@@ -501,5 +501,11 @@ then readIDAT() should work. Thanks to Pierre Cherel who reported this error.

 ** modified the storage of RG, XY and genotype call data to use the ff package and initializeBigMatrix() function from oligoClasses package.  The genotype call data now includes non-polymorphic probes (which have NA calls).  Functions which use ff storage are named readIdatFiles2(), RGtoXY2(), preprocessInfinium2v2(), crlmmIllumina2() and crlmmIlluminaV2().

 2010-04-08 B Carvalho committed version 1.5.46

-

 ** moved NEWS to inst/

+

+2010-04-09 M. Ritchie committed version 1.5.47

+** fixed bugs in code and removed copy number probes from output 

+(now saved separately as before - SnpSuperSet not used)

+** crlmmIllumina2() and crlmmIlluminaV2() now use the crlmmGT2() 

+function (which expects ff objects and supports parallel processing)

+** updated crlmmIllumina.pdf vignette

@@ -1,8 +1,8 @@

 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: 1.5.46

-Date: 2010-02-07

+Version: 1.5.47

+Date: 2010-04-09

 Author: Rafael A Irizarry, Benilton S Carvalho <carvalho@bclab.org>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.edu.au>

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

 Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms

@@ -663,7 +663,7 @@ RGtoXY = function(RG, chipType, verbose=TRUE) {

 #  argrg = aids[rrgg]

 #  brgrg = bids[rrgg]

-  tmpmat = matrix(0, nsnps, narrays)

+  tmpmat = matrix(as.integer(0), nsnps, narrays)

   rownames(tmpmat) = ids

   colnames(tmpmat) = sampleNames(RG)

   XY = new("NChannelSet", X=tmpmat, Y=tmpmat, zero=tmpmat, # Xnb=tmpmat, Ynb=tmpmat, Xse=tmpmat, Yse=tmpmat, zero=tmpmat,

@@ -778,6 +778,10 @@ RGtoXY2 = function(RG, chipType, verbose=TRUE) {

 	     protocolData=RG@protocolData, storage.mode="environment")

   featureNames(XY) = ids # featureNames(RG)

   gc()

+  # Need to initialize - matrices filled with NAs to begin with

+  XY@assayData$X[1:nsnps,] = 0

+  XY@assayData$Y[1:nsnps,] = 0

+  XY@assayData$zero[1:nsnps,] = 0

   # First sort out Infinium II SNPs, X -> R (allele A)  and Y -> G (allele B) from the same probe

   XY@assayData$X[!is.na(aord),] = exprs(channel(RG, "R"))[aord[!is.na(aord)],] # mostly red

@@ -866,8 +870,8 @@ stripNormalize = function(XY, useTarget=TRUE, verbose=TRUE) {

       tmp = normalize.quantiles.use.target(as.matrix(cbind(subX, subY)), targetdist[[s]])

     else

       tmp = normalize.quantiles(as.matrix(cbind(subX, subY)))

-    XY@assayData$X[sel,] = tmp[,1:(ncol(tmp)/2)]+16

-    XY@assayData$Y[sel,] = tmp[,(ncol(tmp)/2+1):ncol(tmp)]+16

+    XY@assayData$X[sel,] = matrix(as.integer(tmp[,1:(ncol(tmp)/2)]+16), nrow(tmp), ncol(tmp)/2)

+    XY@assayData$Y[sel,] = matrix(as.integer(tmp[,(ncol(tmp)/2+1):ncol(tmp)]+16), nrow(tmp), ncol(tmp)/2)

 #    Xqws[sel,] = tmp[,1:(ncol(tmp)/2)]

 #    Yqws[sel,] = tmp[,(ncol(tmp)/2+1):ncol(tmp)]

     rm(subX, subY, tmp, sel)

@@ -972,6 +976,7 @@ preprocessInfinium2 <- function(XY, mixtureSampleSize=10^5,

   ##     (like what happened to GCRMA)

   set.seed(seed)

   idx <- sort(sample(autosomeIndex, mixtureSampleSize))

+  idx2 <- sample(nprobes, 10^5)

   ##S will hold (A+B)/2 and M will hold A-B

   ##NOTE: We actually dont need to save S. Only for pics etc...

@@ -990,7 +995,7 @@ preprocessInfinium2 <- function(XY, mixtureSampleSize=10^5,

      if (getRversion() > '2.7.0') pb <- txtProgressBar(min=0, max=narrays, style=3)

   }

-  idx2 <- sample(nprobes, 10^5)

+

   for(i in 1:narrays){

     SKW[i] = mean((A[idx2,i]-mean(A[idx2,i]))^3)/(sd(A[idx2,i])^3)

     if(fitMixture){

@@ -1034,10 +1039,10 @@ preprocessInfinium2v2 <- function(XY, mixtureSampleSize=10^5,

 				cdfName,

 				sns,

 				stripNorm=TRUE,

-				useTarget=TRUE) { #,

-#				save.it=FALSE,

-#				snpFile,

-#				cnFile) {

+				useTarget=TRUE, # ) { #,

+				save.it=FALSE,

+				snpFile,

+				cnFile) {

   if(stripNorm)

     XY = stripNormalize(XY, useTarget=useTarget, verbose=verbose)

@@ -1069,29 +1074,30 @@ preprocessInfinium2v2 <- function(XY, mixtureSampleSize=10^5,

   #  bIndex <- getVarInEnv("bIndex")

   SMEDIAN <- getVarInEnv("SMEDIAN")

   theKnots <- getVarInEnv("theKnots")

-  gns <- featureNames(XY) # getVarInEnv("gns") # needs to include np probes - gns is only for snps

-  

-  # separate out copy number probes

-  npIndex = getVarInEnv("npProbesFid")

-#  nprobes = length(npIndex)

+#  gns <- featureNames(XY) # getVarInEnv("gns") # needs to include np probes - gns is only for snps

   narrays = ncol(XY)

-#  A <- matrix(as.integer(exprs(channel(XY, "X"))[npIndex,]), nprobes, narrays)

-#  B <- matrix(as.integer(exprs(channel(XY, "Y"))[npIndex,]), nprobes, narrays)

+  

+  if(save.it & !missing(cnFile)) {

+    # separate out copy number probes

+    npIndex = getVarInEnv("npProbesFid")

+    nprobes = length(npIndex)

+    A <- matrix(as.integer(exprs(channel(XY, "X"))[npIndex,]), nprobes, narrays)

+    B <- matrix(as.integer(exprs(channel(XY, "Y"))[npIndex,]), nprobes, narrays)

-  # new lines below - useful to keep track of zeroed out probes

-#  zero <- matrix(as.integer(exprs(channel(XY, "zero"))[npIndex,]), nprobes, narrays) 

+    # new lines below - useful to keep track of zeroed out probes

+    zero <- matrix(as.integer(exprs(channel(XY, "zero"))[npIndex,]), nprobes, narrays) 

-#  colnames(A) <- colnames(B) <- colnames(zero) <- sns

-#  rownames(A) <- rownames(B) <- rownames(zero) <- names(npIndex)

+    colnames(A) <- colnames(B) <- colnames(zero) <- sns

+    rownames(A) <- rownames(B) <- rownames(zero) <- names(npIndex)

-#  cnAB = list(A=A, B=B, zero=zero, sns=sns, gns=names(npIndex), cdfName=cdfName)

-#  if(save.it & !missing(cnFile)) {

-#    t0 <- proc.time() 

-#    save(cnAB, file=cnFile) 

-#    t0 <- proc.time()-t0

-#    if(verbose) message("Used ", round(t0[3],1), " seconds to save ", cnFile, ".")

-#  }

-#  rm(cnAB, B, zero)

+    cnAB = list(A=A, B=B, zero=zero, sns=sns, gns=names(npIndex), cdfName=cdfName)

+    

+    t0 <- proc.time() 

+    save(cnAB, file=cnFile) 

+    t0 <- proc.time()-t0

+    if(verbose) message("Used ", round(t0[3],1), " seconds to save ", cnFile, ".")

+     rm(cnAB, B, zero)

+  }

   # next process snp probes

   snpIndex = getVarInEnv("snpProbesFid")

@@ -1099,12 +1105,12 @@ preprocessInfinium2v2 <- function(XY, mixtureSampleSize=10^5,

   ##We will read each cel file, summarize, and run EM one by one

   ##We will save parameters of EM to use later

-#  mixtureParams <- initializeBigMatrix("crlmmMixt-", 4, length(narrays), "double")

-#  SNR <- initializeBigVector("crlmmSNR-", narrays, "double")

-#  SKW <- initializeBigVector("crlmmSKW-", narrays, "double") 

-  mixtureParams <- matrix(0, 4, narrays)

-  SNR <- vector("numeric", narrays)

-  SKW <- vector("numeric", narrays)

+  mixtureParams <- initializeBigMatrix("crlmmMixt-", 4, narrays, "double")

+  SNR <- initializeBigVector("crlmmSNR-", narrays, "double")

+  SKW <- initializeBigVector("crlmmSKW-", narrays, "double") 

+#  mixtureParams <- matrix(0, 4, narrays)

+#  SNR <- vector("numeric", narrays)

+#  SKW <- vector("numeric", narrays)

   ## This is the sample for the fitting of splines

   ## BC: I like better the idea of the user passing the seed,

@@ -1112,15 +1118,16 @@ preprocessInfinium2v2 <- function(XY, mixtureSampleSize=10^5,

   ##     (like what happened to GCRMA)

   set.seed(seed)

   idx <- sort(sample(autosomeIndex, mixtureSampleSize))

+  idx2 <- sample(nprobes, 10^5)

   ##S will hold (A+B)/2 and M will hold A-B

   ##NOTE: We actually dont need to save S. Only for pics etc...

   ##f is the correction. we save to avoid recomputing

-  A <- exprs(channel(XY, "X"))[,] # ), nrow(XY), narrays) # [snpIndex,]), nprobes, narrays) # matrix(as.integer(0), length(pnsa), length(filenames))

-  B <- exprs(channel(XY, "Y"))[,] # ), nrow(XY), narrays) # [snpIndex,]), nprobes, narrays) # matrix(as.integer(0), length(pnsb), length(filenames))

+#  A <- exprs(channel(XY, "X"))[,] # ), nrow(XY), narrays) # [snpIndex,]), nprobes, narrays) # matrix(as.integer(0), length(pnsa), length(filenames))

+#  B <- exprs(channel(XY, "Y"))[,] # ), nrow(XY), narrays) # [snpIndex,]), nprobes, narrays) # matrix(as.integer(0), length(pnsb), length(filenames))

   # new lines below - useful to keep track of zeroed out SNPs

-  zero <- exprs(channel(XY, "zero"))[,] # )) #[snpIndex,]), nprobes, narrays)

+#  zero <- exprs(channel(XY, "zero"))[,] # )) #[snpIndex,]), nprobes, narrays)

 #  if(!is.matrix(A)) {

 #     A = A[,]

@@ -1128,25 +1135,32 @@ preprocessInfinium2v2 <- function(XY, mixtureSampleSize=10^5,

 #     zero = zero[,]

 #  }

-  if(!is.integer(A)) {

-     A = matrix(as.integer(A), nrow(A), ncol(A))

-     B = matrix(as.integer(B), nrow(B), ncol(B))

-  }  

+#  if(!is.integer(A)) {

+#     A = matrix(as.integer(A), nrow(A), ncol(A))

+#     B = matrix(as.integer(B), nrow(B), ncol(B))

+#  }  

 #  colnames(A) <- colnames(B) <- colnames(zero) <- sns

 #  rownames(A) <- rownames(B) <- rownames(zero) <- names(snpIndex) # gns # featureNames(XY)

+

+  A <- initializeBigMatrix("crlmmA-", nprobes, narrays, "integer")

+  B <- initializeBigMatrix("crlmmB-", nprobes, narrays, "integer")

+  zero <- initializeBigMatrix("crlmmZero-", nprobes, narrays, "integer")

   if(verbose){

      message("Calibrating ", narrays, " arrays.")

      if (getRversion() > '2.7.0') pb <- txtProgressBar(min=0, max=narrays, style=3)

   }

-  idx2 <- sample(nprobes, 10^5)

   for(i in 1:narrays){

-    SKW[i] = mean((A[snpIndex,i][idx2]-mean(A[snpIndex,i][idx2]))^3)/(sd(A[snpIndex,i][idx2])^3)

+     A[,i] = exprs(channel(XY, "X"))[snpIndex,i]

+     B[,i] = exprs(channel(XY, "Y"))[snpIndex,i]

+     zero[,i] = exprs(channel(XY, "zero"))[snpIndex,i] 

+#    SKW[i] = mean((A[snpIndex,i][idx2]-mean(A[snpIndex,i][idx2]))^3)/(sd(A[snpIndex,i][idx2])^3)

+     SKW[i] = mean((A[idx2,i]-mean(A[idx2,i]))^3)/(sd(A[idx2,i])^3)

     if(fitMixture){

-      S <- (log2(A[snpIndex,i][idx])+log2(B[snpIndex,i][idx]))/2 - SMEDIAN

-      M <- log2(A[snpIndex,i][idx])-log2(B[snpIndex,i][idx])

+      S <- (log2(A[idx,i])+log2(B[idx,i]))/2 - SMEDIAN

+      M <- log2(A[idx,i])-log2(B[idx,i])

       ##we need to test the choice of eps.. it is not the max diff between funcs

       tmp <- fitAffySnpMixture56(S, M, theKnots, eps=eps)

@@ -1165,14 +1179,22 @@ preprocessInfinium2v2 <- function(XY, mixtureSampleSize=10^5,

   }

   if (!fitMixture) SNR <- mixtureParams <- NA

   ## gns comes from preprocStuff.rda

-  res = list(A=A, B=B, zero=zero, sns=sns, gns=gns, SNR=SNR, SKW=SKW, mixtureParams=mixtureParams, cdfName=cdfName, snpIndex=snpIndex, npIndex=npIndex)

+  res = list(A=A, B=B,

+             zero=zero, sns=sns, gns=names(snpIndex), SNR=SNR, SKW=SKW,

+             mixtureParams=mixtureParams, cdfName=cdfName) # , snpIndex=snpIndex, npIndex=npIndex)

-#  if(save.it & !missing(snpFile)) {

-#    t0 <- proc.time() 

-#    save(res, file=snpFile) 

-#    t0 <- proc.time()-t0

-#    if(verbose) message("Used ", round(t0[3],1), " seconds to save ", snpFile, ".")

-#  }

+  if(save.it & !missing(snpFile)) {

+    t0 <- proc.time() 

+    save(res, file=snpFile) 

+    t0 <- proc.time()-t0

+    if(verbose) message("Used ", round(t0[3],1), " seconds to save ", snpFile, ".")

+  }

+  close(A)

+  close(B)

+  close(zero)

+  close(SKW)

+  close(mixtureParams)

+  close(SNR)

   return(res)

 }

@@ -1213,8 +1235,8 @@ crlmmIllumina <- function(RG, XY, stripNorm=TRUE, useTarget=TRUE,

         if(!any(obj == "res"))

           stop("Object in ", snpFile, " 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

+  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"]],

@@ -1235,21 +1257,21 @@ crlmmIllumina <- function(RG, XY, stripNorm=TRUE, useTarget=TRUE,

 crlmmIllumina2 <- function(RG, XY, stripNorm=TRUE, useTarget=TRUE,

                   row.names=TRUE, col.names=TRUE,

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

-                  seed=1, # save.it=FALSE, load.it=FALSE, snpFile, cnFile,

+                  seed=1, save.it=FALSE, load.it=FALSE, snpFile, cnFile,

                   mixtureSampleSize=10^5, eps=0.1, verbose=TRUE,

                   cdfName, sns, recallMin=10, recallRegMin=1000,

                   returnParams=FALSE, badSNP=.7) {

-#  if (save.it & (missing(snpFile) | missing(cnFile)))

-#    stop("'snpFile' and/or 'cnFile' is missing and you chose to save.it")

-#  if (load.it & missing(snpFile))

-#    stop("'snpFile' is missing and you chose to load.it")

-#  if (!missing(snpFile))

-#    if (load.it & !file.exists(snpFile)){

-#      load.it <- FALSE

-#      message("File ", snpFile, " does not exist.")

-#      stop("Cannot load SNP data.")

-#  }

-#  if (!load.it){

+  if (save.it & (missing(snpFile) | missing(cnFile)))

+    stop("'snpFile' and/or 'cnFile' is missing and you chose to save.it")

+  if (load.it & missing(snpFile))

+    stop("'snpFile' is missing and you chose to load.it")

+  if (!missing(snpFile))

+    if (load.it & !file.exists(snpFile)){

+      load.it <- FALSE

+      message("File ", snpFile, " does not exist.")

+      stop("Cannot load SNP data.")

+  }

+  if (!load.it){

     if(!missing(RG)) {

       if(missing(XY))

         XY = RGtoXY2(RG, chipType=cdfName)

@@ -1262,48 +1284,51 @@ crlmmIllumina2 <- function(RG, XY, stripNorm=TRUE, useTarget=TRUE,

                         seed=seed, eps=eps, cdfName=cdfName, sns=sns, stripNorm=stripNorm, useTarget=useTarget) #,

                       #  save.it=save.it, snpFile=snpFile, cnFile=cnFile)

-    fD = featureData(XY)

-    phenD = XY@phenoData

-    protD = XY@protocolData

-    rm(XY)

-    gc()

-    if(verbose) message("Initializing container for alleleA, alleleB, call, callProbability")

-    callSet <- new("SnpSuperSet",

-                    alleleA=initializeBigMatrix(name="A", nr=nrow(res[[1]]), nc=length(sns)),

-                    alleleB=initializeBigMatrix(name="B", nr=nrow(res[[1]]), nc=length(sns)),

-                    call=initializeBigMatrix(name="call", nr=nrow(res[[1]]), nc=length(sns)),

-                    callProbability=initializeBigMatrix(name="callPr", nr=nrow(res[[1]]), nc=length(sns)),

-  	            annotation=cdfName, protocolData=protD, phenoData=phenD, featureData=fD) 

-    sampleNames(callSet) <- sns

-    featureNames(callSet) <- res[["gns"]]

-    pData(callSet)$SKW <- rep(NA, length(sns))

-    pData(callSet)$SNR <- rep(NA, length(sns))

-    pData(callSet)$gender <- rep(NA, length(sns))

+#    fD = featureData(XY)

+#    phenD = XY@phenoData

+#    protD = XY@protocolData

+#    rm(XY)

+#    gc()

+#    if(verbose) message("Initializing container for alleleA, alleleB, call, callProbability")

+#    callSet <- new("SnpSuperSet",

+#                    alleleA=initializeBigMatrix(name="A", nr=nrow(res[[1]]), nc=length(sns)),

+#                    alleleB=initializeBigMatrix(name="B", nr=nrow(res[[1]]), nc=length(sns)),

+#                    call=initializeBigMatrix(name="call", nr=nrow(res[[1]]), nc=length(sns)),

+#                    callProbability=initializeBigMatrix(name="callPr", nr=nrow(res[[1]]), nc=length(sns)),

+#  	            annotation=cdfName, protocolData=protD, phenoData=phenD, featureData=fD) 

+#    sampleNames(callSet) <- sns

+#    featureNames(callSet) <- res[["gns"]]

+#    pData(callSet)$SKW <- rep(NA, length(sns))

+#    pData(callSet)$SNR <- rep(NA, length(sns))

+#    pData(callSet)$gender <- rep(NA, length(sns))

-#  }else{

-#      if(verbose) message("Loading ", snpFile, ".")

-#        obj <- load(snpFile)

-#        if(verbose) message("Done.")

-#        if(!any(obj == "res"))

-#          stop("Object in ", snpFile, " seems to be invalid.")

-#  }

+  }else{

+      if(verbose) message("Loading ", snpFile, ".")

+        obj <- load(snpFile)

+        if(verbose) message("Done.")

+        if(!any(obj == "res"))

+          stop("Object in ", snpFile, " seems to be invalid.")

+  }

-    rm(phenD, protD , fD)

+ #   rm(phenD, protD , fD)

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

-    suppressWarnings(A(callSet) <- res[["A"]])

-    suppressWarnings(B(callSet) <- res[["B"]])

-    pData(callSet)$SKW <- res$SKW

-    pData(callSet)$SNR <- res$SNR

-    mixtureParams <- res$mixtureParams

-    rm(res); gc()

-    tmp <- crlmmGT(A=as.matrix(A(callSet)[snp.index,]), # j]),

-                  B=as.matrix(B(callSet)[snp.index,]),  # j]),

-                  SNR=callSet$SNR, # [j],

-                  mixtureParams=mixtureParams,

-                  cdfName=annotation(callSet),

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

-#                  col.names=sampleNames(callSet), #[j],

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

+#    suppressWarnings(A(callSet) <- res[["A"]])

+#    suppressWarnings(B(callSet) <- res[["B"]])

+#    pData(callSet)$SKW <- res$SKW

+#    pData(callSet)$SNR <- res$SNR

+#    mixtureParams <- res$mixtureParams

+#    rm(res); gc()

+  if(row.names) row.names=res$gns else row.names=NULL

+  if(col.names) col.names=res$sns else col.names=NULL

+  

+  res2 <- crlmmGT2(A=res[["A"]], #as.matrix(A(callSet)[snp.index,]), # j]),

+                  B=res[["B"]], # as.matrix(B(callSet)[snp.index,]),  # j]),

+                  SNR=res[["SNR"]], # callSet$SNR, # [j],

+                  mixtureParams=res[["mixtureParams"]], #,

+                  cdfName=res[["cdfName"]], # annotation(callSet),

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

+                  col.names=col.names, # sampleNames(callSet), #[j],

                   probs=probs,

                   DF=DF,

                   SNRMin=SNRMin,

@@ -1313,14 +1338,20 @@ crlmmIllumina2 <- function(RG, XY, stripNorm=TRUE, useTarget=TRUE,

                   verbose=verbose,

                   returnParams=returnParams,

                   badSNP=badSNP)

+#    rm(res); gc()  

 #    suppressWarnings(snpCall(callSet)[snp.index, j] <- tmp[["calls"]])

 #    suppressWarnings(snpCallProbability(callSet)[snp.index, j] <- tmp[["confs"]])

 #    callSet$gender[j] <- tmp$gender

-    suppressWarnings(snpCall(callSet)[snp.index,] <- tmp[["calls"]])

-    suppressWarnings(snpCallProbability(callSet)[snp.index,] <- tmp[["confs"]])

-    callSet$gender <- tmp$gender

-    rm(tmp); gc()

-    return(callSet)

+#    suppressWarnings(snpCall(callSet)[snp.index,] <- tmp[["calls"]])

+#    suppressWarnings(snpCallProbability(callSet)[snp.index,] <- tmp[["confs"]])

+#    callSet$gender <- tmp$gender

+#    rm(tmp); gc()

+#    return(callSet)

+

+  res2[["SNR"]] <- res[["SNR"]]

+  res2[["SKW"]] <- res[["SKW"]]

+  rm(res); gc()

+  return(list2SnpSet(res2, returnParams=returnParams))

 }

@@ -1343,7 +1374,7 @@ crlmmIlluminaV2 = function(sampleSheet=NULL,

           	          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,

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

                           mixtureSampleSize=10^5, eps=0.1, verbose=TRUE,

                           cdfName, sns, recallMin=10, recallRegMin=1000,

                           returnParams=FALSE, badSNP=.7) {

@@ -1354,8 +1385,8 @@ crlmmIlluminaV2 = function(sampleSheet=NULL,

 #  if (save.rg & missing(rgFile))

 #    stop("'rgFile' is missing, and you chose save.rg")

-#  if (save.ab & (missing(snpFile) | missing(cnFile)))

-#    stop("'snpFile' or 'cnFile' is missing and you chose save.ab")

+  if (save.ab & (missing(snpFile) | missing(cnFile)))

+    stop("'snpFile' or 'cnFile' is missing and you chose save.ab")

 #  batches = NULL

 #  if(!is.null(arrayNames))

 #    batches <- rep(1, length(arrayNames)) # problem here if arrayNames not specified! # splitIndicesByLength(seq(along=arrayNames), ocSamples())

@@ -1377,27 +1408,25 @@ crlmmIlluminaV2 = function(sampleSheet=NULL,

 #	save(RG, file=rgFile)

     XY = RGtoXY2(RG, chipType=cdfName)

-    rm(RG)

-    gc()

+    rm(RG); gc()

     if (missing(sns)) { sns = sampleNames(XY) #subsns = sampleNames(XY)

-    } else subsns = sns[j]

+    } # else subsns = sns[j]

     res = preprocessInfinium2v2(XY, mixtureSampleSize=mixtureSampleSize, fitMixture=TRUE, verbose=verbose,

-                               seed=seed, eps=eps, cdfName=cdfName, sns=sns, stripNorm=stripNorm, useTarget=useTarget) # sns=subsns

-#                          save.it=save.ab, snpFile=snpFile, cnFile=cnFile)

-    fD = featureData(XY)

-    phenD = XY@phenoData

-    protD = XY@protocolData

-    rm(XY)

-    gc()

+                               seed=seed, eps=eps, cdfName=cdfName, sns=sns, stripNorm=stripNorm, useTarget=useTarget, #) # sns=subsns

+                               save.it=save.ab, snpFile=snpFile, cnFile=cnFile)

+#    fD = featureData(XY)

+#    phenD = XY@phenoData

+#    protD = XY@protocolData

+    rm(XY); gc()

 #    if(k == 1){

-    if(verbose) message("Initializing container for alleleA, alleleB, call, callProbability")

-    callSet <- new("SnpSuperSet",

-                    alleleA=initializeBigMatrix(name="A", nr=nrow(res[[1]]), nc=length(sns)),

-                    alleleB=initializeBigMatrix(name="B", nr=nrow(res[[1]]), nc=length(sns)),

-                    call=initializeBigMatrix(name="call", nr=nrow(res[[1]]), nc=length(sns)),

-                    callProbability=initializeBigMatrix(name="callPr", nr=nrow(res[[1]]), nc=length(sns)),

- 		    annotation=cdfName, protocolData=protD, phenoData=phenD, featureData=fD)

-    sampleNames(callSet) <- sns

+#    if(verbose) message("Initializing container for alleleA, alleleB, call, callProbability")

+#    callSet <- new("SnpSuperSet",

+#                    alleleA=initializeBigMatrix(name="A", nr=nrow(res[[1]]), nc=length(sns)),

+#                    alleleB=initializeBigMatrix(name="B", nr=nrow(res[[1]]), nc=length(sns)),

+#                    call=initializeBigMatrix(name="call", nr=nrow(res[[1]]), nc=length(sns)),

+#                    callProbability=initializeBigMatrix(name="callPr", nr=nrow(res[[1]]), nc=length(sns)),

+# 		    annotation=cdfName, protocolData=protD, phenoData=phenD, featureData=fD)

+#    sampleNames(callSet) <- sns

 #            phenoData(callSet) <- getPhenoData(sampleSheet=sampleSheet,

 #                                    arrayNames=sns,

 #								    arrayInfoColNames=arrayInfoColNames)

@@ -1405,17 +1434,17 @@ crlmmIlluminaV2 = function(sampleSheet=NULL,

 #            colnames(pD) <- "ScanDate"

 #            protocolData(callSet) <- pData(protD) # new("AnnotatedDataFrame", data=pD)

 #            pData(protocolData(callSet))[j, ] <- pData(protocolData)

-    featureNames(callSet) <- res[["gns"]]

-    pData(callSet)$SKW <- rep(NA, length(sns))

-    pData(callSet)$SNR <- rep(NA, length(sns))

-    pData(callSet)$gender <- rep(NA, length(sns))			

+#    featureNames(callSet) <- res[["gns"]]

+#    pData(callSet)$SKW <- rep(NA, length(sns))

+#    pData(callSet)$SNR <- rep(NA, length(sns))

+#    pData(callSet)$gender <- rep(NA, length(sns))			

 #	}

 #	pData(callSet)[j,] <- phenD

 #	pData(protocolData(callSet))[j,] <- protD

 #	pData(callSet) <- phenD

 #	pData(protocolData(callSet)) <- protD

-    rm(phenD, protD, fD)

+#    rm(phenD, protD, fD)

 #    if(k > 1 & nrow(res[[1]]) != nrow(callSet)){

         ##RS: I don't understand why the IDATS for the

@@ -1424,24 +1453,26 @@ crlmmIlluminaV2 = function(sampleSheet=NULL,

 #        res[["B"]] <- res[["B"]][res$gns %in% featureNames(callSet), ]

 #    }

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

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

 #    suppressWarnings(A(callSet)[, j] <- res[["A"]])

 #    suppressWarnings(B(callSet)[, j] <- res[["B"]])

-    suppressWarnings(A(callSet) <- res[["A"]])

-    suppressWarnings(B(callSet) <- res[["B"]])

+#    suppressWarnings(A(callSet) <- res[["A"]])

+#    suppressWarnings(B(callSet) <- res[["B"]])

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

 #    pData(callSet)$SNR[j] <- res$SNR

-    pData(callSet)$SKW <- res$SKW

-    pData(callSet)$SNR <- res$SNR

-    mixtureParams <- res$mixtureParams

-    rm(res); gc()

-    tmp <- crlmmGT(A=as.matrix(A(callSet)[snp.index,]), # j]),

-                  B=as.matrix(B(callSet)[snp.index,]),  # j]),

-                  SNR=callSet$SNR, # [j],

-                  mixtureParams=mixtureParams,

-                  cdfName=annotation(callSet),

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

-                  col.names=sampleNames(callSet), #[j],

+#    pData(callSet)$SKW <- res$SKW

+#    pData(callSet)$SNR <- res$SNR

+#    mixtureParams <- res$mixtureParams

+#    rm(res); gc()

+  if(row.names) row.names=res$gns else row.names=NULL

+  if(col.names) col.names=res$sns else col.names=NULL    

+  res2 <- crlmmGT2(A=res[["A"]], #as.matrix(A(callSet)[snp.index,]), # j]),

+                  B=res[["B"]], # as.matrix(B(callSet)[snp.index,]),  # j]),

+                  SNR=res[["SNR"]], # callSet$SNR, # [j],

+                  mixtureParams=res[["mixtureParams"]], #,

+                  cdfName=res[["cdfName"]], # annotation(callSet),

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

+                  col.names=col.names, # sampleNames(callSet), #[j],

                   probs=probs,

                   DF=DF,

                   SNRMin=SNRMin,

@@ -1451,14 +1482,44 @@ crlmmIlluminaV2 = function(sampleSheet=NULL,

                   verbose=verbose,

                   returnParams=returnParams,

                   badSNP=badSNP)

+#    rm(res); gc()  

+#    suppressWarnings(snpCall(callSet)[snp.index, j] <- tmp[["calls"]])

+#    suppressWarnings(snpCallProbability(callSet)[snp.index, j] <- tmp[["confs"]])

+#    callSet$gender[j] <- tmp$gender

+#    suppressWarnings(snpCall(callSet)[snp.index,] <- tmp[["calls"]])

+#    suppressWarnings(snpCallProbability(callSet)[snp.index,] <- tmp[["confs"]])

+#    callSet$gender <- tmp$gender

+#    rm(tmp); gc()

+#    return(callSet)

+

+  res2[["SNR"]] <- res[["SNR"]]

+  res2[["SKW"]] <- res[["SKW"]]

+  rm(res); gc()

+  return(list2SnpSet(res2, returnParams=returnParams))

+#    tmp <- crlmmGT(A=as.matrix(A(callSet)[snp.index,]), # j]),

+#                  B=as.matrix(B(callSet)[snp.index,]),  # j]),

+#                  SNR=callSet$SNR, # [j],

+#                  mixtureParams=mixtureParams,

+#                  cdfName=annotation(callSet),

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

+#                  col.names=sampleNames(callSet), #[j],

+#                  probs=probs,

+#                  DF=DF,

+#                  SNRMin=SNRMin,

+#                  recallMin=recallMin,

+#                  recallRegMin=recallRegMin,

+#                  gender=gender,

+#                  verbose=verbose,

+#                  returnParams=returnParams,

+#                  badSNP=badSNP)

 #    suppressWarnings(snpCall(callSet)[snp.index, j] <- tmp[["calls"]])

 #    suppressWarnings(snpCallProbability(callSet)[snp.index, j] <- tmp[["confs"]])

 #    callSet$gender[j] <- tmp$gender

-    suppressWarnings(snpCall(callSet)[snp.index,] <- tmp[["calls"]])

-    suppressWarnings(snpCallProbability(callSet)[snp.index,] <- tmp[["confs"]])

-    callSet$gender <- tmp$gender

-    rm(tmp); gc()

+#    suppressWarnings(snpCall(callSet)[snp.index,] <- tmp[["calls"]])

+#    suppressWarnings(snpCallProbability(callSet)[snp.index,] <- tmp[["confs"]])

+#    callSet$gender <- tmp$gender

+#    rm(tmp); gc()

 #    k <- k+1

 #  }

-    return(callSet)

+#    return(callSet)

 }

Binary files a/inst/doc/crlmmIllumina.pdf and b/inst/doc/crlmmIllumina.pdf differ

@@ -44,12 +44,15 @@ In this user guide we read in and genotype data from 40 HapMap samples

 which have been analyzed using Illumina's 370k Duo BeadChips.

 This data is available in the \Rpackage{hapmap370k} package.  

 Additional chip-specific model parameters and basic SNP annotation 

-information used by CRLMM is stored in the \Rpackage{human370v1c} package.

-These can be downloaded from \href{http://rafalab.jhsph.edu/software.html}{http://rafalab.jhsph.edu/software.html}

-and must be installed for the following code to work.

+information used by CRLMM is stored in the \Rpackage{human370v1cCrlmm} package.

+These packages can be installed in the usual way using the \Rfunction{biocLite} function.

-\section{Reading in data}

+<<<echo=TRUE, results=hide, eval=FALSE>>=

+source("http://www.bioconductor.org/biocLite.R")

+biocLite(c("hapmap370k", "human370v1cCrlmm"))

+@ 

+\section{Reading in data} 

 The function \Rfunction{readIdatFiles} extracts the Red and Green intensities 

 from the binary {\tt idat} files output by Illumina's scanning device.

 The file {\tt samples370k.csv} contains information about each sample.

@@ -58,7 +61,7 @@ The file {\tt samples370k.csv} contains information about each sample.

 options(width=50)

 @ 

-<<read>>=

+<<read, results=hide, eval=TRUE>>=

 library(Biobase)

 library(crlmm)

 library(hapmap370k)

@@ -112,8 +115,7 @@ mtext("Array", side=1, outer=TRUE)

 \section{Genotyping}

-Next we use the function \Rfunction{crlmmIllumina} which performs preprocessing followed by genotyping

-using the CRLMM algorithm.

+Next we use the function \Rfunction{crlmmIllumina} which performs preprocessing followed by genotyping using the CRLMM algorithm.

 <<genotype, results=hide, cache=TRUE>>=

 crlmmResult = crlmmIllumina(RG=RG, cdfName="human370v1c", sns=pData(RG)$ID, returnParams=TRUE)