")
seqID <<- switch(gform, uneak = ghead[2:(nind + 1)], tassel = ghead[-(1:2)])
# find SNPs without reading all genos (perhaps do this only if nogenos is TRUE if its taking too long)
if(gform=="uneak") SNP_Names <<- scan(genofilefn0, what="",skip=1,quote="",flush=TRUE, quiet=TRUE) # 1st column only
if(gform=="tassel") {
tempin <- scan(genofilefn0, what=list(CHROM = "", POS = 0),skip=1,quote="",flush=TRUE, quiet=TRUE) # 1st 2 columns only
chrom <<- tempin$CHROM
pos <<- tempin$POS
SNP_Names <<- paste(chrom,pos,sep="_")
}
nsnps <<- length(SNP_Names)
cat("Data file has", nsnps, "SNPs \n")
if(usedt=="recommended" & nsnps * nind > 2^30) havedt <- FALSE # problem with read.table(text= when > 2bill
if(!nogenos) {
if (havedt & gform=="tassel") {
isgzfile <- grepl(".gz",genofilefn0) #gz unzipping will only work on linux systems
if ( packageVersion("data.table") < "1.12" & isgzfile) {
genosin <- fread(paste("gunzip -c",genofilefn0),sep="\t",header=TRUE,showProgress=FALSE)
} else {
genosin <- fread(genofilefn0,sep="\t",header=TRUE)
}
# alleles <<- as.matrix(reshape(data.frame(ids=rep(1:nind,nsnps),snps=rep(1:nsnps,each=nind),
# read.table(text= t(as.matrix(genosin[,-(1:2),with=FALSE])),sep=",")),
# direction="wide",idvar="ids",timevar="snps"))[,-1]
# dcast syntax dcast(data.table(ids ...), ids ~ snps,value.var = c("V1","V2" )) puts all V1 then all V2 so need to rearrange ...
tempalleles <- as.matrix(dcast(data.table(ids=rep(1:nind,nsnps),snps=rep(1:nsnps,each=nind),
read.table(text= t(as.matrix(genosin[,-(1:2),with=FALSE])),sep=",")),
ids ~ snps,value.var = c("V1","V2" ))[,-1])
alleles <<- matrix(0,nrow=nind, ncol=2*nsnps)
alleles[,seq(1, 2 * nsnps - 1, 2)] <<- tempalleles[,1:nsnps]
alleles[,seq(2, 2 * nsnps, 2)] <<- tempalleles[,nsnps+(1:nsnps)]
} else { # use scan to read input
if (gform == "tassel") genosin <- scan(genofilefn0, skip = 1, sep = "\t", what = c(list(chrom = "", coord = 0), rep(list(""), nind)), quote="", quiet=TRUE)
if (gform == "uneak") genosin <- scan(genofilefn0, skip = 1, sep = "\t", what = c(list(chrom = ""), rep(list(""), nind), list(hetc1 = 0, hetc2 = 0, acount1 = 0, acount2 = 0, p = 0)), quote="", quiet=TRUE)
alleles <<- matrix(0, nrow = nind, ncol = 2 * nsnps)
for (iind in 1:nind) alleles[iind, ] <<- matrix(as.numeric(unlist(strsplit(genosin[[iind + switch(gform, uneak = 1, tassel = 2)]], split = gsep,
fixed = TRUE))), nrow = 1)
if (gform == "uneak") AFrq <<- genosin[[length(genosin)]]
}
}
unadepth <- which(is.na(alleles))
if(length(unadepth)>0) alleles[unadepth] <<- 0
invisible(NULL)
}
parkGBS <- function() {
#refalleles altalleles # swapped Dec 23
if(!exists("alleles")) {
alleles <- matrix(0,nrow=nind, ncol=2*nsnps)
tempalleles <- matrix(0,nrow=nind,ncol=nsnps)
tempalleles[which(genon == 0)] <- depth[which(genon==0)]
tempalleles[which(genon == 1)] <- depth[which(genon==1)]/2
alleles[,seq(2, 2 * nsnps, 2)] <- tempalleles # count of alt alleles
tempalleles[which(genon == 2)] <- depth[which(genon==2)]
tempalleles[which(genon == 0)] <- 0
alleles[,seq(1, 2 * nsnps - 1, 2)] <- tempalleles # count of ref alleles
}
parkeddata <- list(nsnps=nsnps,SNP_Names=SNP_Names, seqID = seqID, nind=nind, alleles = alleles)
if(exists("AFrq")) parkeddata <- append(parkeddata,list(AFrq=AFrq))
parkeddata
}
activateGBS <- function(GBSobj) {
if(!is.null(GBSobj)) {
nsnps <<- GBSobj$nsnps; SNP_Names <<- GBSobj$SNP_Names; seqID <<- GBSobj$seqID; nind <<- GBSobj$nind; alleles <<- GBSobj$alleles
if("AFrq" %in% names(GBSobj)) AFrq <<- GBSobj$AFrq
if(exists("depth")) rm(depth, p, genon, inherits=TRUE)
} else cat("GBSobj is invalid\n")
invisible(NULL)
}
joinGBS <- function(join1, join2=NULL, replace=TRUE, uniqueSNPs=FALSE) {
if(is.null(join2)) join2 <- parkGBS()
cat("Joining set 1",join1$nind,"ind x",join1$nsnps,"SNPs, set 2",join2$nind,"ind x",join2$nsnps,"SNPs\n")
if(uniqueSNPs) join1$SNP_Names <- make.names(c(join2$SNP_Names,join1$SNP_Names),unique=TRUE)[join2$nsnps+(1:join1$nsnps)]
SNP_Names <- unique( c(join1$SNP_Names,join2$SNP_Names))
nsnps <- length(SNP_Names)
nind <- join1$nind+join2$nind
seqID <- c(join1$seqID,join2$seqID)
snppos1 <- match(join1$SNP_Names,SNP_Names)
snppos2 <- match(join2$SNP_Names,SNP_Names)
alleles <- matrix(0,nrow=nind,ncol=2*nsnps)
alleles[1:join1$nind,as.vector(rbind(snppos1*2-1,snppos1*2))] <- join1$alleles
alleles[join1$nind+(1:join2$nind),as.vector(rbind(snppos2*2-1,snppos2*2))] <- join2$alleles
if(any(duplicated(seqID))) cat(sum(duplicated(seqID)),"samples with seqID already present (kept separate). Consider using mergeSamples(seqID)\n")
# scoping problems if mergeSamples used inside this function
outobj <- NULL
if(replace) {
nsnps <<- nsnps; SNP_Names <<- SNP_Names; seqID <<- seqID; nind <<- nind; alleles <<- alleles
if(exists("depth")) rm(depth,genon, pos=1)
if(exists("AFrq")) rm(AFrq, pos=1)
} else {
outobj <- list(nsnps=nsnps,SNP_Names=SNP_Names, seqID = seqID, nind=nind, alleles = alleles)
}
invisible(outobj)
}
samp.remove <- function (samppos=NULL, keep=FALSE) {
if(keep) samppos <- setdiff(1:nind,samppos)
if(length(samppos)>0) {
# if(gform != "chip") alleles <<- alleles[-samppos, ]
# if(gform == "chip" & exists("alleles") & alleles.keep) alleles <<- alleles[-samppos,]
if(exists("alleles")) alleles <<- alleles[-samppos,]
if(exists("depth")) depth <<- depth[-samppos, ]
if(exists("genon")) genon <<- genon[-samppos,]
if(exists("sampdepth")) sampdepth <<- sampdepth[-samppos]
if(exists("samples")) samples <<- samples[-samppos,]
seqID <<- seqID[-samppos]
nind <<- nind - length(samppos)
}
}
snp.remove <- function(snppos=NULL, keep=FALSE) {
if(keep) snppos <- setdiff(1:nsnps,snppos)
if (length(snppos) > 0) {
SNP_Names <<- SNP_Names[-snppos]
nsnps <<- length(SNP_Names)
if(exists("p")) p <<- p[-snppos]
if(exists("depth")) depth <<- depth[, -snppos]
if(exists("genon")) genon <<- genon[, -snppos]
if(exists("samples")) samples <<- samples[,-snppos]
if(exists("chrom")) chrom <<- chrom[-snppos]
if(exists("pos")) pos <<- pos[-snppos]
if(exists("refalleles")) refalleles <<- refalleles[-snppos]
if(exists("altalleles")) altalleles <<- altalleles[-snppos]
if(exists("AFrq")) AFrq <<- AFrq[-snppos]
if (exists("alleles")) {
uremovea <- sort(c(2 * snppos, 2 * snppos - 1)) # allele positions
if(exists("RAcounts")) RAcounts <<- RAcounts[-snppos, ]
alleles <<- alleles[, -uremovea]
if(exists("allelecounts")) allelecounts <<- allelecounts[uremovea]
}
}
}
negCreport <- function(negpos,removeneg=FALSE) {
if(length(negpos)>0) {
negCstats <- data.frame(seqID=seqID[negpos], callrate= 1 - rowSums(depth[negpos,,drop=FALSE] == 0)/nsnps, sampdepth=rowMeans(depth[negpos,,drop=FALSE]), stringsAsFactors = FALSE)
write.csv(negCstats, "negCStats.csv", row.names = FALSE)
removetxt <- ""; if(removeneg) removetxt <- "removed"
cat(length(negpos),"Negative controls", removetxt,"\n mean call rate = ",mean(negCstats$callrate),
"\n max call rate = ",max(negCstats$callrate),
"\n mean depth = ",mean(negCstats$sampdepth),
"\n max depth = ",max(negCstats$sampdepth), "\n")
if(removeneg) samp.remove(negpos)
negCstats
} else
invisible(NULL)
}
HWpops <- function(snpsubset, indsubset, populations=NULL, depthmat = depth) {
if (missing(snpsubset)) snpsubset <- 1:nsnps
if (missing(indsubset)) indsubset <- 1:nind
if (is.null(populations)) populations <- rep("A",nind)
popnames <- unique(populations[indsubset])
npops <- length(popnames)
HWdis <- matrix(NA,nrow=npops,ncol=length(snpsubset)); rownames(HWdis) <- popnames #initialise
l10LRT <- x2star <- l10pstar <- maf <- HWdis
for(ipop in 1:npops) {
thigroup <- popnames[ipop]
indgroup <- intersect(indsubset,which(populations==thigroup))
naa <- colSums(genon[indgroup,snpsubset,drop=FALSE] == 2, na.rm = TRUE)
nab <- colSums(genon[indgroup,snpsubset,drop=FALSE] == 1, na.rm = TRUE)
nbb <- colSums(genon[indgroup,snpsubset,drop=FALSE] == 0, na.rm = TRUE)
n1 <- 2 * naa + nab
n2 <- nab + 2 * nbb
n <- n1 + n2 #n alleles
p1 <- n1/n
p2 <- 1 - p1
HWdis[ipop,] <- naa/(naa + nab + nbb) - p1 * p1
x2 <- (naa + nab + nbb) * HWdis[ipop,]^2/(p1^2 * p2^2)
LRT <- 2 * (n * log(n) + naa * log(pmax(1, naa)) + nab * log(pmax(1, nab)) + nbb * log(pmax(1, nbb)) - (n/2) * log(n/2) - n1 * log(n1) - n2 *
log(n2) - nab * log(2)) # n is # alleles = 2* n obs
# l10p <- -log10(exp(1)) * pchisq(x2, 1, lower.tail = FALSE, log.p = TRUE)
l10LRT[ipop,] <- -log10(exp(1)) * pchisq(LRT, 1, lower.tail = FALSE, log.p = TRUE)
Kdepth <- depth2K(depthmat[indgroup,snpsubset,drop=FALSE])
Kdepth[depthmat[indgroup,snpsubset,drop=FALSE]==0] <- NA
esnphetstar <- 2*p1*p2*(1-2*colMeans(Kdepth,na.rm=TRUE))
osnphetstar <- nab/(naa + nab + nbb)
x2star[ipop,] <- colSums(1-2*Kdepth,na.rm=TRUE)*(1-osnphetstar/esnphetstar)^2 # corrected Nov 2018
l10pstar[ipop,] <- -log10(exp(1)) * pchisq(x2star[ipop,], 1, lower.tail = FALSE, log.p = TRUE)
maf[ipop,] <- ifelse(p1 > 0.5, p2, p1)
}
outobj <- list( HWdis=HWdis, l10LRT=l10LRT, x2star=x2star, l10pstar=l10pstar, maf=maf)
if(npops>1) outobj <- c(outobj, list(l10pstar.pop = -log10(exp(1)) * pchisq(colSums(x2star,na.rm=TRUE), colSums(!is.na(x2star)), lower.tail = FALSE, log.p = TRUE) ))
outobj
}
finplot <- function(HWdiseq=HWdis, MAF=maf, plotname="finplot", finpalette=palette.aquatic, finxlim=c(0,0.5), finylim=c(-0.25, 0.25)) {
depthtrans <- function(x) round(20 * log(-log(1/(x + 0.9)) + 1.05)) # to compress colour scale at higher depths
depthpoints <- c(0.5, 5, 50, 250) # legend points
transpoints <- depthtrans(depthpoints)
mindepthplot <- 0.1
maxdepthplot <- 256
maxtrans <- depthtrans(maxdepthplot)
legend_image <- as.raster(matrix(rev(finpalette[1:maxtrans]), ncol = 1))
png(paste0(plotname,".png"), width = 960, height = 960, pointsize = cex.pointsize * 18)
if(whitedist(finpalette) < 25) par(bg="grey")
plot(HWdiseq ~ MAF, col = finpalette[depthtrans(pmax(mindepthplot, pmin(snpdepth, maxdepthplot)))], cex = 0.8,
xlab = "Minor allele frequency", ylab = "Hardy-Weinberg disequilibrium", cex.lab = 1.5, xlim=finxlim, ylim=finylim)
rasterImage(legend_image, 0.05, -0.2, 0.07, -0.1)
text(x = 0.1, y = -0.2 + 0.1 * transpoints/maxtrans, labels = format(depthpoints))
text(x = 0.075, y = -0.075, labels = "SNP Depth", cex = 1.2)
dev.off()
}
HWsigplot <- function(HWdiseq=HWdis, MAF=maf, ll=l10LRT, plotname="HWdisMAFsig", finpalette=palette.aquatic, finxlim=c(0,0.5), finylim=c(-0.25, 0.25),
llname=expression('-log'[10]*' LRT'), sortord=ll) {
sigtrans <- function(x) round(sqrt(x) * 40/max(sqrt(ll),na.rm=TRUE)) + 1 # to compress colour scale at higher LRT
sigpoints <- c(0.5, 2, 5, 20, 50, 200, 500) # legend points
sigpoints <- sigpoints[union(1:2,which(sigpoints < max(ll,na.rm=TRUE)))]
if(length(sigpoints) > 5) sigpoints <- sigpoints[seq(1,length(sigpoints),2)]
transpoints <- sigtrans(sigpoints)
maxtrans <- sigtrans(max(ll,na.rm=TRUE))
legend_image <- as.raster(matrix(rev(finpalette[1:maxtrans]), ncol = 1))
plotord <- 1:length(MAF)
if(!is.null(sortord)) plotord <- order(sortord)
png(paste0(plotname,".png"), width = 640, height = 640, pointsize = cex.pointsize * 15)
if(whitedist(finpalette) < 25) par(bg="grey")
plot(HWdiseq[plotord] ~ MAF[plotord], col = finpalette[sigtrans(ll)][plotord], cex = 0.8, xlab = "Minor Allele Frequency",
ylab = "Hardy-Weinberg disequilibrium", cex.lab = 1.5, xlim=finxlim, ylim=finylim)
rasterImage(legend_image, 0.05, -0.2, 0.07, -0.1)
text(x = 0.1, y = -0.2 + 0.1 * transpoints/maxtrans, labels = format(sigpoints))
text(x = 0.075, y = -0.075, labels = llname, cex = 1.2)
dev.off()
}
finclass <- function(HWdiseq=HWdis, MAF=maf, colobj, classname=NULL, plotname="finclass", finxlim=c(0,0.5), finylim=c(-0.25, 0.25)) {
if(missing(colobj)) colobj <- list(collabels="",collist="black",sampcol=rep("black",length(MAF)))
plotord <- 1:length(MAF) # fixed for now, keep in case want to allow reordering
png(paste0(plotname,".png"), width = 640, height = 640, pointsize = cex.pointsize * 15)
plot(HWdiseq[plotord] ~ MAF[plotord], col = colobj$sampcol[plotord], cex = 0.8, xlab = "Minor Allele Frequency",
ylab = "Hardy-Weinberg disequilibrium", cex.lab = 1.5, xlim=finxlim, ylim=finylim)
legend(0.05, -0.075, legend=colobj$collabels,col=colobj$collist,pch=1,title=classname)
dev.off()
}
x2starplots <- function () {
HWsigplot(ll=l10pstar,llname=expression('-log'[10]*'p X'^2*'*'), finpalette=colorRampPalette(c("deepskyblue2","red"))(50))
yaxpts <- quantile(x2star,QQprobpts, na.rm=TRUE) # NA when max depth is 1
png("X2star-QQ.png", width = 480, height = 480, pointsize = cex.pointsize * 12)
par(mar = c(5.1, 4.1, 4.1, 4.1))
qqplot(qchisq(ppoints(nsnps), df = 1), x2star, cex=0.75, main = parse(text = "Hardy-Weinberg ~~ X^2~~ '* Q-Q Plot'"),
xlab = parse(text = "Theoretical ~~ (chi[1]^2) ~~ Quantiles"), ylab = "Sample Quantiles")
ytck <- axis(side=4, tick=FALSE, labels=FALSE)
axis(side=4, at=ytck, labels = signif(-log10(exp(1)) * pchisq(ytck, 1, lower.tail = FALSE, log.p = TRUE),3))
mtext(text= expression('-log'[10]*'p X'^2*'*'), side=4, line=3)
if(length(QQprobpts) > 0) {
for(ipt in 1:length(QQprobpts)) {
lines(x=c(0,xaxpts[ipt],xaxpts[ipt]),y=c(yaxpts[ipt],yaxpts[ipt],0), col="grey")
}
text(x=xaxpts/4,y=yaxpts,labels=QQprobpts, pos=3, col="grey")
}
dev.off()
}
HDplot <- function(plotname="HDplot", colourtype = "depth", finpalette=palette.aquatic, HDxlim=c(0,1), HDylim=c(-Inf, Inf), HDcol=NULL,
sortcol = "asc") {
# from Mckinney Mol Ecol Res 2017
# colourtype can be "depth", "HW" (use l10LRT), "HW*" (use l10pstar)
# sortcol can be "asc", "desc" or ""
if(!exists("alleles")) cat("Cannot produce HD plots without alleles object\n")
if(exists("alleles")) {
if(!colourtype %in% c("depth", "HW", "HW*")) colourtype <- "depth"
if(!sortcol %in% c("asc","desc")) sortcol <- ""
if(!exists("HD.saved.KGD")) {
HD.saved.KGD <<- TRUE
Het <<- colSums(genon == 1, na.rm=TRUE) / (nind * SNPcallrate)
SNPN <<- colSums(depth * (genon ==1), na.rm=TRUE)
SNPNA <<- colSums(alleles[, seq(1, 2 * nsnps - 1, 2)] * (genon ==1), na.rm=TRUE)
storage.mode(SNPN) <- storage.mode(SNPNA) <- "integer"
}
SNPz <- (SNPN/2 - SNPNA)/sqrt(SNPN * 0.5 * 0.5) # denom is SNPsd
if(colourtype == "depth") {
colourvar <- snpdepth
legendlab <- "SNP Depth"
colvtrans <- function(x) round(20 * log(-log(1/(x + 0.9)) + 1.05)) # to compress colour scale at higher depths
colvpoints <- c(0.5, 5, 50, 250) # legend points
mincolvplot <- 0.1
maxcolvplot <- 256
}
if(colourtype=="HW") {colourvar <- l10LRT; legendlab <- expression('-log'[10]*' LRT')}
if(colourtype=="HW*") {colourvar <- l10pstar; legendlab <- expression('-log'[10]*'p X'^2*'*')}
if(grepl("^HW", colourtype)) {
mincolvplot <- 0
maxcolvplot <- max(colourvar,na.rm=TRUE)
colvtrans <- function(x) round(sqrt(x) * 40/max(sqrt(colourvar),na.rm=TRUE)) + 1 # to compress colour scale at higher LRT
colvpoints <- c(0.5, 2, 5, 20, 50, 200, 500) # legend points
colvpoints <- colvpoints[union(1:2,which(colvpoints < maxcolvplot))]
if(length(colvpoints) > 5) colvpoints <- colvpoints[seq(1,length(colvpoints),2)]
}
maxtrans <- colvtrans(maxcolvplot)
mintrans <- colvtrans(mincolvplot)
transpoints <- colvtrans(colvpoints)
HDxlim[2] <- HDxlim[2] + 0.13 # make some room for legend
legend_image <- as.raster(matrix(rev(finpalette[1:maxtrans]), ncol = 1))
HDc <- HDcol
if(is.null(HDcol)) HDc <- finpalette[colvtrans(pmax(mincolvplot, pmin(colourvar, maxcolvplot)))]
uplot <- which(SNPN > 0)
if( HDylim[1] == -Inf) HDylim[1] <- min(SNPz[uplot])
if( HDylim[2] == Inf) HDylim[2] <- max(SNPz[uplot])
plotch <- rep(1,nsnps)
plotch[which(SNPz > HDylim[2])] <- 94 # ^
plotch[which(SNPz < HDylim[1])] <- 118 # v
plotord <- 1:nsnps
if(is.null(HDcol)) {
if(sortcol == "asc") plotord <- order(colourvar)
if(sortcol == "desc") plotord <- order(colourvar, decreasing = TRUE)
}
png(paste0(plotname,".png"), width = 960, height = 960, pointsize = cex.pointsize * 18)
if(whitedist(finpalette) < 25) par(bg="grey")
plot(pmax(pmin(SNPz,HDylim[2]),HDylim[1])[plotord][uplot] ~ Het[plotord][uplot], col = HDc[plotord][uplot], cex = 0.8, pch = plotch[plotord][uplot],
xlab = "Proportion of heterozygotes (H)", ylab = "Read ratio deviation (D)", cex.lab = 1.5, xlim=HDxlim, ylim=HDylim)
# frame.plot = FALSE,
if(is.null(HDcol)) {
x0 <- 0.96 # lhs of key
rect((x0-.05)*HDxlim[2], 1.1*HDylim[1]+0.05*HDylim[2], (x0+.13)*HDxlim[2], 0.85*HDylim[1]+0.15*HDylim[2], col="white", border="grey", xpd=NA)
rasterImage(legend_image, x0*HDxlim[2], HDylim[1], (x0+0.04)*HDxlim[2], 0.9*HDylim[1]+0.1*HDylim[2])
text(x = (x0+0.07)*HDxlim[2], y = HDylim[1] + 0.1*(HDylim[2]-HDylim[1]) * (transpoints-mintrans)/(maxtrans-mintrans), labels = format(colvpoints), xpd=NA, cex=0.8)
text(x = (x0+0.03)*HDxlim[2], y =0.88*HDylim[1]+0.12*HDylim[2], labels = legendlab, cex = 1, xpd=NA)
}
dev.off()
}
}
mafplot <- function(MAF=maf,plotname="MAF", barcol="grey", doplot=TRUE, ...) {
if(doplot) png(paste0(plotname,".png"), pointsize = cex.pointsize * 12)
histinfo <- hist(MAF, breaks = 50, xlab = "Minor Allele Frequency", col = barcol, plot= doplot, ...)
if(doplot) dev.off()
if(doplot) histinfo <- paste(plotname," png output created")
histinfo
}
na.zero <- function (x) {
x[is.na(x)] <- 0
return(x)
}
calcp <- function(indsubset, pmethod="A") {
if(!pmethod == "G") pmethod <- "A"
if (missing(indsubset)) indsubset <- 1:nind
if(exists("depth")) if(any(depth==Inf)) pmethod <- "G"
if (pmethod == "A") {
if (nrow(alleles) == nind) {
alleles <- alleles[indsubset,,drop=FALSE] # alleles not repaced in parent env
if(any(alleles==Inf)) {
hetInf <- alleles[,seq(2, 2 * nsnps, 2)] * alleles[,seq(1, 2 * nsnps - 1, 2)]
alleles <- pmin(alleles,2) # perhaps a bit approx? only use if genon not available
uInf <- which(hetInf==Inf, arr.ind=TRUE)
uInf[,2] <- 2*uInf[,2]
alleles[uInf] <- alleles[uInf]/2
uInf[,2] <- uInf[,2]-1
alleles[uInf] <- alleles[uInf]/2
}
RAcountstemp <- matrix(colSums(alleles), ncol = 2, byrow = TRUE) # 1 row per SNP, ref and alt allele counts
afreqs <- RAcountstemp[, 1]/rowSums(RAcountstemp) # p for ref allele - based on # reads, not on inferred # alleles
} else {
afreqs <- NULL
print("Error: alleles is wrong size for pmethod A, using pmethod G instead")
pmethod <- "G"
}
}
if (pmethod == "G") afreqs <- colMeans(genon[indsubset,,drop=FALSE], na.rm = TRUE)/2 # allele freq assuming genotype calls
afreqs
}
GBSsummary <- function() {
gform <- tolower(gform)
depthinfo <- FALSE
havedepth <- exists("depth") # if depth present, assume it and genon are correct & shouldn't be recalculated (as alleles may be the wrong one)
if(havedepth & !gform %in% c("vcf","chip")) cat("Warning: depth object already exists - reusing\n")
if(gform != "chip") {
if (!havedepth) depth <<- alleles[, seq(1, 2 * nsnps - 1, 2),drop=FALSE] + alleles[, seq(2, 2 * nsnps, 2),drop=FALSE]
if(is.null(dim(depth))) depth <<- matrix(depth,nrow=nind,ncol=nsnps)
# storage.mode(depth) <- "integer"
if (nchar(negC) > 0) negCstats <<- negCreport(negpos=do.call(grep,c(list(negC,seqID),negCsettings)),removeneg=TRUE) # check and report negative controls
if (have_rcpp & storage.mode(depth)=="integer") {
sampdepth.max <- rcpp_rowMaximums(depth, nThreads)
} else {
sampdepth.max <- apply(depth, 1, max)
}
sampdepth <<- rowMeans(depth)
depthinfo <- TRUE; if(min(depth)==Inf) depthinfo <- FALSE
ssraw <- data.frame(seqID, sampdepth); if (exists("negCstats")) ssraw <- rbind(negCstats[,c(1,3)],ssraw)
if(depthinfo) write.csv(ssraw, "SampleStatsRaw.csv", row.names = FALSE)
u0 <- which(sampdepth.max == 0)
u1 <- setdiff(which(sampdepth.max == ifelse(sampdepth.thresh==0,0,1) | sampdepth < sampdepth.thresh), u0)
nmax0 <- length(u0)
nmax1 <- length(u1)
seqID.removed <<- character(0)
if (nmax0 > 0) {
cat(nmax0, "samples with no calls (maximum depth = 0) removed:\n")
seqID.removed <<- seqID[u0]
print(data.frame(indnum = u0, seqID = seqID.removed, sampdepth = sampdepth[u0]))
}
if (nmax1 > 0) {
cat(nmax1, "samples with maximum depth of 1 and/or mean depth <", sampdepth.thresh, "removed:\n")
print(data.frame(indnum = u1, seqID = seqID[u1], sampdepth = sampdepth[u1]))
seqID.removed <<- c(seqID.removed, seqID[u1])
}
samp.remove(union(u0, u1))
if (!gform == "chip") {
if (!havedepth) {
genon <<- alleles[, seq(1, 2 * nsnps - 1, 2)]/depth
if(max(alleles)==Inf) {
atemp <- alleles
atemp[alleles==Inf] <- 1e6
dtemp <- atemp[, seq(1, 2 * nsnps - 1, 2)] + atemp[, seq(2, 2 * nsnps, 2)]
genon <<- atemp[, seq(1, 2 * nsnps - 1, 2)]/dtemp
}
genon <<- trunc(2*genon-1)+1
}
# uhet <- which(!genon^2 == genon)
# genon <<- 2*genon
# genon[uhet] <<- 1
if(outlevel > 7) {
samples <<- genon
samples[na.zero(genon) == 1] <<- 2* (sample.int(2, sum(genon == 1, na.rm=TRUE), replace = TRUE) - 1) # allows genon to have > .Machine$integer.max elements
}
# rm(uhet)
}
gc()
docalcp <- FALSE
if (!exists("p") | length(seqID.removed) > 0) docalcp <- TRUE
if (exists("p")) if (length(p) != nsnps) docalcp <- TRUE
if (docalcp) { # not redone e.g. after merge
p <<- calcp()
if (is.null(p)) {
cat("alleles not available, using genotype method for p\n")
p <<- calcp(pmethod="G")
}
}
if(exists("genosin")) rm(genosin)
} #end GBS-specific
write.csv(data.frame(seqID = seqID), "seqID.csv", row.names = FALSE)
snpdepth <<- colMeans(depth)
uremove <- which(p < maf.thresh | p > 1-maf.thresh | snpdepth < snpdepth.thresh) # | is.nan(p) # is this needed too? implies depth=0
nmaf0 <- length(which(p < maf.thresh | p > 1-maf.thresh))
cat(nmaf0, "SNPs with MAF <",maf.thresh,"and", length(uremove)-nmaf0, "SNPs with no data or with depth <", snpdepth.thresh, "removed\n")
snp.remove(uremove)
cat("Analysing", nind, "individuals and", nsnps, "SNPs\n")
###### compare allele frequency estimates from allele counts and from genotype calls (& from input file, if uneak format)
pg <<- colMeans(genon, na.rm = TRUE)/2 # allele freq assuming genotype calls
if(outlevel > 4) {
png("AlleleFreq.png", width = 960, height = 960, pointsize = cex.pointsize * 18)
p.lab <- "Allele frequency from allele counts"
pg.lab <- "Allele frequency from genotype calls"
AF.lab <- "Allele frequency given"
if (outlevel > 6 & exists("AFrq")) pairs(cbind(pg, p, AFrq), col = "#80808020", pch = 16, cex = 0.8, labels = c(pg.lab, p.lab, AF.lab))
if (outlevel < 7 | !exists("AFrq")) plot(pg ~ p, col="#80808020", pch=16, cex=0.8, xlab=p.lab, ylab=pg.lab)
dev.off()
}
# calc some overall snp stats
HWstats <- HWpops(depthmat = depth)
HWdis <<- HWstats$HWdis[1,]; l10LRT <<- HWstats$l10LRT[1,]; x2star <<- HWstats$x2star[1,]; l10pstar <<- HWstats$l10pstar[1,]; maf <<- HWstats$maf[1,]
LRT <- qchisq(-log(10)*l10LRT, 1, lower.tail = FALSE, log.p = TRUE)
sampdepth <<- rowMeans(depth) # recalc after removing SNPs and samples
#if(outlevel > 4) sampdepth.med <<- apply(depth, 1, median)
if(outlevel > 4) {
if (have_rcpp & storage.mode(depth)=="integer") {
sampdepth.med <<- rcpp_rowMedians(depth, nThreads)
} else {
sampdepth.med <<- apply(depth, 1, median)
}
}
depth0 <- rowSums(depth == 0)
snpdepth <<- colMeans(depth)
missrate <- sum(as.numeric(depth == 0))/nrow(depth)/ncol(depth)
cat("Proportion of missing genotypes: ", missrate, "Callrate:", 1-missrate,"\n")
callrate <<- 1 - rowSums(depth == 0)/nsnps # sample callrate, after removing SNPs, samples
SNPcallrate <<- 1 - colSums(depth == 0)/nind
png("CallRate.png", width = 480, height = 480, pointsize = cex.pointsize * 12)
hist(callrate, seq(0,1,0.02), col = "cornflowerblue", border = "cornflowerblue", main = "Histogram of sample call rates", xlab = "Call rate (proportion of SNPs scored)")
dev.off()
png("SNPCallRate.png", width = 480, height = 480, pointsize = cex.pointsize * 12)
# suggested by Jaroslav Klapste (Scion)
hist(SNPcallrate, seq(0,1,0.02), col = "cornflowerblue", border = "cornflowerblue", main = "Histogram of SNP call rates", xlab = "Call rate (proportion of samples scored)")
dev.off()
if (!depthinfo) write.csv(data.frame(seqID, callrate), "SampleStats.csv", row.names = FALSE)
if (depthinfo) {
write.csv(data.frame(seqID, callrate, sampdepth), "SampleStats.csv", row.names = FALSE)
sampdepth.scored <- sampdepth * nsnps/(nsnps - depth0)
ufinite <- which(sampdepth < Inf)
haschip <- (length(ufinite) < nind)
noninf <- ""; if (haschip) noninf <- "(non-chip)"
cat("Mean sample depth:", mean(sampdepth), "\n")
if(haschip) cat("Mean sample depth (non-chip):", mean(sampdepth[ufinite]), "\n")
if(length(ufinite) > 0) {
if(outlevel > 4) {
png("SampDepth.png", width = 480, height = 480, pointsize = cex.pointsize * 12)
plot(sampdepth[ufinite] ~ sampdepth.med[ufinite], col = "#80808080", pch = 16, cex = 1.2, main = paste("Sample Depth",noninf), xlab = "Median", ylab = "Mean")
dev.off()
}
png("SampDepth-scored.png", width = 480, height = 480, pointsize = cex.pointsize * 12)
plot(sampdepth.scored[ufinite] ~ sampdepth[ufinite], col = "#80808080", pch = 16, cex = 1.2, main = paste("Sample Depth",noninf), xlab = "Mean", ylab = "Mean with depth>0")
dev.off()
png("SampDepthHist.png", width = 480, height = 480, pointsize = cex.pointsize * 12)
hist(sampdepth[ufinite], 100, col = "cornflowerblue", border = "cornflowerblue", main = paste("Histogram of mean sample depth",noninf), xlab = "Mean sample depth")
dev.off()
png("SampDepthCR.png", width = 480, height = 480, pointsize = cex.pointsize * 12)
plot(sampdepth[ufinite] ~ callrate[ufinite], col = "#80808080", pch = 16, cex = 1.2, main = paste("Sample Depth v Call rate",noninf), xlab = "Sample call rate", ylab = "Mean sample depth")
dev.off()
}
ufinite <- which(snpdepth < Inf)
haschip <- (length(ufinite) < nsnps)
noninf <- ""; if (haschip) noninf <- "(non-chip)"
if(length(ufinite) >0 ) {
png("SNPDepthHist.png", width = 480, height = 480, pointsize = cex.pointsize * 12)
hist(snpdepth[ufinite], 100, col = "cornflowerblue", border = "cornflowerblue", main = paste("Histogram of mean SNP depth",noninf), xlab = "Mean SNP depth")
dev.off()
png("SNPDepth.png", width = 640, height = 640, pointsize = cex.pointsize * 15)
plot(SNPcallrate[ufinite] ~ snpdepth[ufinite], log="x", col = "#80808080", pch = 16, cex = 1, main = paste("SNP Depth",noninf), ylab = "SNP Call rate (proportion of samples scored)",
xlab = "Mean SNP depth (log scale)")
dev.off()
}
}
xaxpts <<- qchisq(QQprobpts,df=1)
finplot()
x2starplots()
if(outlevel > 9) HWsigplot(plotname="HWdisMAFsig-raw")
if(outlevel > 4) {
yaxpts <- quantile(LRT,QQprobpts, na.rm=TRUE)
png("LRT-QQ.png", width = 480, height = 480, pointsize = cex.pointsize * 12)
qqplot(qchisq(ppoints(nsnps), df = 1), LRT, cex=0.75, main = "Hardy-Weinberg LRT Q-Q Plot",
xlab = parse(text = "Theoretical ~~ (chi[1]^2) ~~ Quantiles"), ylab = "Sample Quantiles")
if(length(QQprobpts) > 0) {
for(ipt in 1:length(QQprobpts)) {
lines(x=c(0,xaxpts[ipt],xaxpts[ipt]),y=c(yaxpts[ipt],yaxpts[ipt],0), col="grey")
}
text(x=xaxpts/4,y=yaxpts,labels=QQprobpts, pos=3, col="grey")
}
dev.off()
png("LRT-hist.png", width = 480, height = 480, pointsize = cex.pointsize * 12)
hist(LRT, breaks = 50, col = "grey", xlab = "Hardy Weinberg likelihood ratio test statistic")
dev.off()
}
mafplot()
######### depth[depth < 2] <<- 1.1 # not using depth values <2 after this so set to >1 to avoid 0 divisor note do not use depth.max <2 though
fcolo <<- rep("black", nind) # modify this to specify colours for individuals in the plots
}
palette.aquatic <- colorRampPalette(c(rgb(200, 200, 200, max = 255), "blue"))(50) # grey to blue
palette.terrain <- terrain.colors(50)
palette.temperature <- colorRampPalette(c("blue","white","red"))(50)
whitedist <- function(pal) min(colSums(abs(col2rgb(pal)-matrix(255,nrow=3,ncol=length(pal)))))
if(!functions.only) {
readGBS()
GBSsummary()
} # !functions.only
################## functions
upper.vec <- function(sqMatrix,diag=FALSE) as.vector(sqMatrix[upper.tri(sqMatrix,diag=diag)])
corner <- function(mtx, size=6L) print(mtx[1:size,1:size])
eqline <- function(a=0,b=1,col="red",lwd=2, ...) abline(a=a,b=b,col=col,lwd=lwd, ...)
colMax <- function(x, na.rm = FALSE) apply(x,2,max,na.rm=na.rm)
rowMax <- function(x, na.rm = FALSE) apply(x,1,max,na.rm=na.rm)
rowMin <- function(x, na.rm = FALSE) apply(x,1,min,na.rm=na.rm)
colMin <- function(x, na.rm = FALSE) apply(x,2,min,na.rm=na.rm)
colwhich.Max <- function(x, na.rm = FALSE) apply(x,2,which.max)
rowwhich.Max <- function(x, na.rm = FALSE) apply(x,1,which.max)
colwhich.Min <- function(x, na.rm = FALSE) apply(x,2,which.min)
rowwhich.Min <- function(x, na.rm = FALSE) apply(x,1,which.min)
#seq2samp <- function(seqIDvec=seqID) read.table(text=seqIDvec,sep="_",stringsAsFactors=FALSE,fill=TRUE)[,1] # might not get number of cols right
seq2samp1 <- function(seqIDvec=seqID, splitby="_", ...) sapply(strsplit(seqIDvec,split=splitby, ...),"[",1)
seq2samp <- function(seqIDvec=seqID, splitby="_",nparts=NULL,dfout=FALSE, ...){
if(is.null(nparts)) {
sampout <- sapply(strsplit(seqIDvec,split=splitby, ...),"[",1)
} else {
nparts <- as.integer(nparts)
if(nparts < 2) nparts <- 2
nsep <- nchar(gsub(paste0("[^",splitby,"]"),"",seqIDvec))
seqIDvec1 <- paste0(seqIDvec, strrep(splitby,pmax(0,nparts-1-nsep)))
if(splitby %in% c(".","$","^","|","?","*","+","(",")")) splitby <- paste0("\\",splitby)
stringpart <- paste(rep("(\\S*)",nparts),collapse=splitby) # * means 0 or more times
stringpart <- paste(rep("(.*)",nparts),collapse=splitby)
seqIDexpr <- paste0("^",stringpart,"$")
sampout <- gsub(seqIDexpr,"\\1",seqIDvec1)
if(dfout) {
sampout <- as.data.frame(sampout,stringsAsFactors=FALSE)
for(ipart in 2:nparts) sampout <- cbind(sampout, gsub(seqIDexpr,paste0("\\",ipart),seqIDvec1),stringsAsFactors=FALSE)
colnames(sampout) <- paste0("V",1:nparts)
}
}
sampout
}
colourby <- function(colgroup, nbreaks=0, col.name=NULL, symbgroup=NULL, symb.name=NULL,groupsort=FALSE,grouporder=NULL, maxlight=1,alpha=1,
reverse=FALSE,symbset=NULL, stdpal= "rainbow", hclpals=character(0),pal.upper=1, nacolour="black") {
#maxlight is maximum lightness between 0 and 1
#nbreaks is suggested # breaks
isgroups <- (nbreaks==0)
npals <- length(hclpals)
if(isgroups) {
collabels <- unique(colgroup)
if(!is.null(nacolour)) collabels <- na.omit(collabels)
if(groupsort) collabels <- sort(collabels,na.last=TRUE)
if(!is.null(grouporder)) if(length(grouporder)==length(collabels)) collabels <- collabels[grouporder]
}
if(!isgroups) {
histinfo <- hist(colgroup,nbreaks,plot=FALSE)
collabels <- histinfo$mids
colgroup <- collabels[cut(colgroup,breaks = histinfo$breaks, labels=FALSE, include.lowest=TRUE)] # redefine input
} else collabels <- as.character(collabels)
ncol <- length(collabels)
if( tryCatch(is.matrix(col2rgb(stdpal[1])), error=function(e) FALSE)) {collist <- stdpal; stdpal <- "rainbow"}
if(stdpal=="rainbow") collist <- rainbow(ncol)
if(grepl("heat",stdpal)) collist <- heat.colors(ncol)
if(grepl("terrain",stdpal)) collist <- terrain.colors(ncol)
if(grepl("topo",stdpal)) collist <- topo.colors(ncol)
if(grepl("cm",stdpal)) collist <- cm.colors(ncol)
if(ncol > 8 & isgroups) collist[seq(2,ncol,2)] <- rgb((t(col2rgb(collist[seq(2,ncol,2)]))+matrix(127,ncol=3,nrow=floor(ncol/2)))/2,maxColorValue = 255) # darken every 2nd one
if(npals > 0 & exists("hcl.colors")) {
for(ipal in 1:npals) {
addcols <- hcl.colors(ceiling(ncol/(npals*pal.upper)),pal=hclpals[ipal])[1:ceiling(ncol/npals)]
if(ipal==1) collist <- addcols else collist <- c(collist,addcols)
}
collist <- collist[1:ncol] # trim if needed
}
lightness <- colSums( col2rgb(collist))/(3*255)
collist <- rgb(t(col2rgb(collist) %*% diag(pmin(lightness,rep(maxlight,length(lightness))) / lightness)), maxColorValue = 255)
if(alpha < 1) {
rgbcols <- col2rgb(collist)
collist <- rgb(rgbcols[1,],rgbcols[2,],rgbcols[3,],alpha*255,maxColorValue=255)
}
if(reverse) collist <- rev(collist)
sampcol <- collist[match(colgroup,collabels)]
sampcol[is.na(sampcol)] <- nacolour
outlist <- list(collabels=collabels,collist=collist,sampcol=sampcol)
if(!is.null(col.name)) outlist <- c(outlist,col.name=col.name)
if (all(as.integer(symbset)==symbset)) {
if(is.null(symbgroup)) {
symblist <- suppressWarnings(symbset + rep(0,ncol)) # uses R recycle
sampsymb <- symblist[match(colgroup,collabels)]
sampsymb[is.na(sampsymb)] <- 1 # make sure no NAs
outlist <- c(outlist,list(symblist=symblist,sampsymb=sampsymb))
} else {
symblabels <- unique(symbgroup)
if(groupsort) symblabels <- sort(symblabels,na.last=TRUE)
nsymb <- length(symblabels)
if(length(symbset) < nsymb) symbset <- union(symbset,1:nsymb)[1:nsymb] # augment with unused from 1,2, ...
sampsymb <- symbset[match(symbgroup,symblabels)]
sampsymb[is.na(sampsymb)] <- 1 # make sure no NAs
outlist <- c(outlist,list(symblabels = symblabels, symblist=symbset,sampsymb=sampsymb))
if(!is.null(symb.name)) outlist <- c(outlist,symb.name=symb.name)
}
}
outlist
}
changecol <- function(colobject,colposition,newcolour) {# provide new colours to colourby object
if(length(colposition)>0) {
if(length(colposition) != length(newcolour)) stop("Error: colposition and newcolour are different lengths")
oldcolour <- colobject$collist
colobject$collist[colposition] <- newcolour
colobject$sampcol <- colobject$collist[match(colobject$sampcol,oldcolour)]
}
colobject
}
# fade (Lumley+)
fade <- function(colors,alpha) {
if(alpha <= 1) alpha <- alpha*255
rgbcols <- col2rgb(colors)
rgb(rgbcols[1,],rgbcols[2,],rgbcols[3,],alpha,max=255)
}
# fadeco fade a colour object (based on Lumley's fade)
fadeco <- function(colobject,alpha) {
colobject$collist <- fade(colobject$collist, alpha)
colobject$sampcol <- fade(colobject$sampcol, alpha)
colobject
}
coloursub <- function(colobj, indsubset) {
#subset a colour object
if(missing(indsubset)) indsubset <- 1:length(colobj$sampcol)
colobj$sampcol <- colobj$sampcol[indsubset]
colobj$sampsymb <- colobj$sampsymb[indsubset]
ncol <- length(colobj$collist)
uused <- which(colobj$collist %in% colobj$sampcol)
colobj$collabels <- colobj$collabels[uused]
colobj$collist <- colobj$collist[uused]
if(is.null(colobj$symblabels)) {
if(length(colobj$symblist)==ncol) colobj$symblist <- colobj$symblist[uused]
} else {
uused <- which(colobj$symblist %in% colobj$sampsymb)