crlmmGT2 <- function(A, B, SNR, mixtureParams, cdfName, row.names=NULL,
col.names=NULL, probs=c(1/3, 1/3, 1/3), DF=6,
SNRMin=5, recallMin=10, recallRegMin=1000,
gender=NULL, desctrucitve=FALSE, verbose=TRUE,
returnParams=FALSE, badSNP=.7,
callsGt,
callsPr){
pkgname <- getCrlmmAnnotationName(cdfName)
stopifnot(require(pkgname, character.only=TRUE, quietly=!verbose))
open(SNR)
open(A)
open(B)
open(mixtureParams)
## expect objects to be ff
keepIndex <- which( SNR[] > SNRMin)
if(length(keepIndex)==0) stop("No arrays above quality threshold!")
loader("preprocStuff.rda", .crlmmPkgEnv, pkgname)
gns <- getVarInEnv("gns", .crlmmPkgEnv)
if(is.null(rownames(A))){
stopifnot(nrow(A) == length(gns))
index <- seq_len(nrow(A))
} else {
index <- match(gns, rownames(A))
}
snpBatches <- splitIndicesByLength(index, ocProbesets())
NR <- length(unlist(snpBatches))
if(verbose) message("Calling ", NR, " SNPs for recalibration... ")
NC <- ncol(A)
##
if(verbose) message("Loading annotations.")
obj1 <- loader("genotypeStuff.rda", .crlmmPkgEnv, pkgname)
obj2 <- loader("mixtureStuff.rda", .crlmmPkgEnv, pkgname)
##
## this is toget rid of the 'no visible binding' notes
## variable definitions
XIndex <- getVarInEnv("XIndex")
autosomeIndex <- getVarInEnv("autosomeIndex")
YIndex <- getVarInEnv("YIndex")
SMEDIAN <- getVarInEnv("SMEDIAN")
theKnots <- getVarInEnv("theKnots")
regionInfo <- getVarInEnv("regionInfo")
params <- getVarInEnv("params")
rm(list=c(obj1, obj2), envir=.crlmmPkgEnv)
rm(obj1, obj2)
## use lexical scope
imputeGender <- function(XIndex, YIndex){
if(length(YIndex) > 0){
a <- log2(as.matrix(A[XIndex,,drop=FALSE]))
b <- log2(as.matrix(B[XIndex,,drop=FALSE]))
meds.X <- (apply(a+b, 2, median))/2
## Y
a <- log2(as.matrix(A[YIndex,,drop=FALSE]))
b <- log2(as.matrix(B[YIndex,,drop=FALSE]))
meds.Y <- (apply(a+b, 2, median))/2
R <- meds.X - meds.Y
if(sum(SNR[] > SNRMin) == 1){
gender <- ifelse(R[SNR[] > SNRMin] > 0.5, 2L, 1L)
} else{
gender <- kmeans(R, c(min(R[SNR[]>SNRMin]), max(R[SNR[]>SNRMin])))[["cluster"]]
}
} else {
XMedian <- apply(log2(as.matrix(A[XIndex,,drop=FALSE]))+log2(as.matrix(B[XIndex,, drop=FALSE])), 2, median)/2
if(sum(SNR > SNRMin) == 1){
gender <- which.min(c(abs(XMedian-8.9), abs(XMedian-9.5)))
} else{
gender <- kmeans(XMedian, c(min(XMedian[SNR[]>SNRMin]), max(XMedian[SNR[]>SNRMin])))[["cluster"]]
}
}
return(gender)
}
if(is.null(gender)){
if(ocProbesets() < length(XIndex)){
if(verbose) message("Using ", ocProbesets(), " SNPs on chrom X and Y to assign gender.")
XIndex2 <- sample(XIndex, ocProbesets(), replace=FALSE)
} else XIndex2 <- XIndex
if(ocProbesets() < length(YIndex)){
YIndex2 <- sample(YIndex, ocProbesets(), replace=FALSE)
} else YIndex2 <- YIndex
message("Imputing gender")
gender <- imputeGender(XIndex=XIndex2, YIndex=YIndex2)
##cnSet$gender[,] <- gender
}
Indexes <- list(autosomeIndex, XIndex, YIndex)
cIndexes <- list(keepIndex,
keepIndex[which(gender[keepIndex]==2)],
keepIndex[which(gender[keepIndex]==1)])
## call C
fIndex <- which(gender==2)
mIndex <- which(gender==1)
## different here
## use gtypeCallerR in batches
batchSize <- ocProbesets()
open(A)
open(B)
open(mixtureParams)
process1 <- function(idxBatch){
snps <- snpBatches[[idxBatch]]
##rSnps <- range(snps)
last <- (idxBatch-1)*batchSize
IndexesBatch <- list(autosomeIndex[autosomeIndex %in% snps]-last,
XIndex[XIndex %in% snps]-last,
YIndex[YIndex %in% snps]-last)
IndexesBatch <- lapply(IndexesBatch, as.integer)
tmpA <- as.matrix(A[snps,])
tmpB <- as.matrix(B[snps,])
tmp <- gtypeCallerR(tmpA, tmpB, fIndex, mIndex,
params[["centers"]][snps,],
params[["scales"]][snps,],
params[["N"]][snps,],
IndexesBatch, cIndexes,
sapply(IndexesBatch, length),
sapply(cIndexes, length), SMEDIAN,
theKnots, mixtureParams[], DF, probs, 0.025)
tmp
}
## Lexical scope
gc(verbose=FALSE)
newparamsBatch <- ocLapply(seq(along=snpBatches), process1, neededPkgs="crlmm")
gc(verbose=FALSE)
if(verbose) message("finished process1")
newparams <- vector("list", 3)
names(newparams) <- c("centers", "scales", "N")
newparams[["centers"]] <- do.call("rbind", lapply(newparamsBatch, "[[", 1))
newparams[["scales"]] <- do.call("rbind", lapply(newparamsBatch, "[[", 2))
newparams[["N"]] <- do.call("rbind", lapply(newparamsBatch, "[[", 3))
rm(newparamsBatch); gc(verbose=FALSE)
if(verbose) message("Done.")
if(verbose) message("Estimating recalibration parameters.")
d <- newparams[["centers"]] - params$centers
##
##regression
Index <- intersect(which(pmin(newparams[["N"]][, 1],
newparams[["N"]][, 2],
newparams[["N"]][, 3]) > recallMin &
!apply(regionInfo, 1, any)),
autosomeIndex)
if(length(Index) < recallRegMin){
warning("Recalibration not possible. Possible cause: small sample size.")
newparams <- params
dev <- vector("numeric", nrow(newparams[["centers"]]))
SS <- matrix(Inf, 3, 3)
DD <- 0
}else{
data4reg <- as.data.frame(newparams[["centers"]][Index,])
names(data4reg) <- c("AA", "AB", "BB")
regParams <- cbind( coef(lm(AA~AB*BB, data=data4reg)),
c(coef(lm(AB~AA+BB, data=data4reg)), 0),
coef(lm(BB~AA*AB, data=data4reg)))
rownames(regParams) <- c("intercept", "X", "Y", "XY")
rm(data4reg)
##
minN <- 3
newparams[["centers"]][newparams[["N"]] < minN] <- NA
Index <- setdiff(which(rowSums(is.na(newparams[["centers"]]))==1), YIndex)
if(verbose) message("Filling out empty centers", appendLF=FALSE)
for(i in Index){
if(verbose) if(i%%10000==0) message(".", appendLF=FALSE)
mu <- newparams[["centers"]][i, ]
j <- which(is.na(mu))
newparams[["centers"]][i, j] <- c(1, mu[-j], prod(mu[-j]))%*%regParams[, j]
rm(mu, j)
}
##
##remaing NAs are made like originals
if(length(YIndex)>0){
noMoveIndex <- union(setdiff(which(rowSums(is.na(newparams[["centers"]]))>0), YIndex),
YIndex[rowSums(is.na(newparams[["centers"]][YIndex, ])>1)])
}
snps2ignore <- which(rowSums(is.na(newparams[["centers"]])) > 0)
snps2keep <- setdiff(autosomeIndex, snps2ignore)
rm(snps2ignore)
newparams[["centers"]][is.na(newparams[["centers"]])] <- params[["centers"]][is.na(newparams[["centers"]])]
if(verbose) cat("\n")
##
if(verbose) message("Calculating and standardizing size of shift... ", appendLF=FALSE)
GG <- DD <- newparams[["centers"]] - params[["centers"]]
DD <- sweep(DD, 2, colMeans(DD[autosomeIndex, ]))
SS <- cov(DD[autosomeIndex, ])
SSI <- solve(SS)
dev <- vector("numeric", nrow(DD))
if(length(YIndex)){
dev[-YIndex] <- apply(DD[-YIndex, ], 1, function(x) x%*%SSI%*%x)
dev[-YIndex] <- 1/sqrt( (2*pi)^3*det(SS))*exp(-0.5*dev[-YIndex])
##Now Y (only two params)
SSY <- SS[c(1, 3), c(1, 3)]
SSI <- solve(SSY)
dev[YIndex] <- apply(DD[YIndex, c(1, 3)], 1, function(x) x%*%SSI%*%x)
dev[YIndex] <- 1/sqrt( (2*pi)^2*det(SSY))*exp(-0.5*dev[YIndex])
} else {
dev=apply(DD,1,function(x) x%*%SSI%*%x)
dev=1/sqrt( (2*pi)^3*det(SS))*exp(-0.5*dev)
}
gc(verbose=FALSE)
}
if (verbose) message("OK")
##
## BC: must keep SD
params[-2] <- newparams[-2]
rm(newparams)
gc(verbose=FALSE)
##
if(verbose) message("Calling ", NR, " SNPs... ", appendLF=FALSE)
##
## ###################
## ## MOVE TO C#######
##
## running in batches
callsGt.present <- !missing(callsGt)
callsPr.present <- !missing(callsPr)
overwriteAB <- !callsGt.present & !callsPr.present
if(!overwriteAB){
open(callsGt)
open(callsPr)
}
process2 <- function(idxBatch){
snps <- snpBatches[[idxBatch]]
tmpA <- as.matrix(A[snps,])
tmpB <- as.matrix(B[snps,])
rSnps <- range(snps)
last <- (idxBatch-1)*batchSize
IndexesBatch <- list(autosomeIndex[autosomeIndex %in% snps]-last,
XIndex[XIndex %in% snps]-last,
YIndex[YIndex %in% snps]-last)
IndexesBatch <- lapply(IndexesBatch, as.integer)
ImNull <- gtypeCallerR2(tmpA, tmpB, fIndex, mIndex,
params[["centers"]][snps,],
params[["scales"]][snps,],
params[["N"]][snps,],
IndexesBatch, cIndexes,
sapply(IndexesBatch, length),
sapply(cIndexes, length),
SMEDIAN, theKnots, mixtureParams[],
DF, probs, 0.025,
which(regionInfo[snps, 2]),
which(regionInfo[snps, 1]))
if(overwriteAB){
A[snps,] <- tmpA
B[snps,] <- tmpB
} else {
callsGt[snps, ] <- tmpA
callsPr[snps, ] <- tmpB
}
}
gc(verbose=FALSE)
ocLapply(seq(along=snpBatches), process2, neededPkgs="crlmm")
close(A)
close(B)
close(mixtureParams)
if(!overwriteAB){
close(callsGt)
close(callsPr)
}
gc(verbose=FALSE)
message("Done with process2")
## END MOVE TO C#######
## ##################
##
dev <- dev/(dev+1/383)
if(!is.null(row.names)){ rownames(A) <- rownames(B) <- names(dev) <- row.names}
if(!is.null(col.names)){ colnames(A) <- colnames(B) <- col.names}
##
if(length(Index) >= recallRegMin){
tmp4batchQC <- DD[autosomeIndex,]/(params[["N"]][autosomeIndex,]+1)
tmpSnpQc <- dev[autosomeIndex]
SS <- cov(tmp4batchQC[tmpSnpQc < badSNP,])
batchQC <- mean(diag(SS))
}else{
SS <- matrix(0, 3, 3)
batchQC <- Inf
}
##
if(verbose) message("Done.")
if (returnParams){
return(list(calls=A, confs=B, SNPQC=dev, batchQC=batchQC, params=params, DD=DD, covDD=SS, gender=gender, pkgname=pkgname))
}else{
return(list(calls=A, confs=B, SNPQC=dev, batchQC=batchQC, DD=DD, covDD=SS, gender=gender, pkgname=pkgname))
}
}
