crlmm <- function(filenames, row.names=TRUE, col.names=TRUE,
probs=c(1/3, 1/3, 1/3), DF=6, SNRMin=5, gender=NULL,
save.it=FALSE, load.it=FALSE, intensityFile,
mixtureSampleSize=10^5, eps=0.1, verbose=TRUE,
cdfName, sns, recallMin=10, recallRegMin=1000,
returnParams=FALSE, badSNP=.7){
if ((load.it | save.it) & missing(intensityFile))
stop("'intensityFile' is missing, and you chose either load.it or save.it")
if (missing(sns)) sns <- basename(filenames)
if (!missing(intensityFile))
if (load.it & !file.exists(intensityFile)){
load.it <- FALSE
message("File ", intensityFile, " does not exist.")
message("Not loading it, but running SNPRMA from scratch.")
}
if (!load.it){
res <- snprma(filenames, fitMixture=TRUE,
mixtureSampleSize=mixtureSampleSize, verbose=verbose,
eps=eps, cdfName=cdfName, sns=sns)
if(save.it){
t0 <- proc.time()
save(res, file=intensityFile)
t0 <- proc.time()-t0
if (verbose) message("Used ", t0[3], " seconds to save ", intensityFile, ".")
}
}else{
if (verbose) message("Loading ", intensityFile, ".")
obj <- load(intensityFile)
if (verbose) message("Done.")
if (obj != "res")
stop("Object in ", intensityFile, " seems to be invalid.")
}
if(row.names) row.names=res$gns else row.names=NULL
if(col.names) col.names=res$sns else col.names=NULL
res2 <- crlmmGT(res[["A"]], res[["B"]], res[["SNR"]],
res[["mixtureParams"]], res[["cdfName"]],
gender=gender, row.names=row.names,
col.names=col.names, recallMin=recallMin,
recallRegMin=1000, SNRMin=SNRMin,
returnParams=returnParams, badSNP=badSNP,
verbose=verbose)
res2[["SNR"]] <- res[["SNR"]]
res2[["SKW"]] <- res[["SKW"]]
return(list2SnpSet(res2, returnParams=returnParams))
}
crlmmGT <- 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){
keepIndex <- which(SNR>SNRMin)
if(length(keepIndex)==0) stop("No arrays above quality threshold!")
NC <- ncol(A)
NR <- nrow(A)
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 annotations.")
loader("genotypeStuff.rda", .crlmmPkgEnv, pkgname)
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")
##IF gender not provide, we predict
if(is.null(gender)){
if(verbose) message("Determining gender.")
XMedian <- apply(log2(A[XIndex,, drop=FALSE])+log2(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"]]
}
}
Indexes <- list(autosomeIndex, XIndex, YIndex)
cIndexes <- list(keepIndex,
keepIndex[which(gender[keepIndex]==2)],
keepIndex[which(gender[keepIndex]==1)])
if(verbose) cat("Calling", NR, "SNPs for recalibration... ")
## call C
fIndex <- which(gender==2)
mIndex <- which(gender==1)
newparams <- gtypeCallerR(A, B, fIndex, mIndex,
params[["centers"]], params[["scales"]], params[["N"]],
Indexes, cIndexes,
sapply(Indexes, length), sapply(cIndexes, length),
SMEDIAN, theKnots,
mixtureParams, DF, probs, 0.025)
gc(verbose=FALSE)
names(newparams) <- c("centers", "scales", "N")
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) cat("Filling out empty centers")
for(i in Index){
if(verbose) if(i%%10000==0)cat(".")
mu <- newparams[["centers"]][i, ]
j <- which(is.na(mu))
newparams[["centers"]][i, j] <- c(1, mu[-j], prod(mu[-j]))%*%regParams[, 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.")
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)
}
}
## BC: must keep SD
params[-2] <- newparams[-2]
rm(newparams);gc(verbose=FALSE)
if(verbose) cat("Calling", NR, "SNPs... ")
## ###################
## ## MOVE TO C#######
ImNull <- gtypeCallerR2(A, B, fIndex, mIndex, params[["centers"]],
params[["scales"]], params[["N"]], Indexes,
cIndexes, sapply(Indexes, length),
sapply(cIndexes, length), SMEDIAN, theKnots,
mixtureParams, DF, probs, 0.025,
which(regionInfo[,2]),
which(regionInfo[,1]))
gc(verbose=FALSE)
## 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))
}
}
gtypeCallerR <- function(A, B, fIndex, mIndex, theCenters, theScales,
theNs, Indexes, cIndexes, nIndexes,
ncIndexes, SMEDIAN, knots, params, dft,
probs, trim){
stopifnot(!missing(A), !missing(B), dim(A)==dim(B),
nrow(A)==nrow(theCenters), nrow(A)==nrow(theScales),
nrow(A) == nrow(theNs), length(knots)==3, nrow(params)==4,
ncol(params)==ncol(A), length(trim)==1, length(probs)==3)
## make code robust
## check types before passing to C
.Call("gtypeCallerPart1", A, B, as.integer(fIndex),
as.integer(mIndex), as.numeric(theCenters),
as.numeric(theScales), as.integer(theNs),
lapply(Indexes, as.integer), lapply(cIndexes, as.integer),
as.integer(nIndexes), as.integer(ncIndexes),
as.numeric(SMEDIAN), as.numeric(knots),
as.numeric(params), as.integer(dft), as.numeric(probs),
as.numeric(trim), PACKAGE="crlmm")
}
gtypeCallerR2 <- function(A, B, fIndex, mIndex, theCenters, theScales,
theNs, Indexes, cIndexes, nIndexes,
ncIndexes, SMEDIAN, knots, params, dft,
probs, trim, noTraining, noInfo){
stopifnot(!missing(A), !missing(B), dim(A)==dim(B),
nrow(A)==nrow(theCenters), nrow(A)==nrow(theScales),
nrow(A) == nrow(theNs), length(knots)==3, nrow(params)==4,
ncol(params)==ncol(A), length(trim)==1, length(probs)==3)
.Call("gtypeCallerPart2", A, B,
as.integer(fIndex), as.integer(mIndex),
as.numeric(theCenters), as.numeric(theScales),
as.integer(theNs), Indexes, cIndexes, nIndexes, ncIndexes,
as.numeric(SMEDIAN), as.numeric(knots), as.numeric(params),
as.integer(dft), as.numeric(probs), as.numeric(trim),
as.integer(noTraining), as.integer(noInfo), PACKAGE="crlmm")
}
### parallel version
crlmm2 <- function(filenames, row.names=TRUE, col.names=TRUE,
probs=c(1/3, 1/3, 1/3), DF=6, SNRMin=5, gender=NULL,
save.it=FALSE, load.it=FALSE, intensityFile,
mixtureSampleSize=10^5, eps=0.1, verbose=TRUE,
cdfName, sns, recallMin=10, recallRegMin=1000,
returnParams=FALSE, badSNP=.7){
if ((load.it || save.it) && missing(intensityFile))
stop("'intensityFile' is missing, and you chose either load.it or save.it")
if (missing(sns)) sns <- basename(filenames)
if (!missing(intensityFile))
if (load.it & !file.exists(intensityFile)){
load.it <- FALSE
message("File ", intensityFile, " does not exist.")
message("Not loading it, but running SNPRMA from scratch.")
}
if (!load.it){
res <- snprma2(filenames, fitMixture=TRUE,
mixtureSampleSize=mixtureSampleSize, verbose=verbose,
eps=eps, cdfName=cdfName, sns=sns)
open(res[["A"]])
open(res[["B"]])
open(res[["SNR"]])
open(res[["mixtureParams"]])
if(save.it){
t0 <- proc.time()
save(res, file=intensityFile)
t0 <- proc.time()-t0
if (verbose) message("Used ", t0[3], " seconds to save ", intensityFile, ".")
}
}else{
if (verbose) message("Loading ", intensityFile, ".")
obj <- load(intensityFile)
if (verbose) message("Done.")
if (obj != "res")
stop("Object in ", intensityFile, " seems to be invalid.")
}
if(row.names) row.names=res$gns else row.names=NULL
if(col.names) col.names=res$sns else col.names=NULL
res2 <- crlmmGT2(res[["A"]], res[["B"]], res[["SNR"]],
res[["mixtureParams"]], res[["cdfName"]],
gender=gender, row.names=row.names,
col.names=col.names, recallMin=recallMin,
recallRegMin=1000, SNRMin=SNRMin,
returnParams=returnParams, badSNP=badSNP,
verbose=verbose)
res2[["SNR"]] <- res[["SNR"]]
res2[["SKW"]] <- res[["SKW"]]
return(list2SnpSet(res2, returnParams=returnParams))
}
predictGender <- function(cols, theA, theB, XIndex){
n <- length(cols)
med <- numeric(n)
if (n > 0){
open(theA)
open(theB)
for (i in 1:n){
vA <- log2(theA[XIndex, cols[i]])
vB <- log2(theB[XIndex, cols[i]])
med[i] <- median(vA+vB)/2
rm(vA, vB)
}
close(theA)
close(theB)
rm(theA, theB)
}
rm(n)
gc(verbose=FALSE)
med
}
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){
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!")
NC <- ncol(A)
NR <- nrow(A)
pkgname <- getCrlmmAnnotationName(cdfName)
stopifnot(require(pkgname, character.only=TRUE, quietly=!verbose))
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)
## IF gender not provide, we predict
## FIXME: XIndex may be greater than ocProbesets()
if(is.null(gender)){
if(verbose) message("Determining gender.")
## XMedian <- apply(log2(A[XIndex,, drop=FALSE])+log2(B[XIndex,, drop=FALSE]), 2, median)/2
XMedian <- ocLapply(splitIndicesByNode(1:NC), predictGender, theA=A, theB=B, XIndex=XIndex, neededPkgs="crlmm")
XMedian <- unlist(XMedian)
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"]]
}
}
Indexes <- list(autosomeIndex, XIndex, YIndex)
cIndexes <- list(keepIndex,
keepIndex[which(gender[keepIndex]==2)],
keepIndex[which(gender[keepIndex]==1)])
if(verbose) cat("Calling", NR, "SNPs for recalibration... ")
## call C
fIndex <- which(gender==2)
mIndex <- which(gender==1)
## different here
## use gtypeCallerR in batches
snpBatches <- splitIndicesByLength(1:nrow(A), ocProbesets())
newparamsBatch <- vector("list", length(snpBatches))
process1 <- function(idxBatch, snpBatches, autosomeIndex, XIndex,
YIndex, A, B, mixtureParams, fIndex, mIndex,
params, cIndexes, SMEDIAN, theKnots, DF, probs, batchSize){
open(A)
open(B)
open(mixtureParams)
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,])
## newparamsBatch[[idxBatch]]
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)
rm(snps, rSnps, IndexesBatch, tmpA, tmpB, last)
gc(verbose=FALSE)
close(A)
close(B)
close(mixtureParams)
tmp
}
newparamsBatch <- ocLapply(seq(along=snpBatches), process1,
snpBatches=snpBatches,
autosomeIndex=autosomeIndex, XIndex=XIndex,
YIndex=YIndex, A=A, B=B,
mixtureParams=mixtureParams, fIndex=fIndex,
mIndex=mIndex, params=params,
cIndexes=cIndexes, SMEDIAN=SMEDIAN,
theKnots=theKnots, DF=DF, probs=probs,
batchSize=ocProbesets())
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)
}
}
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
process2 <- function(idxBatch, snpBatches, autosomeIndex, XIndex,
YIndex, A, B, mixtureParams, fIndex, mIndex,
params, cIndexes, SMEDIAN, theKnots, DF, probs,
regionInfo, batchSize){
open(A)
open(B)
open(mixtureParams)
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]))
A[snps,] <- tmpA
B[snps,] <- tmpB
rm(tmpA, tmpB, snps, rSnps, IndexesBatch, ImNull, last)
gc(verbose=FALSE)
close(A)
close(B)
close(mixtureParams)
}
ocLapply(seq(along=snpBatches), process2, snpBatches=snpBatches,
autosomeIndex=autosomeIndex, XIndex=XIndex, YIndex=YIndex,
A=A, B=B, mixtureParams=mixtureParams, fIndex=fIndex,
mIndex=mIndex, params=params, cIndexes=cIndexes,
SMEDIAN=SMEDIAN, theKnots=theKnots, DF=DF, probs=probs,
regionInfo=regionInfo, batchSize=ocProbesets())
## 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))
}
}
