@@ -561,6 +561,7 @@ function (which expects ff objects and supports parallel processing)

 ** copy number A and B intensities now stored in callSet from genotype.Illumina()

-2010-10-06 M. Ritchie 1.7.18

+2010-10-06 M. Ritchie 1.7.19

 ** new internal function processIDAT which uses ocLapply() to parallelize pre-processing of Illumina data

 ** changes to genotype.Illumina()

+** updated vignettes - crlmmIllumina.Rnw and crlmmIllumina.pdf

@@ -510,6 +510,9 @@ RGtoXY = function(RG, chipType, verbose=TRUE) {

   is.lds = ifelse(isPackageLoaded("ff"), TRUE, FALSE)

   if(is.lds) {

+    close(RG@assayData$G)

+    close(RG@assayData$R)

+    close(RG@assayData$zero)    

     close(XY@assayData$X)

     close(XY@assayData$Y)

     close(XY@assayData$zero)

@@ -722,6 +725,9 @@ preprocessInfinium2 <- function(XY, mixtureSampleSize=10^5,

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

 #  }

   if(is.lds) {

+    close(XY@assayData$X)

+    close(XY@assayData$Y)

+    close(XY@assayData$zero)    

     close(A)

     close(B)

     close(zero)

@@ -888,6 +894,12 @@ crlmmIllumina <- function(RG, XY, stripNorm=TRUE, useTarget=TRUE,

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

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

+#  if(is.lds) {

+#    delete(res[["A"]])

+#    delete(res[["B"]])

+#    delete(res[["SNR"]])

+#    delete(res[["mixtureParams"]])

+#  }

   rm(res); gc()

   return(list2SnpSet(res2, returnParams=returnParams))

 }

@@ -917,6 +929,9 @@ crlmmIlluminaV2 = function(sampleSheet=NULL,

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

   if(!isValidCdfName(cdfName)) stop("cdfName not valid.  see validCdfNames")

+  

+  is.lds = ifelse(isPackageLoaded("ff"), TRUE, FALSE)

+

 #  if(missing(sns)) sns <- basename(arrayNames)

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

@@ -944,25 +959,33 @@ crlmmIlluminaV2 = function(sampleSheet=NULL,

 #	save(RG, file=rgFile)

     XY = RGtoXY(RG, chipType=cdfName)

+    if(is.lds) {

+      open(RG@assayData$R); open(RG@assayData$G); open(RG@assayData$zero)

+      delete(RG@assayData$R); delete(RG@assayData$G); delete(RG@assayData$zero)

+    }

     rm(RG); gc()

+  

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

     } # else subsns = sns[j]

     res = preprocessInfinium2(XY, mixtureSampleSize=mixtureSampleSize, fitMixture=TRUE, verbose=verbose,

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

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

-    is.lds = ifelse(isPackageLoaded("ff"), TRUE, FALSE)

-

-    if(is.lds) {

-      open(res[["A"]])

-      open(res[["B"]])

-      open(res[["SNR"]])

-      open(res[["mixtureParams"]])

-    }

+#    if(is.lds) {

+#      open(res[["A"]])

+#      open(res[["B"]])

+#      open(res[["SNR"]])

+#      open(res[["mixtureParams"]])

+#    }

 #    fD = featureData(XY)

 #    phenD = XY@phenoData

 #    protD = XY@protocolData

+

+    if(is.lds) {

+      open(XY@assayData$X); open(XY@assayData$Y); open(XY@assayData$zero)

+      delete(XY@assayData$X); delete(XY@assayData$Y); delete(XY@assayData$zero)

+    }

     rm(XY); gc()

 #    if(k == 1){

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

@@ -1063,9 +1086,16 @@ crlmmIlluminaV2 = function(sampleSheet=NULL,

 #    callSet$gender <- tmp$gender

 #    rm(tmp); gc()

 #    return(callSet)

+  if(is.lds) {

+     open(res[["SNR"]]); open(res[["SKW"]])

+  }

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

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

+#  if(is.lds) {

+#    delete(res[["A"]]); delete(res[["B"]])

+#    delete(res[["SKW"]]); delete(res[["SNR"]]); delete(res[["mixtureParams"]])

+#  }

   rm(res); gc()

   return(list2SnpSet(res2, returnParams=returnParams))

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

@@ -1353,6 +1383,8 @@ genotype.Illumina <- function(sampleSheet=NULL,

           bb = ocProbesets()*length(sns)*8

 	  ffrowapply(tmpA[i1:i2, ] <- A(callSet)[snp.index,][i1:i2, ], X=A(callSet)[snp.index,], BATCHBYTES=bb)

 	  ffrowapply(tmpB[i1:i2, ] <- B(callSet)[snp.index,][i1:i2, ], X=B(callSet)[snp.index,], BATCHBYTES=bb)

+          close(A(callSet))

+          close(B(callSet))

           close(tmpA)

           close(tmpB)

         } else {

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

@@ -45,11 +45,11 @@ 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{human370v1cCrlmm} package.

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

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

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

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

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

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

 @ 

 \section{Reading in data} 

@@ -80,6 +80,8 @@ RG = readIdatFiles(samples, path=data.dir, arrayInfoColNames=list(barcode=NULL,

 Reading in this data takes approximately 90 seconds and peak memory usage 

 was 1.2 GB of RAM on our linux system.

+If memory is limiting, load the \Rpackage{ff} package and run the same command.

+When this package is available, the objects are stored using disk rather then RAM.

 The \Robject{RG} object is an \Rclass{NChannelSet} which stores the 

 Red and Green intensities, the number of beads and standard errors for 

 each bead-type.