@@ -57,7 +57,7 @@ construct <- function(filenames, cdfName, copynumber=FALSE,

 		stopifnot(length(batch) == length(sns))

 	}

 	if(missing(sns) & missing(filenames)) stop("one of filenames or samplenames (sns) must be provided")

-	if(verbose) message("Initializing container for assay data elements alleleA, alleleB, call, callProbability, CA, CB")

+	if(verbose) message("Initializing container for assay data elements alleleA, alleleB, call, callProbability")

 	if(!missing(filenames)){

 		if(missing(sns)) sns <- basename(filenames)

 		protocolData <- getProtocolData.Affy(filenames)

@@ -605,15 +605,15 @@ crlmmCopynumber <- function(object,

 			if(verbose) message("Batch ", ii, " of ", length(which.batches))

 			row.index <- which(chromosome(object) == i)

 			##Note that ffdf assayDataElements are data.frames after subsetting(not matrices)

-			ca <- as.matrix(CA(object)[row.index, j])

-			cb <- as.matrix(CB(object)[row.index, j])

-			dimnames(ca) <- dimnames(cb) <- list(featureNames(object)[row.index], sampleNames(object)[j])

+##			ca <- as.matrix(CA(object)[row.index, j])

+##			cb <- as.matrix(CB(object)[row.index, j])

+##			dimnames(ca) <- dimnames(cb) <- list(featureNames(object)[row.index], sampleNames(object)[j])

 			tmp <- new("CNSet",

 				   call=as.matrix(calls(object)[row.index, j]),

 				   callProbability=as.matrix(snpCallProbability(object)[row.index, j]),

 				   alleleA=as.matrix(A(object)[row.index, j]),

 				   alleleB=as.matrix(B(object)[row.index, j]),

-				   CA=ca, CB=cb,

+##				   CA=ca, CB=cb,

 				   phenoData=phenoData(object)[j, ],

 				   annotation=annotation(object))

 			featureData(tmp) <- addFeatureAnnotation(tmp)

@@ -634,12 +634,12 @@ crlmmCopynumber <- function(object,

 						 THR.NU.PHI=THR.NU.PHI,

 						 thresholdCopynumber=thresholdCopynumber)

 			fData(tmp) <- fData(tmp)[, -(1:3)]

-			CA(tmp) <- matrix(as.integer(CA(tmp)*100), nrow=nrow(tmp), ncol=ncol(tmp),

-					  dimnames=list(featureNames(tmp), sampleNames(tmp)))

-			CB(tmp) <- matrix(as.integer(CB(tmp)*100), nrow=nrow(tmp), ncol=ncol(tmp),

-					  dimnames=list(featureNames(tmp), sampleNames(tmp)))

-			CA(object)[row.index, j] <- CA(tmp)

-			CB(object)[row.index, j] <- CB(tmp)

+##			CA(tmp) <- matrix(as.integer(CA(tmp)*100), nrow=nrow(tmp), ncol=ncol(tmp),

+##					  dimnames=list(featureNames(tmp), sampleNames(tmp)))

+##			CB(tmp) <- matrix(as.integer(CB(tmp)*100), nrow=nrow(tmp), ncol=ncol(tmp),

+##					  dimnames=list(featureNames(tmp), sampleNames(tmp)))

+##			CA(object)[row.index, j] <- CA(tmp)

+##			CB(object)[row.index, j] <- CB(tmp)

 			##ad-hocery

 			batchName <- unique(batch(object)[j])

 			fvarLabels(tmp)[15:17] <- paste(c("corrAB", "corrBB", "corrAA"), batchName, sep=".")

@@ -1744,8 +1744,8 @@ nonpolymorphic <- function(object, cnOptions, tmp.objects){

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

 	A <- A(object)

 	B <- B(object)

-	CA <- CA(object)

-	CB <- CB(object)

+##	CA <- CA(object)

+##	CB <- CB(object)

 	if(CHR == 23){

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

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

@@ -1813,20 +1813,12 @@ nonpolymorphic <- function(object, cnOptions, tmp.objects){

 		if(any(pseudoAR)){

 			nu1[pseudoAR] <- 2^(mus[pseudoAR, 1]) - 2*phi1[pseudoAR]

 		}

-		CT1 <- 1/phi1*(A.male-nu1)

-		CT2 <- 1/phi2*(A.female-nu2)

-		##CT2 <- 1/phi2*(NP[, gender=="female"]-nu2)

-		##CT1 <- matrix(as.integer(100*CT1), nrow(CT1), ncol(CT1))

-		##CT2 <- matrix(as.integer(100*CT2), nrow(CT2), ncol(CT2))

-		##CT <- envir[["CT"]]

-		CA <- CA(obj1)

-		CA[, gender==1] <- CT1

-		CA[, gender==2] <- CT2

-		CA(object)[!isSnp(object), ] <- CA

-		##CT[, plate==uplate[p] & gender=="male"] <- CT1

-		##CT[, plate==uplate[p] & gender=="female"] <- CT2

-		##envir[["CT"]] <- CT

-

+##		CT1 <- 1/phi1*(A.male-nu1)

+##		CT2 <- 1/phi2*(A.female-nu2)

+##		CA <- CA(obj1)

+##		CA[, gender==1] <- CT1

+##		CA[, gender==2] <- CT2

+##		CA(object)[!isSnp(object), ] <- CA

 		##only using females to compute the variance

 		##normalNP[, gender=="male"] <- NA

 		normal[, gender==1] <- NA

@@ -2287,13 +2279,13 @@ polymorphic <- function(object, cnOptions, tmp.objects){

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

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

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

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

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

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

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

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

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

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

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

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

 	A <- A(object)

 	B <- B(object)

@@ -2305,14 +2297,14 @@ polymorphic <- function(object, cnOptions, tmp.objects){

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

 		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 

-		CA(object) <- copyA

+##		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 

+##		CA(object) <- copyA

 	} else{

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

-		CB(object) <- matrix((1/phiB*(B-nuB)), nrow(B), ncol(B))

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

+##		CB(object) <- matrix((1/phiB*(B-nuB)), nrow(B), ncol(B))

 	}

 	return(object)

@@ -2667,28 +2659,7 @@ thresholdModelParams <- function(object, cnOptions){

 	return(object)

 }

-##computeCopynumber.SnpSuperSet <- function(object, cnOptions){

-####	use.ff <- cnOptions[["use.ff"]]

-####	if(!use.ff){

-####		object <- as(object, "CrlmmSet")

-####	} else	object <- as(object, "CrlmmSetFF")

-##	bias.adj <- cnOptions[["bias.adj"]]

-##	##must be FALSE to initialize parameters

-##	cnOptions[["bias.adj"]] <- FALSE

-##	## Add linear model parameters to the CrlmmSet object

-##	featureData(object) <- lm.parameters(object, cnOptions)

-##	if(!isValidCdfName(annotation(object))) stop(annotation(object), " not supported.")

-##	object <- as(object, "CNSet")

-##	object <- computeCopynumber.CNSet(object, cnOptions)

-##	if(bias.adj==TRUE){## run a second time

-##		object <- computeCopynumber.CNSet(object, cnOptions)

-##	}

-##	return(object)

-##}

-

-

 computeCopynumber.CNSet <- function(object, cnOptions){

-	##PLATE <- unique(object$batch)

 	PLATE <- unique(batch(object))

 	verbose <- cnOptions$verbose

 	tmp.objects <- instantiateObjects(object, cnOptions)

@@ -93,7 +93,7 @@ setMethod("computeCopynumber", "CNSet",

 				    THR.NU.PHI=THR.NU.PHI,

 				    thresholdCopynumber=thresholdCopynumber)

 	bias.adj <- cnOptions[["bias.adj"]]

-	if(bias.adj & all(is.na(CA(object)))){

+	if(bias.adj & all(is.na(nu(object, "A")[, 1])){

 		cnOptions[["bias.adj"]] <- FALSE

 	}

 	object <- computeCopynumber.CNSet(object, cnOptions)				

@@ -129,7 +129,8 @@ if(!file.exists(file.path(outdir, "cnSet.rda"))){

 					   batch=batch[1:20])

 	class(calls(gtSet.assayData_matrix))

 }

-q("no")

+##q("no")

+stop()

 @ 

 Next, we estimate copy number for the 20 CEL files.  The copy number