@@ -1,7 +1,7 @@

 Package: crlmm

 Type: Package

 Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays.

-Version: 1.11.14

+Version: 1.11.15

 Date: 2010-12-10

 Author: Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.edu.au>, Ingo Ruczinski <iruczins@jhsph.edu>, Rafael A Irizarry

 Maintainer: Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.EDU.AU>

@@ -102,3 +102,5 @@ setGeneric("calculatePosteriorMean", function(object, posteriorProb, copyNumber=

 setGeneric("predictionRegion", function(object, copyNumber=0:4)

 	   standardGeneric("predictionRegion"))

+setGeneric("xyplot", useAsDefault=function(x, data, ...) lattice::xyplot(x, data,...))

+setGeneric("xyplotcrlmm", function(x, data, predictRegion,...) standardGeneric("xyplotcrlmm"))

@@ -1,13 +1,4 @@

-getNu <- function(object){

-	nu.a <- nuA(object)

-	nu.b <- nuB(object)

-	cbind(nu.a, nu.b)

-}

-getPhi <- function(object){

-	phi.a <- phiA(object)

-	phi.b <- phiB(object)

-	cbind(phi.a, phi.b)

-}

+

 getSigma2 <- function(object){

 	phi.a <- phiA(object)

@@ -17,7 +8,7 @@ getSigma2 <- function(object){

 getCor <- function(object, gt){

 	if(gt =="NULL") return(rep(0, nrow(object)))

-	as.numeric(assayDataElement(batchStatistics(object), paste("corr", gt, sep="")))

+	assayDataElement(batchStatistics(object), paste("corr", gt, sep=""))

 }

 getTau2 <- function(object, gt){

@@ -31,7 +22,7 @@ getTau2 <- function(object, gt){

 getVar <- function(object, nm){

 	bs <- batchStatistics(object)

-	as.numeric(assayDataElement(bs, nm))

+	assayDataElement(bs, nm)

 }

 setMethod("nuA", signature=signature(object="CNSet"), function(object) nu(object, "A"))

@@ -345,19 +345,38 @@ setMethod("predictionRegion", signature(object="CNSet", copyNumber="integer"),

 		  ## mu: features x genotype x allele

 		  ## Sigma: features x genotype x covariance

 		  stopifnot(all(copyNumber %in% 0:4))

+		  getNu <- function(object){

+			  nu[, 1, ] <- nuA(object)

+			  nu[, 2, ] <- nuB(object)

+			  nu

+		  }

+		  getPhi <- function(object){

+			  phi[,1,] <- phiA(object)

+			  phi[,2,] <- phiB(object)

+			  phi

+		  }

 		  gts <- lapply(as.list(copyNumber), genotypes)

 		  nms <- unlist(gts)

 		  res <- vector("list", length(nms))

 		  ##names(res) <- paste("copyNumber", copyNumber, sep="")

 		  names(res) <- nms

+		  bnms <- batchNames(object)

+		  nu <- array(NA, dim=c(nrow(object), 2, length(bnms)))

+		  phi <- array(NA, dim=c(nrow(object), 2, length(bnms)))

 		  nu <- getNu(object)

 		  phi <- getPhi(object)

-		  mus <- matrix(NA, nrow(nu), 2, dimnames=list(NULL, LETTERS[1:2]))

-		  Sigma <- matrix(NA, nrow(nu), 3)

+		  ##mus <- matrix(NA, nrow(nu), 2, dimnames=list(NULL, LETTERS[1:2]))

+		  mus <- array(NA, dim=c(nrow(nu), 2, length(bnms)),

+			       dimnames=list(NULL, LETTERS[1:2],

+			       bnms))

+		  ## Sigma <- matrix(NA, nrow(nu), 3)

+		  Sigma <- array(NA, dim=c(nrow(nu), 3, length(bnms)),

+				 dimnames=list(NULL, c("varA", "cor", "varB"),

+				 bnms))

 		  bivariateCenter <- function(nu, phi){

 			  ##  lexical scope for mus, CA, CB

-			  mus[,1] <- log2(nu[1] + CA * phi[1])

-			  mus[,2] <- log2(nu[2] + CB * phi[2])

+			  mus[,1, ] <- log2(nu[, 1, ] + CA * phi[, 1, ])

+			  mus[,2, ] <- log2(nu[, 2, ] + CB * phi[, 2, ])

 			  mus

 		  }

 		  for(i in seq_along(copyNumber)){

@@ -372,12 +391,24 @@ setMethod("predictionRegion", signature(object="CNSet", copyNumber="integer"),

 				  gt.corr <- genotypeCorrelation(gt)

 				  nma <- ifelse(CA == 0, "tau2A.BB", "tau2A.AA")

 				  nmb <- ifelse(CB == 0, "tau2B.AA", "tau2B.BB")

-				  Sigma[,1] <- getVar(object, nma)

-				  Sigma[,3] <- getVar(object, nmb)

-				  Sigma[,2] <- getCor(object, gt.corr)

+				  Sigma[, 1, ] <- getVar(object, nma)

+				  Sigma[, 3, ] <- getVar(object, nmb)

+				  Sigma[, 2, ] <- getCor(object, gt.corr)

 				  res[[gt]]$mu <- bivariateCenter(nu, phi)

 				  res[[gt]]$cov <- Sigma

 			  }

 		  }

 		  return(res)

 	  })

+

+

+setMethod("xyplotcrlmm", signature(x="formula", data="CNSet", predictRegion="list"),

+	  function(x, data, predictRegion, ...){

+		  df <- data.frame(A=log2(A(data)), B=log2(B(data)), gt=calls(data), gt.conf=confs(data))#, snp=snpId)

+		  ##df <- as.data.frame(data)

+		  xyplot(x, df, predictRegion=predictRegion, ...)

+	  })

+setMethod("xyplot", signature(x="formula", data="CNSet"),

+	  function(x, data, ...){

+		  xyplotcrlmm(x, data, ...)

+})