# function below works OK provided all .idat files are in the current working directory
# - could add an option to allow files in Illumina directory structure to be handled
# or to use the optional 'Path' column in sampleSheet
# - there is a lot of header information that is currently discarded - could try and store this somewhere in the resulting NChannelSet
readIdatFiles = function(sampleSheet=NULL,
arrayNames=NULL,
ids=NULL,
path=".",
arrayInfoColNames=list(barcode="SentrixBarcode_A", position="SentrixPosition_A"),
highDensity=FALSE,
sep="_",
fileExt=list(green="Grn.idat", red="Red.idat"),
saveDate=FALSE, verbose=FALSE) {
if(!is.null(arrayNames)) {
pd = new("AnnotatedDataFrame", data = data.frame(Sample_ID=arrayNames))
}
if(!is.null(sampleSheet)) { # get array info from Illumina's sample sheet
if(is.null(arrayNames)){
if(!is.null(arrayInfoColNames$barcode) && (arrayInfoColNames$barcode %in% colnames(sampleSheet))) {
barcode = sampleSheet[,arrayInfoColNames$barcode]
arrayNames=barcode
}
if(!is.null(arrayInfoColNames$position) && (arrayInfoColNames$position %in% colnames(sampleSheet))) {
position = sampleSheet[,arrayInfoColNames$position]
if(is.null(arrayNames))
arrayNames=position
else
arrayNames = paste(arrayNames, position, sep=sep)
if(highDensity) {
hdExt = list(A="R01C01", B="R01C02", C="R02C01", D="R02C02")
for(i in names(hdExt))
arrayNames = sub(paste(sep, i, sep=""), paste(sep, hdExt[[i]], sep=""), arrayNames)
}
}
}
pd = new("AnnotatedDataFrame", data = sampleSheet)
sampleNames(pd) = basename(arrayNames)
}
if(is.null(arrayNames)) {
arrayNames = gsub(paste(sep, fileExt$green, sep=""), "", dir(pattern=fileExt$green, path=path))
if(!is.null(sampleSheet)) {
sampleSheet=NULL
cat("Could not find required info in \'sampleSheet\' - ignoring. Check \'sampleSheet\' and/or \'arrayInfoColNames\'\n")
}
pd = new("AnnotatedDataFrame", data = data.frame(Sample_ID=arrayNames))
}
narrays = length(arrayNames)
grnfiles = paste(arrayNames, fileExt$green, sep=sep)
redfiles = paste(arrayNames, fileExt$red, sep=sep)
if(length(grnfiles)==0 || length(redfiles)==0)
stop("Cannot find .idat files")
if(length(grnfiles)!=length(redfiles))
stop("Cannot find matching .idat files")
if(path[1] != "." & path[1] != ""){
grnidats = file.path(path, grnfiles)
redidats = file.path(path, redfiles)
} else {
message("path arg not set. Assuming files are in local directory, or that complete path is provided")
grnidats = grnfiles
redidats = redfiles
}
if(!all(file.exists(grnidats))) stop("Missing some of the *Grn.idat files")
if(!all(file.exists(redidats))) stop("Missing some of the *Red.idat files")
headerInfo = list(nProbes = rep(NA, narrays),
Barcode = rep(NA, narrays),
ChipType = rep(NA, narrays),
Manifest = rep(NA, narrays), # not sure about this one - sometimes blank
Position = rep(NA, narrays)) # this may also vary a bit
dates = list(decode=rep(NA, narrays),
scan=rep(NA, narrays))
## read in the data
for(i in seq_along(arrayNames)) {
if(verbose) {
cat("reading", arrayNames[i], "\t")
cat(paste(sep, fileExt$green, sep=""), "\t")
}
idsG = idsR = G = R = NULL
G = readIDAT(grnidats[i])
idsG = rownames(G$Quants)
headerInfo$nProbes[i] = G$nSNPsRead
headerInfo$Barcode[i] = G$Barcode
headerInfo$ChipType[i] = G$ChipType
headerInfo$Manifest[i] = G$Unknown$MostlyNull
headerInfo$Position[i] = G$Unknowns$MostlyA
if(headerInfo$nProbes[i]>(headerInfo$nProbes[1]+10000) || headerInfo$nProbes[i]<(headerInfo$nProbes[1]-10000)) {
warning("Chips are not of the same type. Skipping ", basename(grnidats[i]), " and ", basename(redidats[i]))
next()
}
dates$decode[i] = G$RunInfo[1, 1]
dates$scan[i] = G$RunInfo[2, 1]
if(i==1) {
if(is.null(ids) && !is.null(G)){
ids = idsG
} else stop("Could not find probe IDs")
nprobes = length(ids)
narrays = length(arrayNames)
RG = new("NChannelSet",
R=matrix(NA, nprobes, narrays),
G=matrix(NA, nprobes, narrays),
zero=matrix(NA, nprobes, narrays),
annotation=headerInfo$Manifest[1],
phenoData=pd, storage.mode="environment")
featureNames(RG) = ids
if(!is.null(sampleSheet) && !is.null(sampleSheet$Sample_ID)){
sampleNames(RG) = sampleSheet$Sample_ID
} else sampleNames(RG) = arrayNames
gc()
}
if(length(ids)==length(idsG)) {
if(sum(ids==idsG)==nprobes) {
RG@assayData$G[,i] = G$Quants[, "Mean"]
zeroG = G$Quants[, "NBeads"]==0
}
} else {
indG = match(ids, idsG)
RG@assayData$G[,i] = G$Quants[indG, "Mean"]
zeroG = G$Quants[indG, "NBeads"]==0
}
rm(G)
gc()
if(verbose) {
cat(paste(sep, fileExt$red, sep=""), "\n")
}
R = readIDAT(redidats[i])
idsR = rownames(R$Quants)
if(length(ids)==length(idsG)) {
if(sum(ids==idsR)==nprobes) {
RG@assayData$R[,i] = R$Quants[ ,"Mean"]
zeroR = R$Quants[ ,"NBeads"]==0
}
} else {
indR = match(ids, idsR)
RG@assayData$R[,i] = R$Quants[indR, "Mean"]
zeroR = R$Quants[indR, "NBeads"]==0
}
RG@assayData$zero[,i] = zeroG | zeroR
rm(R, zeroG, zeroR)
gc()
}
if(saveDate) {
protocolData(RG)[["ScanDate"]] = dates$scan
}
storageMode(RG) = "lockedEnvironment"
RG
}
## the readIDAT() and readBPM() functions below were provided by Keith Baggerly, 27/8/2008
readIDAT <- function(idatFile){
fileSize <- file.info(idatFile)$size
tempCon <- file(idatFile,"rb")
prefixCheck <- readChar(tempCon,4)
if(prefixCheck != "IDAT"){
}
versionNumber <- readBin(tempCon, "integer", n=1, size=8,
endian="little", signed=FALSE)
if(versionNumber<3)
stop("Older style IDAT files not supported: consider updating your scanner settings")
nFields <- readBin(tempCon, "integer", n=1, size=4,
endian="little", signed=FALSE)
fields <- matrix(0,nFields,3);
colnames(fields) <- c("Field Code", "Byte Offset", "Bytes")
for(i1 in 1:nFields){
fields[i1,"Field Code"] <-
readBin(tempCon, "integer", n=1, size=2, endian="little", signed=FALSE)
fields[i1,"Byte Offset"] <-
readBin(tempCon, "integer", n=1, size=8, endian="little", signed=FALSE)
}
knownCodes <- c(1000, 102, 103, 104, 107, 200, 300, 400,
401, 402, 403, 404, 405, 406, 407, 408, 409)
names(knownCodes) <-
c("nSNPsRead", # 1000
"IlluminaID", # 102
"SD", # 103
"Mean", # 104
"NBeads", # 107
"MidBlock", # 200
"RunInfo", # 300
"RedGreen", # 400
"MostlyNull", # 401
"Barcode", # 402
"ChipType", # 403
"MostlyA", # 404
"Unknown.1", # 405
"Unknown.2", # 406
"Unknown.3", # 407
"Unknown.4", # 408
"Unknown.5" # 409
)
nNewFields <- 1
rownames(fields) <- paste("Null", 1:nFields)
for(i1 in 1:nFields){
temp <- match(fields[i1,"Field Code"], knownCodes)
if(!is.na(temp)){
rownames(fields)[i1] <- names(knownCodes)[temp]
}else{
rownames(fields)[i1] <- paste("newField", nNewFields, sep=".")
nNewFields <- nNewFields + 1
}
}
seek(tempCon, fields["nSNPsRead", "Byte Offset"])
nSNPsRead <- readBin(tempCon, "integer", n=1, size=4,
endian="little", signed=FALSE)
seek(tempCon, fields["IlluminaID", "Byte Offset"])
IlluminaID <- readBin(tempCon, "integer", n=nSNPsRead, size=4,
endian="little", signed=FALSE)
seek(tempCon, fields["SD", "Byte Offset"])
SD <- readBin(tempCon, "integer", n=nSNPsRead, size=2,
endian="little", signed=FALSE)
seek(tempCon, fields["Mean", "Byte Offset"])
Mean <- readBin(tempCon, "integer", n=nSNPsRead, size=2,
endian="little", signed=FALSE)
seek(tempCon, fields["NBeads", "Byte Offset"])
NBeads <- readBin(tempCon, "integer", n=nSNPsRead, size=1, signed=FALSE)
seek(tempCon, fields["MidBlock", "Byte Offset"])
nMidBlockEntries <- readBin(tempCon, "integer", n=1, size=4,
endian="little", signed=FALSE)
MidBlock <- readBin(tempCon, "integer", n=nMidBlockEntries, size=4,
endian="little", signed=FALSE)
seek(tempCon, fields["RunInfo", "Byte Offset"])
nRunInfoBlocks <- readBin(tempCon, "integer", n=1, size=4,
endian="little", signed=FALSE)
RunInfo <- matrix(NA, nRunInfoBlocks, 5)
colnames(RunInfo) <- c("RunTime", "BlockType", "BlockPars",
"BlockCode", "CodeVersion")
for(i1 in 1:2) { #nRunInfoBlocks){ ## MR edit
for(i2 in 1:5){
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
RunInfo[i1,i2] <- readChar(tempCon, nChars)
}
}
seek(tempCon, fields["RedGreen", "Byte Offset"])
RedGreen <- readBin(tempCon, "numeric", n=1, size=4,
endian="little", signed=FALSE)
#RedGreen <- readBin(tempCon, "integer", n=4, size=1,
# endian="little", signed=FALSE)
seek(tempCon, fields["MostlyNull", "Byte Offset"])
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
MostlyNull <- readChar(tempCon, nChars)
seek(tempCon, fields["Barcode", "Byte Offset"])
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
Barcode <- readChar(tempCon, nChars)
seek(tempCon, fields["ChipType", "Byte Offset"])
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
ChipType <- readChar(tempCon, nChars)
seek(tempCon, fields["MostlyA", "Byte Offset"])
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
MostlyA <- readChar(tempCon, nChars)
seek(tempCon, fields["Unknown.1", "Byte Offset"])
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
Unknown.1 <- readChar(tempCon, nChars)
seek(tempCon, fields["Unknown.2", "Byte Offset"])
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
Unknown.2 <- readChar(tempCon, nChars)
seek(tempCon, fields["Unknown.3", "Byte Offset"])
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
Unknown.3 <- readChar(tempCon, nChars)
seek(tempCon, fields["Unknown.4", "Byte Offset"])
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
Unknown.4 <- readChar(tempCon, nChars)
seek(tempCon, fields["Unknown.5", "Byte Offset"])
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
Unknown.5 <- readChar(tempCon, nChars)
close(tempCon)
Unknowns <-
list(MostlyNull=MostlyNull,
MostlyA=MostlyA,
Unknown.1=Unknown.1,
Unknown.2=Unknown.2,
Unknown.3=Unknown.3,
Unknown.4=Unknown.4,
Unknown.5=Unknown.5)
Quants <- cbind(Mean, SD, NBeads)
colnames(Quants) <- c("Mean", "SD", "NBeads")
rownames(Quants) <- as.character(IlluminaID)
idatValues <-
list(fileSize=fileSize,
versionNumber=versionNumber,
nFields=nFields,
fields=fields,
nSNPsRead=nSNPsRead,
#IlluminaID=IlluminaID,
#SD=SD,
#Mean=Mean,
#NBeads=NBeads,
Quants=Quants,
MidBlock=MidBlock,
RunInfo=RunInfo,
RedGreen=RedGreen,
Barcode=Barcode,
ChipType=ChipType,
Unknowns=Unknowns)
idatValues
}
readBPM <- function(bpmFile){
## Reads and parses Illumina BPM files
fileSize <- file.info(bpmFile)$size
tempCon <- file(bpmFile,"rb")
# The first few bytes of the egtFile are some type of
# header, but there's no related byte offset information.
prefixCheck <- readChar(tempCon,3) ## should be "BPM"
null.1 <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
## should be 1
versionNumber <-
readBin(tempCon, "integer", n=1, size=4, endian="little", signed=FALSE)
## should be 4
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
chipType <- readChar(tempCon, nChars)
null.2 <- readBin(tempCon, "integer", n=2, size=1, signed=FALSE)
csvLines <- readLines(tempCon, 22)
entriesByteOffset <- seek(tempCon);
nEntries <- readBin(tempCon, "integer", n=1, size=4,
endian="little", signed=FALSE)
if(FALSE){
snpIndexByteOffset <- seek(tempCon)
snpIndex <- readBin(tempCon, "integer", n=nEntries, size=4,
endian="little", signed=FALSE)
## for the 1M array, these are simply in order from 1 to 1072820.
snpNamesByteOffset <- seek(tempCon)
snpNames <- rep("A", nEntries)
for(i1 in 1:nEntries){
nChars <- readBin(tempCon, "integer", n=1, size=1, signed=FALSE)
snpNames[i1] <- readChar(tempCon, nChars)
}
}
seek(tempCon, 15278138)
normIDByteOffset <- seek(tempCon)
normID <- readBin(tempCon, "integer", n=nEntries, size=1, signed=FALSE) + 1
newBlockByteOffset <- seek(tempCon)
newBlock <- readBin(tempCon, "integer", n=10000, size=1, signed=FALSE)
close(tempCon)
byteOffsets <- list(entriesByteOffset=entriesByteOffset,
#snpIndexByteOffset=snpIndexByteOffset,
#snpNamesByteOffset=snpNamesByteOffset,
normIDByteOffset=normIDByteOffset,
newBlockByteOffset=newBlockByteOffset)
allStuff <- list(prefixCheck=prefixCheck,
null.1=null.1,
versionNumber=versionNumber,
chipType=chipType,
null.2=null.2,
csvLines=csvLines,
nEntries=nEntries,
#snpIndex=snpIndex,
#snpNames=snpNames,
normID=normID,
newBlock=newBlock,
byteOffsets=byteOffsets)
allStuff
}
RGtoXY = function(RG, chipType, verbose=TRUE) {
chipList = c("human1mv1c", # 1M
"human370v1c", # 370CNV
"human650v3a", # 650Y
"human610quadv1b", # 610 quad
"human660quadv1a", # 660 quad
"human370quadv3c", # 370CNV quad
"human550v3b", # 550K
"human1mduov3b", # 1M Duo
"humanomni1quadv1b", # Omni1 quad
"humanomni25quadv1b", # Omni2.5 quad
"humanomniexpress12v1b", # Omni express 12
"humanimmuno12v1b") # Immuno chip 12
if(missing(chipType)){
chipType = match.arg(annotation(RG), chipList)
} else chipType = match.arg(chipType, chipList)
pkgname = getCrlmmAnnotationName(chipType)
if(!require(pkgname, character.only=TRUE, quietly=!verbose)){
suggCall = paste("library(", pkgname, ", lib.loc='/Altern/Lib/Loc')", sep="")
msg = paste("If", pkgname, "is installed on an alternative location, please load it manually by using", suggCall)
message(strwrap(msg))
stop("Package ", pkgname, " could not be found.")
rm(suggCall, msg)
}
if(verbose) message("Loading chip annotation information.")
loader("address.rda", .crlmmPkgEnv, pkgname)
aids = getVarInEnv("addressA") # comes from AddressA_ID or Address column in manifest
bids = getVarInEnv("addressB") # comes from AddressB_ID or Address2 column in manifest
ids = names(aids)
snpbase = getVarInEnv("base")
nsnps = length(aids)
narrays = ncol(RG)
# aidcol = match("AddressA_ID", colnames(annot))
# if(is.na(aidcol))
# aidcol = match("Address", colnames(annot))
# bidcol = match("AddressB_ID", colnames(annot))
# if(is.na(bidcol))
# bidcol = match("Address2", colnames(annot))
# aids = annot[, aidcol]
# bids = annot[, bidcol]
# snpids = annot[,"Name"]
# snpbase = annot[,"SNP"]
infI = !is.na(bids) & bids!=0
aord = match(aids, featureNames(RG)) # NAs are possible here
bord = match(bids, featureNames(RG)) # and here
# argrg = aids[rrgg]
# brgrg = bids[rrgg]
XY = new("NChannelSet",
X=matrix(0, nsnps, narrays),
Y=matrix(0, nsnps, narrays),
zero=matrix(0, nsnps, narrays),
annotation=chipType, phenoData=RG@phenoData,
protocolData=RG@protocolData, storage.mode="environment")
featureNames(XY) = ids
sampleNames(XY) = sampleNames(RG)
gc()
# Need to initialize - matrices filled with NAs to begin with
# XY@assayData$X[1:nsnps,] = 0
# XY@assayData$Y[1:nsnps,] = 0
# XY@assayData$zero[1:nsnps,] = 0
# First sort out Infinium II SNPs, X -> R (allele A) and Y -> G (allele B) from the same probe
XY@assayData$X[!is.na(aord),] = exprs(channel(RG, "R"))[aord[!is.na(aord)],] # mostly red
XY@assayData$Y[!is.na(aord),] = exprs(channel(RG, "G"))[aord[!is.na(aord)],] # mostly green
XY@assayData$zero[!is.na(aord),] = exprs(channel(RG, "zero"))[aord[!is.na(aord)],] # mostly green
gc()
## Warning - not 100% sure that the code below is correct - could be more complicated than this
# Next Infinium I where X -> R from allele A probe and Y -> R from allele B probe
# infIRR = infI & (snpbase=="[A/T]" | snpbase=="[T/A]" | snpbase=="[a/t]" | snpbase=="[t/a]")
# X[infIRR,] = exprs(channel(RG, "R"))[aord[infIRR],] # mostly red
# Y[infIRR,] = exprs(channel(RG, "R"))[bord[infIRR],] # mostly green
# Finally Infinium I where X -> G from allele A probe and Y -> G from allele B probe
# infIGG = infI & (snpbase=="[C/G]" | snpbase=="[G/C]" | snpbase=="[g/c]" | snpbase=="[c/g]")
# X[infIGG,] = exprs(channel(RG, "G"))[aord[infIGG],] # mostly red
# Y[infIGG,] = exprs(channel(RG, "G"))[bord[infIGG],] # mostly green
# For now zero out Infinium I probes
# XY@assayData$X[infI,] = 0
# XY@assayData$Y[infI,] = 0
# XY@assayData$zero[infI,] = 0
# gc()
XY
}
stripNormalize = function(XY, useTarget=TRUE, verbose=TRUE) {
pkgname = getCrlmmAnnotationName(annotation(XY))
if(!require(pkgname, character.only=TRUE, quietly=!verbose)){
suggCall = paste("library(", pkgname, ", lib.loc='/Altern/Lib/Loc')", sep="")
msg = paste("If", pkgname, "is installed on an alternative location, please load it manually by using", suggCall)
message(strwrap(msg))
stop("Package ", pkgname, " could not be found.")
rm(suggCall, msg)
}
if(verbose) message("Loading strip and reference normalization information.")
loader("preprocStuff.rda", .crlmmPkgEnv, pkgname)
stripnum = getVarInEnv("stripnum")
if(useTarget)
targetdist = getVarInEnv("reference")
if(verbose){
message("Quantile normalizing ", ncol(XY), " arrays by ", max(stripnum), " strips.")
if (getRversion() > '2.7.0') pb = txtProgressBar(min=0, max=max(stripnum), style=3)
}
for(s in 1:max(stripnum)) {
if(verbose) {
if (getRversion() > '2.7.0') setTxtProgressBar(pb, s)
else cat(".")
}
sel = stripnum==s
subX = exprs(channel(XY, "X"))[sel,]
subY = exprs(channel(XY, "Y"))[sel,]
if(useTarget)
tmp = normalize.quantiles.use.target(as.matrix(cbind(subX, subY)), targetdist[[s]])
else
tmp = normalize.quantiles(as.matrix(cbind(subX, subY)))
XY@assayData$X[sel,] = matrix(as.integer(tmp[,1:(ncol(tmp)/2)]+16), nrow(tmp), ncol(tmp)/2)
XY@assayData$Y[sel,] = matrix(as.integer(tmp[,(ncol(tmp)/2+1):ncol(tmp)]+16), nrow(tmp), ncol(tmp)/2)
rm(subX, subY, tmp, sel)
gc()
}
if(verbose)
cat("\n")
XY
}
preprocessInfinium2 = function(XY, mixtureSampleSize=10^5,
fitMixture=TRUE,
eps=0.1,
verbose=TRUE,
seed=1,
cdfName,
sns,
stripNorm=TRUE,
useTarget=TRUE) { #,
# outdir=".") {
# save.it=FALSE,
# snpFile,
# cnFile) {
if(stripNorm)
XY = stripNormalize(XY, useTarget=useTarget, verbose=verbose)
## MR: the code below is mostly straight from snprma.R
if (missing(sns)) sns = sampleNames(XY) #$X
if(missing(cdfName))
cdfName = annotation(XY)
pkgname = getCrlmmAnnotationName(cdfName)
if(!require(pkgname, character.only=TRUE, quietly=!verbose)){
suggCall = paste("library(", pkgname, ", lib.loc='/Altern/Lib/Loc')", sep="")
msg = paste("If", pkgname, "is installed on an alternative location, please load it manually by using", suggCall)
message(strwrap(msg))
stop("Package ", pkgname, " could not be found.")
rm(suggCall, msg)
}
if(verbose) message("Loading snp annotation and mixture model parameters.")
loader("genotypeStuff.rda", .crlmmPkgEnv, pkgname)
loader("mixtureStuff.rda", .crlmmPkgEnv, pkgname)
loader("snpProbesFid.rda", .crlmmPkgEnv, pkgname)
loader("npProbesFid.rda", .crlmmPkgEnv, pkgname)
autosomeIndex = getVarInEnv("autosomeIndex")
SMEDIAN = getVarInEnv("SMEDIAN")
theKnots = getVarInEnv("theKnots")
narrays = ncol(XY)
# if(save.it & !missing(cnFile)) {
# separate out copy number probes
npIndex = getVarInEnv("npProbesFid")
if(length(nprobes)>0) {
nprobes = length(npIndex)
A = matrix(as.integer(exprs(channel(XY, "X"))[npIndex,]), nprobes, narrays)
B = matrix(as.integer(exprs(channel(XY, "Y"))[npIndex,]), nprobes, narrays)
# new lines below - useful to keep track of zeroed out probes
zero = matrix(as.integer(exprs(channel(XY, "zero"))[npIndex,]), nprobes, narrays)
colnames(A) = colnames(B) = colnames(zero) = sns
rownames(A) = rownames(B) = rownames(zero) = names(npIndex)
cnAB = list(A=A, B=B, zero=zero, sns=sns, gns=names(npIndex), cdfName=cdfName)
# t0 <- proc.time()
# save(cnAB, file=cnFile)
# t0 <- proc.time()-t0
# if(verbose) message("Used ", round(t0[3],1), " seconds to save ", cnFile, ".")
rm(A, B, zero)
# }
}
# next process snp probes
snpIndex = getVarInEnv("snpProbesFid")
nprobes = length(snpIndex)
##We will read each cel file, summarize, and run EM one by one
##We will save parameters of EM to use later
mixtureParams = matrix(NA, 4, narrays)
SNR = rep(NA, narrays)
SKW = rep(NA, narrays)
## This is the sample for the fitting of splines
## BC: I like better the idea of the user passing the seed,
## because this might intefere with other analyses
## (like what happened to GCRMA)
set.seed(seed)
idx = sort(sample(autosomeIndex, mixtureSampleSize))
idx2 = sample(nprobes, 10^5)
##S will hold (A+B)/2 and M will hold A-B
##NOTE: We actually dont need to save S. Only for pics etc...
##f is the correction. we save to avoid recomputing
A = matrix(NA, nprobes, narrays)
B = matrix(NA, nprobes, narrays)
zero = matrix(NA, nprobes, narrays)
if(verbose){
message("Calibrating ", narrays, " arrays.")
if (getRversion() > '2.7.0') pb = txtProgressBar(min=0, max=narrays, style=3)
}
for(i in 1:narrays){
A[,i] = as.integer(exprs(channel(XY, "X"))[snpIndex,i])
B[,i] = as.integer(exprs(channel(XY, "Y"))[snpIndex,i])
zero[,i] = as.integer(exprs(channel(XY, "zero"))[snpIndex,i])
SKW[i] = mean((A[idx2,i]-mean(A[idx2,i]))^3)/(sd(A[idx2,i])^3)
if(fitMixture){
S = (log2(A[idx,i])+log2(B[idx,i]))/2 - SMEDIAN
M = log2(A[idx,i])-log2(B[idx,i])
##we need to test the choice of eps.. it is not the max diff between funcs
tmp = fitAffySnpMixture56(S, M, theKnots, eps=eps)
mixtureParams[, i] = tmp[["coef"]]
SNR[i] = tmp[["medF1"]]^2/(tmp[["sigma1"]]^2+tmp[["sigma2"]]^2)
}
if(verbose) {
if (getRversion() > '2.7.0') setTxtProgressBar(pb, i)
else cat(".")
}
}
if (verbose) {
if (getRversion() > '2.7.0') close(pb)
else cat("\n")
}
if (!fitMixture) SNR = mixtureParams = NA
## gns comes from preprocStuff.rda
# if(save.it & !missing(snpFile)) {
# t0 <- proc.time()
# save(res, file=snpFile)
# t0 <- proc.time()-t0
# if(verbose) message("Used ", round(t0[3],1), " seconds to save ", snpFile, ".")
# }
res = list(A=A, B=B,
zero=zero, sns=sns, gns=names(snpIndex), SNR=SNR, SKW=SKW,
mixtureParams=mixtureParams, cdfName=cdfName, cnAB=cnAB)
# open(res[["A"]])
# open(res[["B"]])
# open(res[["zero"]])
# open(res[["SNR"]])
# open(res[["mixtureParams"]])
# if(save.it & !missing(snpFile)) {
# t0 <- proc.time()
# save(res, file=snpFile)
# t0 <- proc.time()-t0
# if(verbose) message("Used ", round(t0[3],1), " seconds to save ", snpFile, ".")
# }
# close(res[["A"]])
# close(res[["B"]])
# close(res[["zero"]])
# close(res[["SNR"]])
# close(res[["mixtureParams"]])
return(res)
}
crlmmIllumina = function(RG, XY, stripNorm=TRUE, useTarget=TRUE,
row.names=TRUE, col.names=TRUE,
probs=c(1/3, 1/3, 1/3), DF=6, SNRMin=5, gender=NULL,
seed=1, # save.it=FALSE, load.it=FALSE, snpFile, cnFile,
mixtureSampleSize=10^5, eps=0.1, verbose=TRUE,
cdfName, sns, recallMin=10, recallRegMin=1000,
returnParams=FALSE, badSNP=.7) {
# if (save.it & (missing(snpFile) | missing(cnFile)))
# stop("'snpFile' and/or 'cnFile' is missing and you chose to save.it")
# if (load.it & missing(snpFile))
# stop("'snpFile' is missing and you chose to load.it")
# if (!missing(snpFile))
# if (load.it & !file.exists(snpFile)){
# load.it <- FALSE
# message("File ", snpFile, " does not exist.")
# stop("Cannot load SNP data.")
# }
# if (!load.it){
if(!missing(RG)) {
if(missing(XY))
XY = RGtoXY(RG, chipType=cdfName)
else
stop("Both RG and XY specified - please use one or the other")
}
if (missing(sns)) sns = sampleNames(XY)
res = preprocessInfinium2(XY, mixtureSampleSize=mixtureSampleSize, fitMixture=TRUE, verbose=verbose,
seed=seed, eps=eps, cdfName=cdfName, sns=sns, stripNorm=stripNorm, useTarget=useTarget) #,
# save.it=save.it, snpFile=snpFile, cnFile=cnFile)
# is.lds = ifelse(isPackageLoaded("ff"), TRUE, FALSE)
is.lds=FALSE
# if(is.lds) {
# open(res[["A"]])
# open(res[["B"]])
# open(res[["SNR"]])
# open(res[["mixtureParams"]])
# }
# fD = featureData(XY)
# phenD = XY@phenoData
# protD = XY@protocolData
# rm(XY)
# gc()
# if(verbose) message("Initializing container for alleleA, alleleB, call, callProbability")
# callSet <- new("SnpSuperSet",
# alleleA=initializeBigMatrix(name="A", nr=nrow(res[[1]]), nc=length(sns)),
# alleleB=initializeBigMatrix(name="B", nr=nrow(res[[1]]), nc=length(sns)),
# call=initializeBigMatrix(name="call", nr=nrow(res[[1]]), nc=length(sns)),
# callProbability=initializeBigMatrix(name="callPr", nr=nrow(res[[1]]), nc=length(sns)),
# annotation=cdfName, protocolData=protD, phenoData=phenD, featureData=fD)
# sampleNames(callSet) <- sns
# featureNames(callSet) <- res[["gns"]]
# pData(callSet)$SKW <- rep(NA, length(sns))
# pData(callSet)$SNR <- rep(NA, length(sns))
# pData(callSet)$gender <- rep(NA, length(sns))
# }else{
# if(verbose) message("Loading ", snpFile, ".")
# obj <- load(snpFile)
# if(verbose) message("Done.")
# if(!any(obj == "res"))
# stop("Object in ", snpFile, " seems to be invalid.")
# }
# rm(phenD, protD , fD)
# snp.index <- res$snpIndex #match(res$gns, featureNames(callSet))
# suppressWarnings(A(callSet) <- res[["A"]])
# suppressWarnings(B(callSet) <- res[["B"]])
# pData(callSet)$SKW <- res$SKW
# pData(callSet)$SNR <- res$SNR
# mixtureParams <- res$mixtureParams
# rm(res); gc()
if(row.names) row.names=res$gns else row.names=NULL
if(col.names) col.names=res$sns else col.names=NULL
FUN = ifelse(is.lds, "crlmmGT2", "crlmmGT")
## genotyping
crlmmGTfxn = function(FUN,...){
switch(FUN,
crlmmGT2=crlmmGT2(...),
crlmmGT=crlmmGT(...))
}
res2 = crlmmGTfxn(FUN,
A=res[["A"]],
B=res[["B"]],
SNR=res[["SNR"]],
mixtureParams=res[["mixtureParams"]],
cdfName=cdfName,
row.names=row.names,
col.names=col.names,
probs=probs,
DF=DF,
SNRMin=SNRMin,
recallMin=recallMin,
recallRegMin=recallRegMin,
gender=gender,
verbose=verbose,
returnParams=returnParams,
badSNP=badSNP)
# res2 <- crlmmGT2(A=res[["A"]], #as.matrix(A(callSet)[snp.index,]), # j]),
# B=res[["B"]], # as.matrix(B(callSet)[snp.index,]), # j]),
# SNR=res[["SNR"]], # callSet$SNR, # [j],
# mixtureParams=res[["mixtureParams"]], #,
# cdfName=res[["cdfName"]], # annotation(callSet),
# row.names=row.names, # featureNames(callSet)[snp.index],
# col.names=col.names, # sampleNames(callSet), #[j],
# probs=probs,
# DF=DF,
# SNRMin=SNRMin,
# recallMin=recallMin,
# recallRegMin=recallRegMin,
# gender=gender,
# verbose=verbose,
# returnParams=returnParams,
# badSNP=badSNP)
# rm(res); gc()
# suppressWarnings(snpCall(callSet)[snp.index, j] <- tmp[["calls"]])
# suppressWarnings(snpCallProbability(callSet)[snp.index, j] <- tmp[["confs"]])
# callSet$gender[j] <- tmp$gender
# suppressWarnings(snpCall(callSet)[snp.index,] <- tmp[["calls"]])
# suppressWarnings(snpCallProbability(callSet)[snp.index,] <- tmp[["confs"]])
# callSet$gender <- tmp$gender
# rm(tmp); gc()
# return(callSet)
res2[["SNR"]] = res[["SNR"]]
res2[["SKW"]] = res[["SKW"]]
# if(is.lds) {
# delete(res[["A"]])
# delete(res[["B"]])
# delete(res[["SNR"]])
# delete(res[["mixtureParams"]])
# }
rm(res); gc()
return(list2SnpSet(res2, returnParams=returnParams))
}
## MR: Below is a more memory efficient version of crlmmIllumina() which
## reads in the .idats and genotypes in the one function and removes objects
## after they have been used
crlmmIlluminaV2 = function(sampleSheet=NULL,
arrayNames=NULL,
ids=NULL,
path=".",
arrayInfoColNames=list(barcode="SentrixBarcode_A", position="SentrixPosition_A"),
highDensity=FALSE,
sep="_",
fileExt=list(green="Grn.idat", red="Red.idat"),
saveDate=FALSE,
stripNorm=TRUE,
useTarget=TRUE,
# outdir=".",
row.names=TRUE,
col.names=TRUE,
probs=c(1/3, 1/3, 1/3), DF=6, SNRMin=5, gender=NULL,
seed=1, # save.it=FALSE, snpFile, cnFile,
mixtureSampleSize=10^5, eps=0.1, verbose=TRUE,
cdfName, sns, recallMin=10, recallRegMin=1000,
returnParams=FALSE, badSNP=.7) {
if(missing(cdfName)) stop("must specify cdfName")
if(!isValidCdfName(cdfName)) stop("cdfName not valid. see validCdfNames")
# is.lds = ifelse(isPackageLoaded("ff"), TRUE, FALSE)
is.lds=FALSE
RG = readIdatFiles(sampleSheet=sampleSheet, arrayNames=arrayNames,
ids=ids, path=path, arrayInfoColNames=arrayInfoColNames,
highDensity=highDensity, sep=sep, fileExt=fileExt, saveDate=saveDate)
XY = RGtoXY(RG, chipType=cdfName)
# if(is.lds) {
# open(RG@assayData$R); open(RG@assayData$G); open(RG@assayData$zero)
# delete(RG@assayData$R); delete(RG@assayData$G); delete(RG@assayData$zero)
# }
rm(RG); gc()
if (missing(sns)) { sns = sampleNames(XY)
}
res = preprocessInfinium2(XY, mixtureSampleSize=mixtureSampleSize, fitMixture=TRUE, verbose=verbose,
seed=seed, eps=eps, cdfName=cdfName, sns=sns, stripNorm=stripNorm, useTarget=useTarget) #, outdir=outdir) #,
# save.it=save.it, snpFile=snpFile, cnFile=cnFile)
# if(is.lds) {
# open(XY@assayData$X); open(XY@assayData$Y); open(XY@assayData$zero)
# delete(XY@assayData$X); delete(XY@assayData$Y); delete(XY@assayData$zero)
# }
rm(XY); gc()
if(row.names) row.names=res$gns else row.names=NULL
if(col.names) col.names=res$sns else col.names=NULL
FUN = ifelse(is.lds, "crlmmGT2", "crlmmGT")
## genotyping
crlmmGTfxn = function(FUN,...){
switch(FUN,
crlmmGT2=crlmmGT2(...),
crlmmGT=crlmmGT(...))
}
res2 = crlmmGTfxn(FUN,
A=res[["A"]],
B=res[["B"]],
SNR=res[["SNR"]],
mixtureParams=res[["mixtureParams"]],
cdfName=cdfName,
row.names=row.names,
col.names=col.names,
probs=probs,
DF=DF,
SNRMin=SNRMin,
recallMin=recallMin,
recallRegMin=recallRegMin,
gender=gender,
verbose=verbose,
returnParams=returnParams,
badSNP=badSNP)
# if(is.lds) {
# open(res[["SNR"]]); open(res[["SKW"]])
# }
res2[["SNR"]] = res[["SNR"]]
res2[["SKW"]] = res[["SKW"]]
# if(is.lds) {
# delete(res[["A"]]); delete(res[["B"]])
# delete(res[["SKW"]]); delete(res[["SNR"]]); delete(res[["mixtureParams"]])
# }
rm(res); gc()
return(list2SnpSet(res2, returnParams=returnParams))
}
# Functions analogous to Rob's Affy functions to set up container
getProtocolData.Illumina = function(filenames, sep="_", fileExt="Grn.idat", verbose=FALSE) {
narrays = length(filenames)
headerInfo = list(nProbes = rep(NA, narrays),
Barcode = rep(NA, narrays),
ChipType = rep(NA, narrays),
Manifest = rep(NA, narrays),
Position = rep(NA, narrays))
scanDates = data.frame(ScanDate=rep(NA, narrays), DecodeDate=rep(NA, narrays))
rownames(scanDates) = gsub(paste(sep, fileExt, sep=""), "", filenames)
## read in the data
for(i in seq_along(filenames)) {
if(verbose)
cat("reading", filenames[i], "\n")
idsG = G = NULL
G = readIDAT(filenames[i])
idsG = rownames(G$Quants)
headerInfo$nProbes[i] = G$nSNPsRead
headerInfo$Barcode[i] = G$Barcode
headerInfo$ChipType[i] = G$ChipType
headerInfo$Manifest[i] = G$Unknown$MostlyNull
headerInfo$Position[i] = G$Unknowns$MostlyA
if(headerInfo$nProbes[i]>(headerInfo$nProbes[1]+10000) || headerInfo$nProbes[i]<(headerInfo$nProbes[1]-10000)) {
warning("Chips are not of the same type. Skipping ", basename(filenames[i]))
next()
}
scanDates$ScanDate[i] = G$RunInfo[1, 1]
scanDates$DecodeDate[i] = G$RunInfo[2, 1]
rm(G)
gc()
}
protocoldata = new("AnnotatedDataFrame",
data=scanDates,
varMetadata=data.frame(labelDescription=colnames(scanDates),
row.names=colnames(scanDates)))
return(protocoldata)
}
construct.Illumina = function(sampleSheet=NULL,
arrayNames=NULL,
ids=NULL,
path=".",
arrayInfoColNames=list(barcode="SentrixBarcode_A", position="SentrixPosition_A"),
highDensity=FALSE,
sep="_",
fileExt=list(green="Grn.idat",
red="Red.idat"),
cdfName,
copynumber=TRUE,
verbose=FALSE, batch, #fns,
saveDate=TRUE) { #, outdir="."){
if(!is.null(arrayNames)) {
pd = new("AnnotatedDataFrame", data = data.frame(Sample_ID=arrayNames))
}
if(!is.null(sampleSheet)) { # get array info from Illumina's sample sheet
if(is.null(arrayNames)) {
if(!is.null(arrayInfoColNames$barcode) && (arrayInfoColNames$barcode %in% colnames(sampleSheet))) {
barcode = sampleSheet[,arrayInfoColNames$barcode]
arrayNames=barcode
}
if(!is.null(arrayInfoColNames$position) && (arrayInfoColNames$position %in% colnames(sampleSheet))) {
position = sampleSheet[,arrayInfoColNames$position]
if(is.null(arrayNames))
arrayNames=position
else
arrayNames = paste(arrayNames, position, sep=sep)
if(highDensity) {
hdExt = list(A="R01C01", B="R01C02", C="R02C01", D="R02C02")
for(i in names(hdExt))
arrayNames = sub(paste(sep, i, sep=""), paste(sep, hdExt[[i]], sep=""), arrayNames)
}
}
}
pd = new("AnnotatedDataFrame", data = sampleSheet)
sampleNames(pd) = basename(arrayNames)
}
if(is.null(arrayNames)) {
arrayNames = gsub(paste(sep, fileExt$green, sep=""), "", dir(pattern=fileExt$green, path=path))
if(!is.null(sampleSheet)) {
sampleSheet=NULL
cat("Could not find required info in \'sampleSheet\' - ignoring. Check \'sampleSheet\' and/or \'arrayInfoColNames\'\n")
}
pd = new("AnnotatedDataFrame", data = data.frame(Sample_ID=arrayNames))
}
narrays = length(arrayNames)
if(!missing(batch)) {
stopifnot(length(batch) == narrays)
}
if(missing(batch)) {
stop("Must specify 'batch'") # batch = as.factor(rep(1, narrays))
}
grnfiles = paste(arrayNames, fileExt$green, sep=sep)
redfiles = paste(arrayNames, fileExt$red, sep=sep)
if(length(grnfiles)==0 || length(redfiles)==0)
stop("Cannot find .idat files")
if(length(grnfiles)!=length(redfiles))
stop("Cannot find matching .idat files")
if(path[1] != "." & path[1] != ""){
grnidats = file.path(path, grnfiles)
redidats = file.path(path, redfiles)
} else {
message("path arg not set. Assuming files are in local directory, or that complete path is provided")
grnidats = grnfiles
redidats = redfiles
}
if(!all(file.exists(grnidats))) stop("Missing some of the *Grn.idat files")
if(!all(file.exists(redidats))) stop("Missing some of the *Red.idat files")
if(verbose) message("Initializing container for genotyping and copy number estimation")
featureData = getFeatureData.Affy(cdfName, copynumber=copynumber)
# if(!missing(fns)){
# index = match(fns, featureNames(featureData))
# if(all(is.na(index))) stop("fns not in featureNames")
# featureData = featureData[index, ]
# }
nr = nrow(featureData); nc = narrays
# ldPath(outdir)
cnSet = new("CNSet",
alleleA=initializeBigMatrix(name="A", nr, nc),
alleleB=initializeBigMatrix(name="B", nr, nc),
call=initializeBigMatrix(name="call", nr, nc),
callProbability=initializeBigMatrix(name="callPr", nr,nc),
annotation=cdfName,
batch=batch)
if (!is.null(sampleSheet) && !is.null(sampleSheet$Sample_ID)) {
sampleNames(cnSet) = sampleSheet$Sample_ID
}
else sampleNames(cnSet) = arrayNames
if(saveDate){
protocolData = getProtocolData.Illumina(grnidats, sep=sep, fileExt=fileExt$green, verbose=verbose)
} else{
protocolData = annotatedDataFrameFrom(A(cnSet), byrow=FALSE)
}
rownames(pData(protocolData)) = sampleNames(cnSet)
protocolData(cnSet) = protocolData
featureData(cnSet) = featureData
featureNames(cnSet) = featureNames(featureData)
pd = data.frame(matrix(NA, nc, 3), row.names=sampleNames(cnSet))
colnames(pd)=c("SKW", "SNR", "gender")
phenoData(cnSet) = new("AnnotatedDataFrame", data=pd)
return(cnSet)
}
genotype.Illumina = function(sampleSheet=NULL,
arrayNames=NULL,
ids=NULL,
path=".",
arrayInfoColNames=list(barcode="SentrixBarcode_A", position="SentrixPosition_A"),
highDensity=FALSE,
sep="_",
fileExt=list(green="Grn.idat", red="Red.idat"),
cdfName,
copynumber=TRUE,
batch,
# outdir=".",
# fns,
saveDate=TRUE,
stripNorm=TRUE,
useTarget=TRUE,
mixtureSampleSize=10^5,
fitMixture=TRUE,
eps=0.1,
verbose=TRUE,
seed=1,
sns,
probs=rep(1/3, 3),
DF=6,
SNRMin=5,
recallMin=10,
recallRegMin=1000,
gender=NULL,
returnParams=TRUE,
badSNP=0.7) {
is.lds = ifelse(isPackageLoaded("ff"), TRUE, FALSE)
if(missing(cdfName)) stop("must specify cdfName")
if(!isValidCdfName(cdfName)) stop("cdfName not valid. see validCdfNames")
pkgname = getCrlmmAnnotationName(cdfName)
# if(missing(outdir))
# stop("Must specify a directory to store large data objects")
callSet = construct.Illumina(sampleSheet=sampleSheet, arrayNames=arrayNames,
ids=ids, path=path, arrayInfoColNames=arrayInfoColNames,
highDensity=highDensity, sep=sep, fileExt=fileExt,
cdfName=cdfName, copynumber=copynumber, verbose=verbose, batch=batch, # fns=fns,
saveDate=saveDate) #, outdir=outdir)
if(missing(sns)) sns = sampleNames(callSet)
if(is.lds) {
open(A(callSet))
open(B(callSet))
# open(callSet)
}
is.snp = isSnp(callSet)
snp.index = which(is.snp)
narrays = ncol(callSet)
if(is.lds) {
sampleBatches = splitIndicesByLength(seq(along=sampleNames(callSet)), ocSamples())
mixtureParams = initializeBigMatrix("crlmmMixt-", 4, narrays, "double")
SNR = initializeBigVector("crlmmSNR-", narrays, "double")
SKW = initializeBigVector("crlmmSKW-", narrays, "double")
ocLapply(sampleBatches, processIDAT, sampleSheet=sampleSheet, arrayNames=arrayNames,
ids=ids, path=path, arrayInfoColNames=arrayInfoColNames, highDensity=highDensity,
sep=sep, fileExt=fileExt, saveDate=saveDate, verbose=verbose, mixtureSampleSize=mixtureSampleSize,
fitMixture=fitMixture, eps=eps, seed=seed, cdfName=cdfName, sns=sns, stripNorm=stripNorm,
useTarget=useTarget, A=A(callSet), B=B(callSet), SKW=SKW, SNR=SNR,
mixtureParams=mixtureParams, is.snp=is.snp, neededPkgs=c("crlmm", pkgname)) # outdir=outdir,
open(SKW)
open(SNR)
pData(callSet)$SKW = SKW
pData(callSet)$SNR = SNR
close(SNR)
close(SKW)
} else {
mixtureParams = matrix(NA, 4, nrow(callSet))
RG = readIdatFiles(sampleSheet=sampleSheet, arrayNames=arrayNames,
ids=ids, path=path, arrayInfoColNames=arrayInfoColNames,
highDensity=highDensity, sep=sep, fileExt=fileExt, saveDate=saveDate)
XY = RGtoXY(RG, chipType=cdfName)
rm(RG); gc()
res = preprocessInfinium2(XY, mixtureSampleSize=mixtureSampleSize, fitMixture=TRUE, verbose=verbose,
seed=seed, eps=eps, cdfName=cdfName, sns=sns, stripNorm=stripNorm, useTarget=useTarget) # , outdir=outdir)
rm(XY); gc()
if(verbose) message("Finished preprocessing.")
np.index = which(!is.snp)
A(callSet)[snp.index, ] = res[["A"]]
B(callSet)[snp.index, ] = res[["B"]]
if(length(np.index)>0) {
A(callSet)[np.index, ] = res[["cnAB"]]$A
B(callSet)[np.index, ] = res[["cnAB"]]$B
}
SKW = pData(callSet)$SKW = res[["SKW"]]
SNR = pData(callSet)$SNR = res[["SNR"]]
mixtureParams = res[["mixtureParams"]]
rm(res)
}
FUN = ifelse(is.lds, "crlmmGT2", "crlmmGT")
## genotyping
crlmmGTfxn = function(FUN,...){
switch(FUN,
crlmmGT2=crlmmGT2(...),
crlmmGT=crlmmGT(...))
}
if(is.lds) {
open(A(callSet))
open(B(callSet))
tmpA = initializeBigMatrix(name="A", length(snp.index), narrays)
tmpB = initializeBigMatrix(name="B", length(snp.index), narrays)
# bb = getOption("ffbatchbytes")
# bb = ocProbesets()*length(sns)*8
ffcolapply(tmpA[,i1:i2] <- A(callSet)[snp.index,i1:i2], X=A(callSet)) #, BATCHBYTES=bb) # X=A(callSet)[snp.index,]
ffcolapply(tmpB[,i1:i2] <- B(callSet)[snp.index,i1:i2], X=B(callSet)) #, BATCHBYTES=bb) # X=B(callSet)[snp.index,]
close(A(callSet))
close(B(callSet))
close(tmpA)
close(tmpB)
} else {
tmpA = A(callSet)[snp.index,]
tmpB = B(callSet)[snp.index,]
}
tmp = crlmmGTfxn(FUN,
A=tmpA,
B=tmpB,
SNR=SNR,
mixtureParams=mixtureParams,
cdfName=cdfName,
row.names=NULL,
col.names=sampleNames(callSet),
probs=probs,
DF=DF,
SNRMin=SNRMin,
recallMin=recallMin,
recallRegMin=recallRegMin,
gender=gender,
verbose=verbose,
returnParams=returnParams,
badSNP=badSNP)
if(verbose) message("Genotyping finished. Updating container with genotype calls and confidence scores.")
if(is.lds){
# bb = getOption("ffbatchbytes") # ocProbesets()*ncol(callSet)*8
open(tmp[["calls"]])
open(tmp[["confs"]])
ffcolapply(snpCall(callSet)[snp.index,i1:i2] <- tmp[["calls"]][,i1:i2], X=tmp[["calls"]]) #, BATCHBYTES=bb)
ffcolapply(snpCallProbability(callSet)[snp.index,i1:i2] <- tmp[["confs"]][,i1:i2], X=tmp[["confs"]]) #, BATCHBYTES=bb)
# close(tmp[["calls"]])
# close(tmp[["confs"]])
open(tmpA); open(tmpB)
delete(tmpA); delete(tmpB);
delete(tmp[["calls"]]); delete(tmp[["confs"]])
rm(tmpA, tmpB)
} else {
calls(callSet)[snp.index, ] = tmp[["calls"]]
snpCallProbability(callSet)[snp.index, ] = tmp[["confs"]]
rm(tmpA, tmpB)
}
callSet$gender = tmp$gender
rm(tmp)
close(callSet)
return(callSet)
}
processIDAT = function(sel, sampleSheet=NULL,
arrayNames=NULL,
ids=NULL,
path=".",
arrayInfoColNames=list(barcode="SentrixBarcode_A", position="SentrixPosition_A"),
highDensity=FALSE,
sep="_",
fileExt=list(green="Grn.idat", red="Red.idat"),
saveDate=FALSE,
verbose=TRUE,
mixtureSampleSize=10^5,
fitMixture=TRUE,
eps=0.1,
seed=1,
cdfName,
sns,
stripNorm=TRUE,
useTarget=TRUE,
A, B, SKW, SNR, mixtureParams, is.snp) { #, outdir=".") {
if(length(path)>= length(sel)) path = path[sel]
RG = readIdatFiles(sampleSheet=sampleSheet[sel,], arrayNames=arrayNames[sel],
ids=ids, path=path, arrayInfoColNames=arrayInfoColNames,
highDensity=highDensity, sep=sep, fileExt=fileExt, saveDate=saveDate, verbose=verbose)
XY = RGtoXY(RG, chipType=cdfName)
# open(RG@assayData$R); open(RG@assayData$G); open(RG@assayData$zero)
# delete(RG@assayData$R); delete(RG@assayData$G); delete(RG@assayData$zero);
rm(RG)
gc()
if (missing(sns) || length(sns)!=ncol(XY)) sns = sampleNames(XY)
res = preprocessInfinium2(XY, mixtureSampleSize=mixtureSampleSize, fitMixture=TRUE, verbose=verbose,
seed=seed, eps=eps, cdfName=cdfName, sns=sns, stripNorm=stripNorm, useTarget=useTarget) #, outdir=outdir)
# save.it=save.it, snpFile=snpFile, cnFile=cnFile)
# open(XY@assayData$X); open(XY@assayData$Y); open(XY@assayData$zero)
# delete(XY@assayData$X); delete(XY@assayData$Y); delete(XY@assayData$zero);
rm(XY)
gc()
if(verbose) message("Finished preprocessing.")
snp.index = which(is.snp)
np.index = which(!is.snp)
# open(res[["A"]])
# open(res[["B"]])
# open(res[["SKW"]])
# open(res[["SNR"]])
# open(res[["mixtureParams"]])
# remove this line: bb = ocProbesets()*length(sns)*8
# Add these
ffcolapply(A[snp.index,sel][,i1:i2] <- res[["A"]][,i1:i2], X=res[["A"]])
ffcolapply(B[snp.index,sel][,i1:i2] <- res[["B"]][,i1:i2], X=res[["B"]])
# ffrowapply(A[snp.index,][i1:i2, sel] <- res[["A"]][i1:i2, ], X=res[["A"]], BATCHBYTES=bb)
# ffrowapply(B[snp.index,][i1:i2, sel] <- res[["B"]][i1:i2, ], X=res[["B"]], BATCHBYTES=bb)
if(length(np.index)>0) {
# for (j in 1:length(sel)) {
ffcolapply(A[np.index,sel][,i1:i2] <- res[["cnAB"]]$A[,i1:i2], X=res[["cnAB"]]$A)
ffcolapply(B[np.index,sel][,i1:i2] <- res[["cnAB"]]$B[,i1:i2], X=res[["cnAB"]]$B)
# A[np.index, sel[j]] = res[["cnAB"]]$A[,j]
# B[np.index, sel[j]] = res[["cnAB"]]$B[,j]
# }
}
# delete(res[["A"]]); delete(res[["B"]])
SKW[sel] = res[["SKW"]][]
SNR[sel] = res[["SNR"]][]
mixtureParams[,sel] = res[["mixtureParams"]][]
close(A)
close(B)
close(SNR)
close(SKW)
close(mixtureParams)
# delete(res[["SKW"]]); delete(res[["SNR"]]); delete(res[["mixtureParams"]])
rm(res)
gc()
TRUE
}
