@@ -28,7 +28,7 @@ getFeatureData.Affy <- function(cdfName, copynumber=FALSE){

 		load(file.path(path, "cnProbes.rda"))

 		cnProbes <- get("cnProbes")

 		snpIndex <- seq(along=gns)

-		npIndex <- seq(along=rownames(cnProbes)) + max(snpIndex) 

+		npIndex <- seq(along=rownames(cnProbes)) + max(snpIndex)

 		featurenames <- c(gns, rownames(cnProbes))

 	} else featurenames <- gns

 	fvarlabels=c("chromosome", "position", "isSnp")

@@ -84,7 +84,7 @@ construct <- function(filenames, cdfName, copynumber=FALSE,

 	colnames(CA) <- sns

 	CB=initializeBigMatrix(name="CB", nr, nc)

 	colnames(CB) <- sns

-	callSet <- new("CNSetLM", 

+	callSet <- new("CNSetLM",

 		       alleleA=alleleA,

 		       alleleB=alleleB,

 		       call=call,

@@ -157,7 +157,7 @@ genotype <- function(filenames,

 			np.index <- which(isSnp(callSet) == 0)

 			cnrmaRes <- cnrma(filenames=filenames[j],

 					  cdfName=cdfName,

-					  row.names=featureNames(callSet)[np.index],				  

+					  row.names=featureNames(callSet)[np.index],

 					  sns=sns[j],

 					  seed=seed,

 					  verbose=verbose)

@@ -459,7 +459,7 @@ getPhenoData <- function(sampleSheet=NULL, arrayNames=NULL,

 				barcode = sampleSheet[,arrayInfoColNames$barcode]

 				arrayNames=barcode

 			}

-			if(!is.null(arrayInfoColNames$position) && (arrayInfoColNames$position %in% colnames(sampleSheet))) {  

+			if(!is.null(arrayInfoColNames$position) && (arrayInfoColNames$position %in% colnames(sampleSheet))) {

 				position = sampleSheet[,arrayInfoColNames$position]

 				if(is.null(arrayNames))

 					arrayNames=position

@@ -473,7 +473,7 @@ getPhenoData <- function(sampleSheet=NULL, arrayNames=NULL,

 			}

 		}

 		pd = new("AnnotatedDataFrame", data = sampleSheet)

-		sampleNames(pd) <- basename(arrayNames)               

+		sampleNames(pd) <- basename(arrayNames)

 	}

 	if(is.null(arrayNames)) {

 		arrayNames = gsub(paste(sep, fileExt$green, sep=""), "", dir(pattern=fileExt$green, path=path))

@@ -528,7 +528,7 @@ crlmmIlluminaRS <- function(sampleSheet=NULL,

 			    fileExt=list(green="Grn.idat", red="Red.idat"),

 			    stripNorm=TRUE,

 			    useTarget=TRUE,

-			    row.names=TRUE, 

+			    row.names=TRUE,

 			    col.names=TRUE,

 			    probs=c(1/3, 1/3, 1/3), DF=6, SNRMin=5, gender=NULL,

 			    seed=1, save.ab=FALSE, snpFile, cnFile,

@@ -545,7 +545,7 @@ crlmmIlluminaRS <- function(sampleSheet=NULL,

 	} else {

 		if(length(batch) != length(sns))

 			stop("batch variable must be the same length as the filenames")

-	}	

+	}

 	batches <- splitIndicesByLength(seq(along=arrayNames), ocSamples())

 	k <- 1

 	for(j in batches){

@@ -618,7 +618,7 @@ crlmmIlluminaRS <- function(sampleSheet=NULL,

 			protocolData(callSet)$batch[j] <- as.numeric(as.factor(protocolData$ScanDate))

 		}

 		## MR: we need to define a snp.index vs np.index

-		snp.index <- match(res$gns, featureNames(callSet))		

+		snp.index <- match(res$gns, featureNames(callSet))

 		A(callSet)[snp.index, j] <- res[["A"]]

 		B(callSet)[snp.index, j] <- res[["B"]]

 		pData(callSet)$SKW[j] <- res$SKW

@@ -733,7 +733,7 @@ generateIXTX <- function(x, nrow=3) {

 }

 nuphiAllele2 <- function(allele, Ystar, W, Ns){

-	complete <- rowSums(is.na(W)) == 0 

+	complete <- rowSums(is.na(W)) == 0

 	if(any(!is.finite(W))){## | any(!is.finite(V))){

 		i <- which(rowSums(!is.finite(W)) > 0)

 		stop("Possible zeros in the within-genotype estimates of the spread (vA, vB). ")

@@ -741,7 +741,7 @@ nuphiAllele2 <- function(allele, Ystar, W, Ns){

 	NOHET <- mean(Ns[, 2], na.rm=TRUE) < 0.05

 	if(missing(allele)) stop("must specify allele")

 	if(allele == "A") X <- cbind(1, 2:0) else X <- cbind(1, 0:2)

-	if(NOHET) X <- X[-2, ] ##more than 1 X chromosome, but all homozygous		

+	if(NOHET) X <- X[-2, ] ##more than 1 X chromosome, but all homozygous

 	##How to quickly generate Xstar, Xstar = diag(W) %*% X

 	Xstar <- apply(W, 1, generateX, X)

 	IXTX <- apply(Xstar, 2, generateIXTX, nrow=nrow(X))

@@ -758,7 +758,7 @@ nuphiAllele2 <- function(allele, Ystar, W, Ns){

 }

 nuphiAlleleX <- function(allele, Ystar, W, Ns, chrX=FALSE){

-	complete <- rowSums(is.na(W)) == 0 

+	complete <- rowSums(is.na(W)) == 0

 	if(any(!is.finite(W))){## | any(!is.finite(V))){

 		i <- which(rowSums(!is.finite(W)) > 0)

 		stop("Possible zeros in the within-genotype estimates of the spread (vA, vB). ")

@@ -766,14 +766,14 @@ nuphiAlleleX <- function(allele, Ystar, W, Ns, chrX=FALSE){

 	##NOHET <- mean(Ns[, 2], na.rm=TRUE) < 0.05

 	if(missing(allele)) stop("must specify allele")

 	if(allele == "A") X <- cbind(1, c(1, 0, 2, 1, 0), c(0, 1, 0, 1, 2))

-	if(allele == "B") X <- cbind(1, c(0, 1, 0, 1, 2), c(1, 0, 2, 1, 0))	

-	##if(NOHET) X <- X[-2, ] ##more than 1 X chromosome, but all homozygous		

+	if(allele == "B") X <- cbind(1, c(0, 1, 0, 1, 2), c(1, 0, 2, 1, 0))

+	##if(NOHET) X <- X[-2, ] ##more than 1 X chromosome, but all homozygous

 	##How to quickly generate Xstar, Xstar = diag(W) %*% X

 	Xstar <- apply(W, 1, generateX, X)

 	IXTX <- apply(Xstar, 2, generateIXTX, nrow=nrow(X))

 	betahat <- matrix(NA, 3, nrow(Ystar))

-	ses <- matrix(NA, 3, nrow(Ystar))			

+	ses <- matrix(NA, 3, nrow(Ystar))

 	##betahat <- matrix(NA, 2, nrow(Ystar))

 	##ses <- matrix(NA, 2, nrow(Ystar))

 	for(i in 1:nrow(Ystar)){

@@ -798,10 +798,10 @@ nuphiAllele <- function(object, allele, Ystar, W, tmp.objects, cnOptions){

 		i <- which(rowSums(!is.finite(W)) > 0)

 		##browser()

 		stop("Possible zeros in the within-genotype estimates of the spread (vA, vB). ")

-	}	

-	Ns <- tmp.objects[["Ns"]]	

+	}

+	Ns <- tmp.objects[["Ns"]]

 	Ns <- Ns[I, ]

-	

+

 	CHR <- unique(chromosome(object))

 	##batch <- unique(object$batch)

 	batch <- unique(batch(object))

@@ -812,10 +812,10 @@ nuphiAllele <- function(object, allele, Ystar, W, tmp.objects, cnOptions){

 	phiA <- getParam(object, "phiA", batch)

 	phiB <- getParam(object, "phiB", batch)

 	phiA.se <- getParam(object, "phiA.se", batch)

-	phiB.se <- getParam(object, "phiB.se", batch)	

+	phiB.se <- getParam(object, "phiB.se", batch)

 	if(CHR==23){

 		phiAX <- getParam(object, "phiAX", batch)

-		phiBX <- getParam(object, "phiBX", batch)		

+		phiBX <- getParam(object, "phiBX", batch)

 	}

 	NOHET <- mean(Ns[, "AB"], na.rm=TRUE) < 0.05

 	if(missing(allele)) stop("must specify allele")

@@ -830,7 +830,7 @@ nuphiAllele <- function(object, allele, Ystar, W, tmp.objects, cnOptions){

 		}

 	} else {##autosome

 		if(allele == "A") X <- cbind(1, 2:0) else X <- cbind(1, 0:2)

-		if(NOHET) X <- X[-2, ] ##more than 1 X chromosome, but all homozygous		

+		if(NOHET) X <- X[-2, ] ##more than 1 X chromosome, but all homozygous

 	}

 	##How to quickly generate Xstar, Xstar = diag(W) %*% X

 	Xstar <- apply(W, 1, generateX, X)

@@ -838,7 +838,7 @@ nuphiAllele <- function(object, allele, Ystar, W, tmp.objects, cnOptions){

 	##as.numeric(diag(W[1, ]) %*% X)

 	if(CHR == 23){

 		betahat <- matrix(NA, 3, nrow(Ystar))

-		ses <- matrix(NA, 3, nrow(Ystar))		

+		ses <- matrix(NA, 3, nrow(Ystar))

 	} else{

 		betahat <- matrix(NA, 2, nrow(Ystar))

 		ses <- matrix(NA, 2, nrow(Ystar))

@@ -859,7 +859,7 @@ nuphiAllele <- function(object, allele, Ystar, W, tmp.objects, cnOptions){

 		object <- pr(object, "phiA.se", batch, phiA.se)

 		if(CHR == 23){

 			phiAX[complete] <- betahat[3, ]

-			object <- pr(object, "phiAX", batch, phiAX)			

+			object <- pr(object, "phiAX", batch, phiAX)

 		}

 	}

 	if(allele=="B"){

@@ -869,7 +869,7 @@ nuphiAllele <- function(object, allele, Ystar, W, tmp.objects, cnOptions){

 		phiB.se[complete] <- ses[2, ]

 		object <- pr(object, "nuB", batch, nuB)

 		object <- pr(object, "nuB.se", batch, nuB.se)

-		object <- pr(object, "phiB", batch, phiB)		

+		object <- pr(object, "phiB", batch, phiB)

 		object <- pr(object, "phiB.se", batch, phiB.se)

 		if(CHR == 23){

 			phiBX[complete] <- betahat[3, ]

@@ -893,7 +893,7 @@ predictGender <- function(res, cdfName="genomewidesnp6", SNRMin=5){

         loader("23index.rda", .crlmmPkgEnv, pkgname)

 	index <- getVarInEnv("index")

 	##load(file.path(path, "23index.rda"))

-	XIndex <- index[[1]]	

+	XIndex <- index[[1]]

 	if(class(res) != "ABset"){

 		A <- res$A

 		B <- res$B

@@ -1013,7 +1013,7 @@ crlmmCopynumber <- function(object,

 					column <- match(batchName, colnames(lM(object)[[k]]))

 					lM(object)[[k]][row.index, column] <- fData(tmp)[, k]

 				}

-			}			

+			}

 			rm(tmp); gc()

 			ii <- ii+1

 		}

@@ -1053,7 +1053,7 @@ crlmmCopynumber2 <- function(object,

 	##autosomeIndex.snps <- (1:nrow(object))[chromosome(object) < 23 & isSnp(object) & !is.na(chromosome(object))]

 	autosomeIndex.snps <- which(chromosome(object) < 23 & isSnp(object) & !is.na(chromosome(object)))

 	autosomeIndex.nps <- which(chromosome(object) < 23 & !isSnp(object) & !is.na(chromosome(object)))

-	##autosomeIndex.nps <- (1:nrow(object))[chromosome(object) < 23 & !isSnp(object) & !is.na(chromosome(object))]	

+	##autosomeIndex.nps <- (1:nrow(object))[chromosome(object) < 23 & !isSnp(object) & !is.na(chromosome(object))]

 	## Do chromosome X in batches

 	Ns <- initializeBigMatrix("Ns", nrow(object), 5)

@@ -1069,8 +1069,8 @@ crlmmCopynumber2 <- function(object,

 		save(snpflags, file=file.path(ldPath(), "snpflags.rda"))

 	} else{

 		load(file.path(ldPath(), "snpflags.rda"))

-				

-	} 

+

+	}

 	if(verbose) message("Estimating allele-specific copy number at autosomal SNPs")

 	snpBatches <- splitIndicesByLength(autosomeIndex.snps, ocProbesets())

 	ocLapply(seq(along=snpBatches),

@@ -1181,7 +1181,7 @@ fit.lm1 <- function(idxBatch,

 	snps <- snpBatches[[idxBatch]]

 	batches <- split(seq(along=batch(object)), batch(object))

 	batches <- batches[sapply(batches, length) >= MIN.SAMPLES]

-	

+

 	open(object)

 	open(snpflags)

 	open(normal)

@@ -1312,14 +1312,14 @@ fit.lm1 <- function(idxBatch,

 	cB <- matrix(as.integer(cB*100), nrow(cB), ncol(cB))

-	

+

 	CA(object)[snps, ] <- cA

 	CB(object)[snps, ] <- cB

-	

+

 	snpflags[snps, ] <- flags

 	lapply(lM(object), open)

-	

+

 	tmp <- physical(lM(object))$tau2A

 	tmp[snps, ] <- tau2A

 	lM(object)$tau2A <- tmp

@@ -1356,7 +1356,7 @@ fit.lm1 <- function(idxBatch,

 	tmp <- physical(lM(object))$corrBB

 	tmp[snps, ] <- corrBB

 	lM(object)$corrAB <- tmp

-	

+

 	lapply(assayData(object), close)

 	lapply(lM(object), close)

 	TRUE

@@ -1384,15 +1384,15 @@ fit.lm2 <- function(idxBatch,

 	snps <- snpBatches[[idxBatch]]

 	batches <- split(seq(along=batch(object)), batch(object))

 	batches <- batches[sapply(batches, length) >= MIN.SAMPLES]

-	

+

 	open(object)

 	open(snpflags)

 	open(normal)

-	

+

 	cA <- matrix(NA, length(snps), ncol(object))

 	ii <- isSnp(object) & chromosome(object) < 23 & !is.na(chromosome(object))

-	flags <- as.matrix(snpflags[,])  

+	flags <- as.matrix(snpflags[,])

 	noflags <- rowSums(flags, na.rm=TRUE) == 0  ##NA's for unevaluated batches

 	## We do not want to write to discuss for each batch.  More efficient to

 	## write to disk after estimating these parameters for all batches.

@@ -1501,7 +1501,7 @@ fit.lm3 <- function(idxBatch,

 		snps <- snpBatches[[idxBatch]]

 	batches <- split(seq(along=batch(object)), batch(object))

 	batches <- batches[sapply(batches, length) >= MIN.SAMPLES]

-	

+

 	open(snpflags)

 	open(normal)

 	open(object)

@@ -1519,7 +1519,7 @@ fit.lm3 <- function(idxBatch,

 	AA <- as.matrix(A(object)[snps, ])

 	BB <- as.matrix(B(object)[snps, ])

 	for(k in batches){

-		##if(verbose) message("SNP batch ", ii, " of ", length(batches)) 

+		##if(verbose) message("SNP batch ", ii, " of ", length(batches))

 		## within-genotype moments

 		gender <- object$gender[k]

 		G <- GG[, k]

@@ -1540,7 +1540,7 @@ fit.lm3 <- function(idxBatch,

 		vA.F <- applyByGenotype(A[, gender==2], rowMAD, G[, gender==2])

 		vB.F <- applyByGenotype(B[, gender==2], rowMAD, G[, gender==2])

 		vA.M <- applyByGenotype(A[, gender==1], rowMAD, G[, gender==1])

-		vB.M <- applyByGenotype(B[, gender==1], rowMAD, G[, gender==1])		

+		vB.M <- applyByGenotype(B[, gender==1], rowMAD, G[, gender==1])

 		vA.F <- shrink(vA.F, Ns.F, DF.PRIOR)

 		vA.M <- shrink(vA.M, Ns.M, DF.PRIOR)

 		vB.F <- shrink(vB.F, Ns.F, DF.PRIOR)

@@ -1583,7 +1583,7 @@ fit.lm3 <- function(idxBatch,

 		notMissing <- !(is.na(muA.M[, 1]) | is.na(muA.M[, 3]) | is.na(muB.M[, 1]) | is.na(muB.M[, 3]))

 		complete <- list()

 		complete[[1]] <- which(correct.orderA & correct.orderB & nobsA & notMissing) ##be selective here

-		complete[[2]] <- which(correct.orderA & correct.orderB & nobsB & notMissing) ##be selective here	

+		complete[[2]] <- which(correct.orderA & correct.orderB & nobsB & notMissing) ##be selective here

 		size <- min(5000, length(complete[[1]]))

 		if(size > 5000) complete <- lapply(complete, function(x) sample(x, size))

 		##

@@ -1658,7 +1658,7 @@ fit.lm3 <- function(idxBatch,

 	cB[cB < 0.05] <- 0.05

 	cA[cA > 5] <-  5

 	cB[cB > 5] <- 5

-	

+

 	##--------------------------------------------------

 	##RS: need to fix.  why are there NA's by coercion

 	cA <- matrix(as.integer(cA*100), nrow(cA), ncol(cA))

@@ -1702,7 +1702,7 @@ fit.lm3 <- function(idxBatch,

 	tmp <- physical(lM(object))$phiPrimeB

 	tmp[snps, ] <- phiB2

 	lM(object)$phiPrimeB <- tmp

-	

+

 	tmp <- physical(lM(object))$corrAB

 	tmp[snps, ] <- corrAB

 	lM(object)$corrAB <- tmp

@@ -1711,7 +1711,7 @@ fit.lm3 <- function(idxBatch,

 	lM(object)$corrAA <- tmp

 	tmp <- physical(lM(object))$corrBB

 	tmp[snps, ] <- corrBB

-	lM(object)$corrAB <- tmp	

+	lM(object)$corrAB <- tmp

 ##	lM(object)$tau2A[snps, ] <- tau2A

 ##	lM(object)$tau2B[snps, ] <- tau2B

 ##	lM(object)$sig2A[snps, ] <- sig2A

@@ -1744,7 +1744,7 @@ fit.lm4 <- function(idxBatch,

 		    MIN.NU,

 		    MIN.PHI,

 		    verbose, ...){

-	if(verbose) message("Probe batch ", idxBatch, " of ", length(snpBatches))	

+	if(verbose) message("Probe batch ", idxBatch, " of ", length(snpBatches))

 	open(object)

 	open(normal)

 	open(snpflags)

@@ -1766,7 +1766,7 @@ fit.lm4 <- function(idxBatch,

 	i2 <- rowSums(nuIB < 20, na.rm=TRUE) == 0

 	i3 <- rowSums(phiIA < 20, na.rm=TRUE) == 0

 	i4 <- rowSums(phiIB < 20, na.rm=TRUE) == 0

-	

+

 	snp.index <- which(i1 & i2 & i3 & i4 & noflags)

 	if(length(snp.index) == 0){

 		warning("No snps meet the following criteria: (1) nu and phi > 20 and (2) at least MIN.OBS in each genotype cluster. CN not estimated for nonpolymorphic loci on X")

@@ -1793,7 +1793,7 @@ fit.lm4 <- function(idxBatch,

 	##if(missing(which.batches)) which.batches <- seq(along=batches)

 	##batches <- batches[which.batches]

 	for(k in batches){

-		##if(verbose) message("SNP batch ", ii, " of ", length(batches)) 

+		##if(verbose) message("SNP batch ", ii, " of ", length(batches))

 		G <- GG[, k]

 		xx <- CP[, k]

 		highConf <- (1-exp(-xx/1000)) > GT.CONF.THR

@@ -1802,7 +1802,7 @@ fit.lm4 <- function(idxBatch,

 		AA <- A.snp[, k]

 		BB <- B.snp[, k]

-				  

+

 		##index <- GT.B <- GT.A <- vector("list", 3)

 		##names(index) <- names(GT.B) <- names(GT.A) <- c("AA", "AB", "BB")

 		Ns <- applyByGenotype(matrix(1, nrow(G), ncol(G)), rowSums, G)

@@ -1812,7 +1812,7 @@ fit.lm4 <- function(idxBatch,

 		muB <- muB[, 3]

 		X <- cbind(1, log2(c(muA, muB)))

 		J <- match(unique(batch(object)[k]), unique(batch(object)))

-		

+

 		Y <- log2(c(phiA.snp[, J], phiB.snp[, J]))

 		##--------------------------------------------------

@@ -1822,7 +1822,7 @@ fit.lm4 <- function(idxBatch,

 		X <- X[!remove, ]

 		##--------------------------------------------------

 		betahat <- solve(crossprod(X), crossprod(X, Y))

-		

+

 		##nonpolymorphic

 		A <- AA.np[, k]

@@ -1917,7 +1917,7 @@ cnrma2 <- function(A, filenames, row.names, verbose=TRUE, seed=1, cdfName, sns){

 		 row.names=row.names,

 		 filenames=filenames,

 		 A=A,

-		 SKW=SKW, 

+		 SKW=SKW,

 		 seed=seed,

 		 pkgname=pkgname,

 		 cdfName=cdfName,

@@ -2051,15 +2051,15 @@ lm.parameters <- function(object, batch){##cnOptions){

 			    paste("sig2A", uplate, sep="_"),

 			    paste("sig2B", uplate, sep="_"),

 			    paste("nuA", uplate, sep="_"),

-			    paste("nuA.se", uplate, sep="_"),			    

+			    paste("nuA.se", uplate, sep="_"),

 			    paste("nuB", uplate, sep="_"),

-			    paste("nuB.se", uplate, sep="_"),			    			    

+			    paste("nuB.se", uplate, sep="_"),

 			    paste("phiA", uplate, sep="_"),

-			    paste("phiA.se", uplate, sep="_"),			    

+			    paste("phiA.se", uplate, sep="_"),

 			    paste("phiB", uplate, sep="_"),

-			    paste("phiB.se", uplate, sep="_"),			    

+			    paste("phiB.se", uplate, sep="_"),

 			    paste("phiAX", uplate, sep="_"),

-			    paste("phiBX", uplate, sep="_"),			    

+			    paste("phiBX", uplate, sep="_"),

 			    paste("corr", uplate, sep="_"),

 			    paste("corrA.BB", uplate, sep="_"),

 			    paste("corrB.AA", uplate, sep="_"))

@@ -2098,7 +2098,7 @@ nonpolymorphic <- function(object, cnOptions, tmp.objects){

 	nuA <- getParam(object, "nuA", batch)

 	nuB <- getParam(object, "nuB", batch)

 	phiA <- getParam(object, "phiA", batch)

-	phiB <- getParam(object, "phiB", batch)	

+	phiB <- getParam(object, "phiB", batch)

 	sns <- sampleNames(object)

 	muA <- tmp.objects[["muA"]]

 	muB <- tmp.objects[["muB"]]

@@ -2128,13 +2128,13 @@ nonpolymorphic <- function(object, cnOptions, tmp.objects){

 		ix <- 1:nrow(X)

 	}

 	betahat <- solve(crossprod(X[ix, ]), crossprod(X[ix, ], Y[ix]))

-	normal <- tmp.objects[["normal"]][!isSnp(object), ]	

+	normal <- tmp.objects[["normal"]][!isSnp(object), ]

 	if(CHR == 23){

 		##normalNP <- envir[["normalNP"]]

 		##normalNP <- normalNP[, plate==uplate[p]]

 		##nuT <- envir[["nuT"]]

 		##phiT <- envir[["phiT"]]

-		

+

 		##cnvs <- envir[["cnvs"]]

                 ##loader("cnProbes.rda", pkgname=pkgname, envir=.crlmmPkgEnv)

                 ##cnProbes <- get("cnProbes", envir=.crlmmPkgEnv)

@@ -2162,7 +2162,7 @@ nonpolymorphic <- function(object, cnOptions, tmp.objects){

 		mus <- log2(cbind(mu1, mu2))

 		X.men <- cbind(1, mus[, 1])

 		X.fem <- cbind(1, mus[, 2])

-		

+

 		Yhat1 <- as.numeric(X.men %*% betahat)

 		Yhat2 <- as.numeric(X.fem %*% betahat)

 		phi1 <- 2^(Yhat1)

@@ -2199,17 +2199,17 @@ nonpolymorphic <- function(object, cnOptions, tmp.objects){

 		nuA[!isSnp(object)] <- nu2

 		phiA[!isSnp(object)] <- phi2

-		

+

 		THR.NU.PHI <- cnOptions$THR.NU.PHI

 		if(THR.NU.PHI){

 			verbose <- cnOptions$verbose

 			##Assign values to object

 			object <- pr(object, "nuA", batch, nuA)

-			object <- pr(object, "phiA", batch, phiA)			

+			object <- pr(object, "phiA", batch, phiA)

 			##if(verbose) message("Thresholding nu and phi")

 			object <- thresholdModelParams(object, cnOptions)

 		} else {

-			object <- pr(object, "nuA", batch, nuA)		

+			object <- pr(object, "nuA", batch, nuA)

 			object <- pr(object, "phiA", batch, phiA)

 		}

 	} else {

@@ -2226,7 +2226,7 @@ nonpolymorphic <- function(object, cnOptions, tmp.objects){

 			verbose <- cnOptions$verbose

 			##Assign values to object

 			object <- pr(object, "nuA", batch, nuA)

-			object <- pr(object, "phiA", batch, phiA)			

+			object <- pr(object, "phiA", batch, phiA)

 			##if(verbose) message("Thresholding nu and phi")

 			object <- thresholdModelParams(object, cnOptions)

 			##reassign values (now thresholded at MIN.NU and MIN.PHI

@@ -2254,7 +2254,7 @@ withinGenotypeMoments <- function(object, cnOptions, tmp.objects){

 	vA <- tmp.objects[["vA"]]

 	vB <- tmp.objects[["vB"]]

 	Ns <- tmp.objects[["Ns"]]

-	G <- snpCall(object) 

+	G <- snpCall(object)

 	GT.CONF.THR <- cnOptions$GT.CONF.THR

 	CHR <- unique(chromosome(object))

 	A <- A(object)

@@ -2281,11 +2281,11 @@ withinGenotypeMoments <- function(object, cnOptions, tmp.objects){

 	for(j in 1:3){

 		GT <- G==j & highConf & IX & snpIndicator

 		GT <- GT * normal

-		Ns[, j+2] <- rowSums(GT, na.rm=TRUE)				

+		Ns[, j+2] <- rowSums(GT, na.rm=TRUE)

 		GT[GT == FALSE] <- NA

 		GT.A[[j]] <- GT*A

 		GT.B[[j]] <- GT*B

-		index[[j]] <- which(Ns[, j+2] > 0 & isSnp(object)) ##RS: added		

+		index[[j]] <- which(Ns[, j+2] > 0 & isSnp(object)) ##RS: added

 		muA[, j+2] <- rowMedians(GT.A[[j]], na.rm=TRUE)

 		muB[, j+2] <- rowMedians(GT.B[[j]], na.rm=TRUE)

 		vA[, j+2] <- rowMAD(GT.A[[j]], na.rm=TRUE)

@@ -2307,14 +2307,14 @@ withinGenotypeMoments <- function(object, cnOptions, tmp.objects){

 	if(CHR == 23){

 		k <- 1

 		for(j in c(1,3)){

-			GT <- G==j & highConf & !IX 

+			GT <- G==j & highConf & !IX

 			Ns[, k] <- rowSums(GT)

 			GT[GT == FALSE] <- NA

 			muA[, k] <- rowMedians(GT*A, na.rm=TRUE)

 			muB[, k] <- rowMedians(GT*B, na.rm=TRUE)

 			vA[, k] <- rowMAD(GT*A, na.rm=TRUE)

 			vB[, k] <- rowMAD(GT*B, na.rm=TRUE)

-			

+

 			DF <- Ns[, k]-1

 			DF[DF < 1] <- 1

 			v0A <- median(vA[, k], na.rm=TRUE)

@@ -2322,7 +2322,7 @@ withinGenotypeMoments <- function(object, cnOptions, tmp.objects){

 			vA[, k] <- (vA[, k]*DF + v0A*DF.PRIOR)/(DF.PRIOR+DF)

 			vA[is.na(vA[, k]), k] <- v0A

 			vB[, k] <- (vB[, k]*DF + v0B*DF.PRIOR)/(DF.PRIOR+DF)

-			vB[is.na(vB[, k]), k] <- v0B			

+			vB[is.na(vB[, k]), k] <- v0B

 			k <- k+1

 		}

 	}

@@ -2394,7 +2394,7 @@ oneBatch <- function(object, cnOptions, tmp.objects){

 	index.BB <- which(Ns[, "BB"] >= MIN.OBS)

 	correct.orderA <- muA[, "AA"] > muA[, "BB"]

 	correct.orderB <- muB[, "BB"] > muB[, "AA"]

-	##For chr X, this will ignore the males 

+	##For chr X, this will ignore the males

 	nobs <- rowSums(Ns[, 3:5] >= MIN.OBS, na.rm=TRUE) == 3

 	index.complete <- which(correct.orderA & correct.orderB & nobs) ##be selective here

 	size <- min(5000, length(index.complete))

@@ -2423,7 +2423,7 @@ oneBatch <- function(object, cnOptions, tmp.objects){

 	notMissing <- !(is.na(muA[, "A"]) | is.na(muA[, "B"]) | is.na(muB[, "A"]) | is.na(muB[, "B"]))

 	complete <- list()

 	complete[[1]] <- which(correct.orderA & correct.orderB & nobsA & notMissing) ##be selective here

-	complete[[2]] <- which(correct.orderA & correct.orderB & nobsB & notMissing) ##be selective here	

+	complete[[2]] <- which(correct.orderA & correct.orderB & nobsB & notMissing) ##be selective here

 	size <- min(5000, length(complete[[1]]))

 	if(size > 5000) complete <- lapply(complete, function(x) sample(x, size))

 	if(CHR == 23){

@@ -2473,7 +2473,7 @@ oneBatch <- function(object, cnOptions, tmp.objects){

 		muB[index[[j]], -c(1, 2, k+2)] <- mus[, 3:4]

 	}

 	negA <- rowSums(muA < 0) > 0

-	negB <- rowSums(muB < 0) > 0	

+	negB <- rowSums(muB < 0) > 0

 	snpflags <- snpflags| negA | negB | rowSums(is.na(muA[, 3:5]), na.rm=TRUE) > 0

 	tmp.objects$snpflags <- snpflags

 	tmp.objects[["muA"]] <- muA

@@ -2497,7 +2497,7 @@ locationAndScale <- function(object, cnOptions, tmp.objects){

 	AA.A <- GT.A[[1]]

 	AB.A <- GT.A[[2]]

 	BB.A <- GT.A[[3]]

-	

+

 	AA.B <- GT.B[[1]]

 	AB.B <- GT.B[[2]]

 	BB.B <- GT.B[[3]]

@@ -2505,7 +2505,7 @@ locationAndScale <- function(object, cnOptions, tmp.objects){

 	##batch <- unique(object$batch)

 	batch <- unique(batch(object))

 	tau2A <- getParam(object, "tau2A", batch)

-	tau2A[index.BB] <- rowMAD(log2(x), log2(x), na.rm=TRUE)^2	

+	tau2A[index.BB] <- rowMAD(log2(x), log2(x), na.rm=TRUE)^2

 	DF <- Ns[, "BB"]-1

 	DF[DF < 1] <- 1

 	med <- median(tau2A, na.rm=TRUE)

@@ -2513,17 +2513,17 @@ locationAndScale <- function(object, cnOptions, tmp.objects){

 	tau2A[is.na(tau2A) & isSnp(object)] <- med

 	object <- pr(object, "tau2A", batch, tau2A)

-	sig2B <- getParam(object, "sig2B", batch)	

+	sig2B <- getParam(object, "sig2B", batch)

 	x <- BB.B[index.BB, ]

-	sig2B[index.BB] <- rowMAD(log2(x), log2(x), na.rm=TRUE)^2	

+	sig2B[index.BB] <- rowMAD(log2(x), log2(x), na.rm=TRUE)^2

 	med <- median(sig2B, na.rm=TRUE)

 	sig2B <- (sig2B * DF  +  med * DF.PRIOR)/(DF.PRIOR + DF)

 	sig2B[is.na(sig2B) & isSnp(object)] <- med

-	object <- pr(object, "sig2B", batch, sig2B)	

+	object <- pr(object, "sig2B", batch, sig2B)

-	tau2B <- getParam(object, "tau2B", batch)	

+	tau2B <- getParam(object, "tau2B", batch)

 	x <- AA.B[index.AA, ]

-	tau2B[index.AA] <- rowMAD(log2(x), log2(x), na.rm=TRUE)^2		

+	tau2B[index.AA] <- rowMAD(log2(x), log2(x), na.rm=TRUE)^2

 	DF <- Ns[, "AA"]-1

 	DF[DF < 1] <- 1

 	med <- median(tau2B, na.rm=TRUE)

@@ -2531,9 +2531,9 @@ locationAndScale <- function(object, cnOptions, tmp.objects){

 	tau2B[is.na(tau2B) & isSnp(object)] <- med

 	object <- pr(object, "tau2B", batch, tau2B)

-	sig2A <- getParam(object, "sig2A", batch)	

+	sig2A <- getParam(object, "sig2A", batch)

 	x <- AA.A[index.AA, ]

-	sig2A[index.AA] <- rowMAD(log2(x), log2(x), na.rm=TRUE)^2##var(log(IA)|AA)	

+	sig2A[index.AA] <- rowMAD(log2(x), log2(x), na.rm=TRUE)^2##var(log(IA)|AA)

 	med <- median(sig2A, na.rm=TRUE)

 	sig2A <- (sig2A * DF  +  med * DF.PRIOR)/(DF.PRIOR + DF)

 	sig2A[is.na(sig2A) & isSnp(object)] <- med

@@ -2550,7 +2550,7 @@ locationAndScale <- function(object, cnOptions, tmp.objects){

 		med <- median(corr, na.rm=TRUE)

 		corr <- (corr*DF  +  med * DF.PRIOR)/(DF.PRIOR + DF)

 		corr[is.na(corr) & isSnp(object)] <- med

-		object <- pr(object, "corr", batch, corr)		

+		object <- pr(object, "corr", batch, corr)

 	}

 	corrB.AA <- getParam(object, "corrB.AA", batch)

 	backgroundB <- AA.B[index.AA, ]

@@ -2563,7 +2563,7 @@ locationAndScale <- function(object, cnOptions, tmp.objects){

 	corrB.AA[is.na(corrB.AA) & isSnp(object)] <- med

 	object <- pr(object, "corrB.AA", batch, corrB.AA)

-	corrA.BB <- getParam(object, "corrA.BB", batch)	

+	corrA.BB <- getParam(object, "corrA.BB", batch)

 	backgroundA <- BB.A[index.BB, ]

 	signalB <- BB.B[index.BB, ]

 	corrA.BB[index.BB] <- rowCors(backgroundA, signalB, na.rm=TRUE)

@@ -2598,7 +2598,7 @@ coefs <- function(object, cnOptions, tmp.objects){

 			IB <- muB[, -4]

 			vA <- vA[, -4]

 			vB <- vB[, -4]

-			Np <- Ns[, -4] 

+			Np <- Ns[, -4]

 		} else{

 			IA <- muA

 			IB <- muB

@@ -2606,7 +2606,7 @@ coefs <- function(object, cnOptions, tmp.objects){

 			vB <- vB

 			Np <- Ns

 		}

-		

+

 	}

 	Np[Np < 1] <- 1

 	vA2 <- vA^2/Np

@@ -2617,7 +2617,7 @@ coefs <- function(object, cnOptions, tmp.objects){

 	YB <- IB*wB

 	##update lm.coefficients stored in object

 	object <- nuphiAllele(object, allele="A", Ystar=YA, W=wA, tmp.objects=tmp.objects, cnOptions=cnOptions)

-	object <- nuphiAllele(object, allele="B", Ystar=YB, W=wB, tmp.objects=tmp.objects, cnOptions=cnOptions)	

+	object <- nuphiAllele(object, allele="B", Ystar=YB, W=wB, tmp.objects=tmp.objects, cnOptions=cnOptions)

 	##---------------------------------------------------------------------------

 	##Estimate crosshyb using X chromosome and sequence information

 	##---------------------------------------------------------------------------

@@ -2644,7 +2644,7 @@ polymorphic <- function(object, cnOptions, tmp.objects){

 	vA <- tmp.objects[["vA"]]

 	vB <- tmp.objects[["vB"]]

 	Ns <- tmp.objects[["Ns"]]

-	

+

 	nuA <- getParam(object, "nuA", batch)

 	nuB <- getParam(object, "nuB", batch)

 	nuA.se <- getParam(object, "nuA.se", batch)

@@ -2663,12 +2663,12 @@ polymorphic <- function(object, cnOptions, tmp.objects){

 	##---------------------------------------------------------------------------

 	if(CHR == 23){

 		phiAX <- getParam(object, "phiAX", batch)  ##nonspecific hybridization coef

-		phiBX <- getParam(object, "phiBX", batch)  ##nonspecific hybridization coef			

-		phistar <- phiBX/phiA  

+		phiBX <- getParam(object, "phiBX", batch)  ##nonspecific hybridization coef

+		phistar <- phiBX/phiA

 		tmp <- (B-nuB - phistar*A + phistar*nuA)/phiB

 		copyB <- tmp/(1-phistar*phiAX/phiB)

 		copyA <- (A-nuA-phiAX*copyB)/phiA

-		CB(object) <- copyB  ## multiplies by 100 and converts to integer 

+		CB(object) <- copyB  ## multiplies by 100 and converts to integer

 		CA(object) <- copyA

 	} else{

 		CA(object) <- matrix((1/phiA*(A-nuA)), nrow(A), ncol(A))

@@ -2693,21 +2693,21 @@ posteriorProbability.snps <- function(object, cnOptions, tmp.objects=list()){

 	sig2A <- getParam(object, "sig2A", batch)[I]

 	sig2B <- getParam(object, "sig2B", batch)[I]

 	tau2A <- getParam(object, "tau2A", batch)[I]

-	tau2B <- getParam(object, "tau2B", batch)[I]	

+	tau2B <- getParam(object, "tau2B", batch)[I]

 	corrA.BB <- getParam(object, "corrA.BB", batch)[I]

 	corrB.AA <- getParam(object, "corrB.AA", batch)[I]

-	corr <- getParam(object, "corr", batch)[I]		

+	corr <- getParam(object, "corr", batch)[I]

 	nuA <- getParam(object, "nuA", batch)[I]

-	nuB <- getParam(object, "nuB", batch)[I]	

+	nuB <- getParam(object, "nuB", batch)[I]

 	phiA <- getParam(object, "phiA", batch)[I]

 	phiB <- getParam(object, "phiB", batch)[I]

 	normal <- tmp.objects[["normal"]][I, ]

 	prior.prob <- cnOptions$prior.prob

-	emit <- array(NA, dim=c(nrow(A), ncol(A), 10))##SNPs x sample x 'truth'	

+	emit <- array(NA, dim=c(nrow(A), ncol(A), 10))##SNPs x sample x 'truth'

 	lA <- log2(A)

-	lB <- log2(B)	

+	lB <- log2(B)

 	X <- cbind(lA, lB)

-	counter <- 1##state counter								

+	counter <- 1##state counter

 	for(CT in 0:3){

 		for(CA in 0:CT){

 			cat(".")

@@ -2721,7 +2721,7 @@ posteriorProbability.snps <- function(object, cnOptions, tmp.objects=list()){

 			if(CHR == 23){

 				##means <- cbind(suppressWarnings(log2(nuA[, p]+CA*phiA[, p] + CB*phiAx[, p])), suppressWarnings(log2(nuB[, p]+CB*phiB[, p] + CA*phiBx[, p])))

 				meanA <- suppressWarnings(log2(nuA+CA*phiA + CB*phiAX))

-				meanB <- suppressWarnings(log2(nuB+CB*phiB + CA*phiBX))				

+				meanB <- suppressWarnings(log2(nuB+CB*phiB + CA*phiBX))

 			} else{

 				##means <- cbind(suppressWarnings(log2(nuA+CA*phiA)), suppressWarnings(log2(nuB+CB*phiB)))

 				meanA <- suppressWarnings(log2(nuA+CA*phiA))

@@ -2803,7 +2803,7 @@ bias1 <- function(idxBatch,

 		  prior.prob,

 		  MIN.SAMPLES,

 		  verbose){

-	

+

 }

 bias2 <- function(idxBatch,

@@ -2854,7 +2854,7 @@ bias2 <- function(idxBatch,

 		index <- which(prUp > 0.05 & prUp > prDn)

 		##if proportion up greater than 5%, trim the high cn est.

-		norm[index, k] <- argmax.cn[index, ] > 3 

+		norm[index, k] <- argmax.cn[index, ] > 3

 		index <- which(prDn > 0.05 & prDn > prUp)

 		norm[index, k] <- argmax.cn[index, ] < 3

@@ -2897,7 +2897,7 @@ biasAdjust <- function(object, prior.prob=rep(1/4, 4), MIN.SAMPLES=10, verbose=T

 		 prior.prob=prior.prob,

 		 emit=emit,

 		 MIN.SAMPLES=MIN.SAMPLES,

-		 verbose=verbose)	

+		 verbose=verbose)

 }

@@ -2915,7 +2915,7 @@ biasAdjNP <- function(object, cnOptions, tmp.objects){

 	nuA <- getParam(object, "nuA", batch)

 	phiA <- getParam(object, "phiA", batch)

 	prior.prob <- cnOptions$prior.prob

-	emit <- array(NA, dim=c(nrow(A), ncol(A), 4))##SNPs x sample x 'truth'	

+	emit <- array(NA, dim=c(nrow(A), ncol(A), 4))##SNPs x sample x 'truth'

 	lT <- log2(A)

 	I <- isSnp(object)

 	counter <- 1 ##state counter

@@ -2936,7 +2936,7 @@ biasAdjNP <- function(object, cnOptions, tmp.objects){

 		counter <- counter+1

 	}

 	mostLikelyState <- apply(emit, c(1, 2), function(x) order(x, decreasing=TRUE)[1])

-	

+

 	if(CHR == 23){

 		## the state index for male on chromosome 23  is 2

 		## add 1 so that the state index is 3 for 'normal' state

@@ -2966,11 +2966,11 @@ biasAdjNP <- function(object, cnOptions, tmp.objects){

 getParams <- function(object, batch){

 	##batch <- unique(object$batch)

 	batch <- unique(batch(object))

-	if(length(batch) > 1) stop("batch variable not unique")		

+	if(length(batch) > 1) stop("batch variable not unique")

 	nuA <- as.numeric(fData(object)[, match(paste("nuA", batch, sep="_"), fvarLabels(object))])

-	nuB <- as.numeric(fData(object)[, match(paste("nuB", batch, sep="_"), fvarLabels(object))])	

+	nuB <- as.numeric(fData(object)[, match(paste("nuB", batch, sep="_"), fvarLabels(object))])

 	phiA <- as.numeric(fData(object)[, match(paste("phiA", batch, sep="_"), fvarLabels(object))])

-	phiB <- as.numeric(fData(object)[, match(paste("phiB", batch, sep="_"), fvarLabels(object))])	

+	phiB <- as.numeric(fData(object)[, match(paste("phiB", batch, sep="_"), fvarLabels(object))])

 	tau2A <- as.numeric(fData(object)[, match(paste("tau2A", batch, sep="_"), fvarLabels(object))])

 	tau2B <- as.numeric(fData(object)[, match(paste("tau2B", batch, sep="_"), fvarLabels(object))])

 	sig2A <- as.numeric(fData(object)[, match(paste("sig2A", batch, sep="_"), fvarLabels(object))])

@@ -3012,17 +3012,17 @@ thresholdModelParams <- function(object, cnOptions){

 	phiB <- getParam(object, "phiB", batch)

 	if(!all(is.na(phiB))){

 		phiB[phiB < MIN.PHI] <- MIN.PHI

-		object <- pr(object, "phiB", batch, phiB)		

+		object <- pr(object, "phiB", batch, phiB)

 	}

-	phiAX <- as.numeric(getParam(object, "phiAX", batch))	

+	phiAX <- as.numeric(getParam(object, "phiAX", batch))

 	if(!all(is.na(phiAX))){

 		phiAX[phiAX < MIN.PHI] <- MIN.PHI

-		object <- pr(object, "phiAX", batch, phiAX)		

+		object <- pr(object, "phiAX", batch, phiAX)

 	}

 	phiBX <- as.numeric(getParam(object, "phiBX", batch))

 	if(!all(is.na(phiBX))){

 		phiBX[phiBX < MIN.PHI] <- MIN.PHI

-		object <- pr(object, "phiBX", batch, phiBX)			

+		object <- pr(object, "phiBX", batch, phiBX)

 	}

 	return(object)

 }

@@ -3079,11 +3079,11 @@ computeCopynumber.CNSet <- function(object, cnOptions){

 		verbose <- cnOptions$verbose

 		##if(verbose) message("Thresholding nu and phi")

 		object <- thresholdModelParams(object, cnOptions)

-	}		

-	##if(verbose) message("\nAllele specific copy number")	

+	}

+	##if(verbose) message("\nAllele specific copy number")

 	object <- polymorphic(object, cnOptions, tmp.objects)

 	if(any(!isSnp(object))){ ## there are nonpolymorphic probes

-		##if(verbose) message("\nCopy number for nonpolymorphic probes...")	

+		##if(verbose) message("\nCopy number for nonpolymorphic probes...")

 		object <- nonpolymorphic(object, cnOptions, tmp.objects)

 	}

 	##---------------------------------------------------------------------------

@@ -3101,9 +3101,9 @@ computeCopynumber.CNSet <- function(object, cnOptions){

 	object <- pr(object, "phiB", PLATE, getParam(object, "phiB", PLATE))

 	object <- pr(object, "phiB.se", PLATE, getParam(object, "phiB.se", PLATE))

 	object <- pr(object, "tau2A", PLATE, getParam(object, "tau2A", PLATE))

-	object <- pr(object, "tau2B", PLATE, getParam(object, "tau2B", PLATE))				

+	object <- pr(object, "tau2B", PLATE, getParam(object, "tau2B", PLATE))

 	object <- pr(object, "sig2A", PLATE, getParam(object, "sig2A", PLATE))

-	object <- pr(object, "sig2B", PLATE, getParam(object, "sig2B", PLATE))		

+	object <- pr(object, "sig2B", PLATE, getParam(object, "sig2B", PLATE))

 	object <- pr(object, "phiAX", PLATE, as.numeric(getParam(object, "phiAX", PLATE)))

 	object <- pr(object, "phiBX", PLATE, as.numeric(getParam(object, "phiBX", PLATE)))

 	object <- pr(object, "corr", PLATE, getParam(object, "corr", PLATE))

@@ -3122,3 +3122,50 @@ computeCopynumber.CNSet <- function(object, cnOptions){

+constructFeatureData <- function(gns, cdfName){

+	pkgname <- paste(cdfName, "Crlmm", sep="")

+	path <- system.file("extdata", package=pkgname)

+	load(file.path(path, "cnProbes.rda"))

+	load(file.path(path, "snpProbes.rda"))

+	cnProbes$chr <- chromosome2integer(cnProbes$chr)

+	cnProbes <- as.matrix(cnProbes)

+	snpProbes$chr <- chromosome2integer(snpProbes$chr)

+	snpProbes <- as.matrix(snpProbes)

+	mapping <- rbind(snpProbes, cnProbes, deparse.level=0)

+	mapping <- mapping[match(gns, rownames(mapping)), ]

+	isSnp <- 1L-as.integer(gns %in% rownames(cnProbes))

+	mapping <- cbind(mapping, isSnp, deparse.level=0)

+	stopifnot(identical(rownames(mapping), gns))

+	colnames(mapping) <- c("chromosome", "position", "isSnp")

+	new("AnnotatedDataFrame",

+	    data=data.frame(mapping),

+	    varMetadata=data.frame(labelDescription=colnames(mapping)))

+}

+constructAssayData <- function(np, snp, object, storage.mode="environment", order.index){

+	stopifnot(identical(snp$gns, featureNames(object)))

+	gns <- c(featureNames(object), np$gns)

+	sns <- np$sns

+	np <- np[1:2]

+	snp <- snp[1:2]

+	stripnames <- function(x) {

+		dimnames(x) <- NULL

+		x

+	}

+	np <- lapply(np, stripnames)

+	snp <- lapply(snp, stripnames)

+	A <- rbind(snp[[1]], np[[1]], deparse.level=0)[order.index, ]

+	B <- rbind(snp[[2]], np[[2]], deparse.level=0)[order.index, ]

+	gt <- stripnames(calls(object))

+	emptyMatrix <- matrix(integer(), nrow(np[[1]]), ncol(A))

+	gt <- rbind(gt, emptyMatrix, deparse.level=0)[order.index,]

+	pr <- stripnames(snpCallProbability(object))

+	pr <- rbind(pr, emptyMatrix, deparse.level=0)[order.index, ]

+	emptyMatrix <- matrix(integer(), nrow(A), ncol(A))

+	aD <- assayDataNew(storage.mode,

+			   alleleA=A,

+			   alleleB=B,

+			   call=gt,

+			   callProbability=pr,

+			   CA=emptyMatrix,

+			   CB=emptyMatrix)

+}

@@ -39,50 +39,4 @@ setMethod("totalCopyNumber",

 })

-constructFeatureData <- function(gns, cdfName){

-	pkgname <- paste(cdfName, "Crlmm", sep="")	

-	path <- system.file("extdata", package=pkgname)

-	load(file.path(path, "cnProbes.rda"))

-	load(file.path(path, "snpProbes.rda"))

-	cnProbes$chr <- chromosome2integer(cnProbes$chr)

-	cnProbes <- as.matrix(cnProbes)

-	snpProbes$chr <- chromosome2integer(snpProbes$chr)

-	snpProbes <- as.matrix(snpProbes)

-	mapping <- rbind(snpProbes, cnProbes, deparse.level=0)

-	mapping <- mapping[match(gns, rownames(mapping)), ]

-	isSnp <- 1L-as.integer(gns %in% rownames(cnProbes))

-	mapping <- cbind(mapping, isSnp, deparse.level=0)

-	stopifnot(identical(rownames(mapping), gns))

-	colnames(mapping) <- c("chromosome", "position", "isSnp")

-	new("AnnotatedDataFrame",

-	    data=data.frame(mapping),

-	    varMetadata=data.frame(labelDescription=colnames(mapping)))	

-}

-constructAssayData <- function(np, snp, object, storage.mode="environment", order.index){

-	stopifnot(identical(snp$gns, featureNames(object)))

-	gns <- c(featureNames(object), np$gns)

-	sns <- np$sns

-	np <- np[1:2]

-	snp <- snp[1:2]

-	stripnames <- function(x) {

-		dimnames(x) <- NULL

-		x

-	}

-	np <- lapply(np, stripnames)

-	snp <- lapply(snp, stripnames)