@@ -43,6 +43,7 @@

 \section{Set up}

 <<crlmm, results=hide, echo=FALSE>>=

+library(Biobase)

 library(crlmm)

 options(width=70)

 options(continue=" ")

@@ -238,7 +239,6 @@ cnSet <- genotype.Illumina(sampleSheet=samplesheet,

 			   arrayInfoColNames=arrayInfo,

 			   cdfName="human370v1c",

 			   batch=batch)

-stop()

 @

@@ -238,6 +238,7 @@ cnSet <- genotype.Illumina(sampleSheet=samplesheet,

 			   arrayInfoColNames=arrayInfo,

 			   cdfName="human370v1c",

 			   batch=batch)

+stop()

 @

@@ -6,6 +6,7 @@

 \usepackage{graphicx}

 \usepackage{natbib}

 \usepackage{amsmath}

+\usepackage{url}

 \usepackage[margin=1in]{geometry}

 \newcommand{\crlmm}{\Rpackage{crlmm}}

 \newcommand{\Rfunction}[1]{{\texttt{#1}}}

@@ -233,10 +234,10 @@ wrapper to the above functions and returns an object of class

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

 cnSet <- genotype.Illumina(sampleSheet=samplesheet,

-			    arrayNames=arrayNames,

-			    arrayInfoColNames=arrayInfo,

-			    cdfName="human370v1c",

-			    batch=batch)

+			   arrayNames=arrayNames,

+			   arrayInfoColNames=arrayInfo,

+			   cdfName="human370v1c",

+			   batch=batch)

 @

@@ -60,6 +60,7 @@ calls.

 <<ldpath,results=hide>>=

 library(ff)

+options(ffcaching="ffeachflush")

 outdir <- paste("/local_data/r00/crlmm/", getRversion(), "/illumina_vignette", sep="")

 ldPath(outdir)

 dir.create(outdir, recursive=TRUE, showWarnings=FALSE)

@@ -60,10 +60,7 @@ calls.

 <<ldpath,results=hide>>=

 library(ff)

-if(getRversion() < "2.13.0"){

-	rpath <- getRversion()

-} else rpath <- "trunk"

-outdir <- paste("/thumper/ctsa/snpmicroarray/rs/ProcessedData/crlmm/", rpath, "/illumina_vignette", sep="")

+outdir <- paste("/local_data/r00/crlmm/", getRversion(), "/illumina_vignette", sep="")

 ldPath(outdir)

 dir.create(outdir, recursive=TRUE, showWarnings=FALSE)

 @

@@ -5,6 +5,7 @@

 \documentclass{article}

 \usepackage{graphicx}

 \usepackage{natbib}

+\usepackage{amsmath}

 \usepackage[margin=1in]{geometry}

 \newcommand{\crlmm}{\Rpackage{crlmm}}

 \newcommand{\Rfunction}[1]{{\texttt{#1}}}

@@ -84,15 +85,15 @@ ocProbesets(150e3)

 ocSamples(500)

 @

-\section{Initializing a container for storing processed data}

-

-This section will initialize a container for storing processed forms

-of the data, including the normalized intensities for the A and B

-alleles and the CRLMM genotype calls and confidence scores.  In

-addition, the container will store information on the markers

-(physical position, chromosome, and a SNP indicator), the batch, and

-the samples (e.g., gender).  To construct this container for Infinium

-platforms, several steps are required.

+%\section{Initializing a container for storing processed data}

+%

+%This section will initialize a container for storing processed forms

+%of the data, including the normalized intensities for the A and B

+%alleles and the CRLMM genotype calls and confidence scores.  In

+%addition, the container will store information on the markers

+%(physical position, chromosome, and a SNP indicator), the batch, and

+%the samples (e.g., gender).  To construct this container for Infinium

+%platforms, several steps are required.

 We begin by specifying the path containing the raw IDAT files for a

 set of samples from the Infinium 370k platform.

@@ -143,129 +144,140 @@ processed at similar times (e.g., within a few weeks).

 batch <- rep("1", nrow(samplesheet))

 @

-Finally, we initialize an object of class \Robject{CNSet} using the

-function \Rfunction{constructInf}.

-

-<<container,cache=TRUE>>=

-cnSet <- constructInf(sampleSheet=samplesheet,

-		      arrayNames=arrayNames,

-		      batch=batch,

-		      arrayInfoColNames=arrayInfo,

-		      cdfName=cdfName,

-		      verbose=TRUE,

-		      saveDate=TRUE)

-@

-

-A concise summary of the object's contents can be viewed with the

-\Rfunction{print} function.

-

-<<cnset>>=

-print(cnSet)

-@

-

-Note that the above object does not yet contain any processed data

-(only \verb+NA+'s).  As the elements of the \verb+assayData+ slot are

-\Robject{ff} objects (not matrices), several \verb+.ff+ files now

-appear in the \verb+outdir+. The \verb+.ff+ files should not be

-removed and can be listed using the \R{} function

-\Rfunction{list.files}.  %For the most part, the \emph{appearance} that

+%Finally, we initialize an object of class \Robject{CNSet} using the

+%function \Rfunction{constructInf}.

+%

+%<<container,cache=TRUE>>=

+%cnSet <- constructInf(sampleSheet=samplesheet,

+%		      arrayNames=arrayNames,

+%		      batch=batch,

+%		      arrayInfoColNames=arrayInfo,

+%		      cdfName=cdfName,

+%		      verbose=TRUE,

+%		      saveDate=TRUE)

+%@

+%

+%A concise summary of the object's contents can be viewed with the

+%\Rfunction{print} function.

+%

+%<<cnset>>=

+%print(cnSet)

+%@

+%

+%Note that the above object does not yet contain any processed data

+%(only \verb+NA+'s).  As the elements of the \verb+assayData+ slot are

+%\Robject{ff} objects (not matrices), several \verb+.ff+ files now

+%appear in the \verb+outdir+. The \verb+.ff+ files should not be

+%removed and can be listed using the \R{} function

+%\Rfunction{list.files}.  %For the most part, the \emph{appearance} that

 %the data is stored in memory is preserved.

-

-<<listff>>=

-sapply(assayData(cnSet), function(x) class(x)[1])

-list.files(outdir, pattern=".ff")[1:5]

-@

-

-\section{Preprocessing}

+%

+%<<listff>>=

+%sapply(assayData(cnSet), function(x) class(x)[1])

+%list.files(outdir, pattern=".ff")[1:5]

+%@

+%

+\section{Preprocessing and genotyping}

 The raw intensities from the Infinium IDAT files are read and

 normalized using the function \Rfunction{preprocessInf}.  The function

 \Rfunction{preprocessInf} returns a \Robject{ff} object containing the

 parameters for the mixture model used by the CRLMM genotyping

-algorithm.

-

-<<preprocess,cache=TRUE, results=hide>>=

-mixtureParams <- preprocessInf(cnSet=cnSet, sampleSheet=samplesheet, arrayNames=arrayNames, arrayInfoColNames=arrayInfo)

-@

-

-<<showMixtureParams>>=

-invisible(open(mixtureParams))

-str(mixtureParams[])

-invisible(close(mixtureParams))

-@

-

-Note that the normalized intensities for the A and B alleles are no

-longer \verb+NA+s and can be inspected using the methods \Rfunction{A}

-and \Rfunction{B}, respectively.

-

-<<intensities>>=

-invisible(open(A(cnSet)))

-invisible(open(B(cnSet)))

-as.matrix(A(cnSet)[1:5, 1:5])

-as.matrix(B(cnSet)[1:5, 1:5])

-invisible(close(A(cnSet)))

-invisible(close(B(cnSet)))

-@

-

-\section{Genotyping}

-

-CRLMM genotype calls and confidence scores are estimated using the

-function \Rfunction{genotypeInf}.

-

-<<genotype,cache=TRUE>>=

-updated <- genotypeInf(cnSet, mixtureParams=mixtureParams)

-@

-\vspace{-0.5em}

-<<>>=

-updated

-@

-

-\textbf{Wrapper:} As an alternative to calling the functions

-\Rfunction{constructInf}, \Rfunction{preprocessInf} and

-\Rfunction{genotypeInf} in sequence, a convenience function called

-\Rfunction{genotype.Illumina} is a wrapper for the above functions and

-produces identical results.

+algorithm.  Following preprocessing, the \Rfunction{genotypeInf}

+genotypes the samples. The function \Rfunction{genotype.Illumina} is a

+wrapper to the above functions and returns an object of class

+\Rclass{CNSet}.

+

+%<<preprocess,cache=TRUE, results=hide>>=

+%mixtureParams <- preprocessInf(cnSet=cnSet, sampleSheet=samplesheet,

+%			       arrayNames=arrayNames,

+%			       arrayInfoColNames=arrayInfo,

+%			       cdfName=cdfName)

+%@

+%

+%<<showMixtureParams>>=

+%invisible(open(mixtureParams))

+%str(mixtureParams[])

+%invisible(close(mixtureParams))

+%@

+%

+%Note that the normalized intensities for the A and B alleles are no

+%longer \verb+NA+s and can be inspected using the methods \Rfunction{A}

+%and \Rfunction{B}, respectively.

+%

+%<<intensities>>=

+%invisible(open(A(cnSet)))

+%invisible(open(B(cnSet)))

+%as.matrix(A(cnSet)[1:5, 1:5])

+%as.matrix(B(cnSet)[1:5, 1:5])

+%invisible(close(A(cnSet)))

+%invisible(close(B(cnSet)))

+%@

+%

+%\section{Genotyping}

+%

+%CRLMM genotype calls and confidence scores are estimated using the

+%function \Rfunction{genotypeInf}.

+%

+%<<genotype,cache=TRUE>>=

+%updated <- genotypeInf(cnSet, mixtureParams=mixtureParams, cdfName=cdfName)

+%@

+%\vspace{-0.5em}

+%<<>>=

+%updated

+%@

+%

+%\textbf{Wrapper:} As an alternative to calling the functions

+%\Rfunction{constructInf}, \Rfunction{preprocessInf} and

+%\Rfunction{genotypeInf} in sequence, a convenience function called

+%\Rfunction{genotype.Illumina} is a wrapper for the above functions and

+%produces identical results.

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

-cnSet2 <- genotype.Illumina(sampleSheet=samplesheet,

+cnSet <- genotype.Illumina(sampleSheet=samplesheet,

 			    arrayNames=arrayNames,

 			    arrayInfoColNames=arrayInfo,

 			    cdfName="human370v1c",

 			    batch=batch)

 @

-<<checkIdenticalCalls>>=

-invisible(open(calls(cnSet)))

-invisible(open(calls(cnSet2)))

-snp.index <- which(isSnp(cnSet))

-identical(calls(cnSet)[snp.index, 1:20], calls(cnSet2)[snp.index, 1:20])

-invisible(close(calls(cnSet)))

-invisible(close(calls(cnSet2)))

-@

-To fully remove the data associated with the \Robject{cnSet2} object,

-one should use the \Rfunction{delete} function in the \Rpackage{ff}

-package followed by the \Rfunction{rm} function.  The following code

-is not evaluated is it would change the results of the cached

-computations in the previous code chunk.

+Note, to fully remove the data associated with the \Robject{cnSet2}

+object, one should use the \Rfunction{delete} function in the

+\Rpackage{ff} package followed by the \Rfunction{rm} function.  The

+following code is not evaluated is it would change the results of the

+cached computations in the previous code chunk.

 <<delete, eval=FALSE>>=

-lapply(assayData(cnSet2), delete)

-lapply(batchStatistics(cnSet2), delete)

-delete(cnSet2$gender)

-delete(cnSet2$SNR)

-delete(cnSet2$SKW)

-rm(cnSet2)

+lapply(assayData(cnSet), delete)

+lapply(batchStatistics(cnSet), delete)

+delete(cnSet$gender)

+delete(cnSet$SNR)

+delete(cnSet$SKW)

+rm(cnSet)

 @

-Users can proceed to the \verb+copynumber+ vignette for copy number

-analyses.  See the \verb+Infrastructure+ vignette for additional

-details on the \Robject{CNSet} class, including an overview of the

-available accessors.

+%\section{Copy number estimation}

+

+\SweaveInput{copynumber}

 \section{Session information}

 <<sessionInfo, results=tex>>=

 toLatex(sessionInfo())

 @

+\begin{figure}[f]

+  \begin{center}

+  \includegraphics[width=0.6\textwidth]{IlluminaPreprocessCN-snr.pdf}

+  \caption{The signal to noise ratio (SNR) for 180 HapMap samples. For

+    Affymetrix platforms, SNR values below 5 can indicate possible

+    problems with sample quality.  In some circumstances, it may be

+    more helpful to exclude samples with poor DNA quality.}

+\end{center}

+\end{figure}

+

+

+\bibliography{refs}

+\bibliographystyle{plain}

+

 \end{document}

@@ -184,8 +184,11 @@ normalized using the function \Rfunction{preprocessInf}.  The function

 parameters for the mixture model used by the CRLMM genotyping

 algorithm.

-<<preprocess,cache=TRUE>>=

+<<preprocess,cache=TRUE, results=hide>>=

 mixtureParams <- preprocessInf(cnSet=cnSet, sampleSheet=samplesheet, arrayNames=arrayNames, arrayInfoColNames=arrayInfo)

+@

+

+<<showMixtureParams>>=

 invisible(open(mixtureParams))

 str(mixtureParams[])

 invisible(close(mixtureParams))

@@ -211,20 +214,10 @@ function \Rfunction{genotypeInf}.

 <<genotype,cache=TRUE>>=

 updated <- genotypeInf(cnSet, mixtureParams=mixtureParams)

-print(updated)

 @

-

-The posterior probabilities for the genotype calls in the

-\verb+callProbability+ element of the \verb+assayData+ are stored as

-integers to reduce the file size on disk. The scores can be

-transformed to the probability scale using the \Rfunction{i2p}

-function as illustrated in the following code chunk.

-

-<<callprobs>>=

-invisible(open(snpCallProbability(cnSet)))

-callProbs <- as.matrix(snpCallProbability(cnSet)[1:5, 1:5])

-i2p(callProbs)

-invisible(close(snpCallProbability(cnSet)))

+\vspace{-0.5em}

+<<>>=

+updated

 @

 \textbf{Wrapper:} As an alternative to calling the functions

@@ -265,27 +258,14 @@ delete(cnSet2$SKW)

 rm(cnSet2)

 @

-Users may now proceed to the CopyNumber vignette for copy number

-analyses.

-

-

+Users can proceed to the \verb+copynumber+ vignette for copy number

+analyses.  See the \verb+Infrastructure+ vignette for additional

+details on the \Robject{CNSet} class, including an overview of the

+available accessors.

 \section{Session information}

 <<sessionInfo, results=tex>>=

 toLatex(sessionInfo())

 @

-

-<<>>=

-cnset.updated <- crlmmCopynumber(cnSet)

-@

-

-<<>>=

-snp.index <- which(chromosome(cnSet)==1)

-cnset2 <- cnSet[snp.index, ]

-trace(crlmmCopynumber, browser)

-object <- crlmmCopynumber(cnset2)

-## elements no longer ff

-@

-

 \end{document}

new file mode 100755

@@ -0,0 +1,291 @@

+%\VignetteIndexEntry{IlluminaPreprocessCN Vignette for Illumina}

+%\VignetteDepends{crlmm, ff, cacheSweave}

+%\VignetteKeywords{crlmm, illumina, copy number, SNP}

+%\VignettePackage{crlmm}

+\documentclass{article}

+\usepackage{graphicx}

+\usepackage{natbib}

+\usepackage[margin=1in]{geometry}

+\newcommand{\crlmm}{\Rpackage{crlmm}}

+\newcommand{\Rfunction}[1]{{\texttt{#1}}}

+\newcommand{\Rmethod}[1]{{\texttt{#1}}}

+\newcommand{\Rcode}[1]{{\texttt{#1}}}

+\newcommand{\Robject}[1]{{\texttt{#1}}}

+\newcommand{\Rpackage}[1]{{\textsf{#1}}}

+\newcommand{\Rclass}[1]{{\textit{#1}}}

+\newcommand{\oligo}{\Rpackage{oligo }}

+\newcommand{\R}{\textsf{R}}

+\newcommand{\ff}{\Rpackage{ff}}

+

+\begin{document}

+\title{Preprocessing and Genotyping Illumina Arrays for Copy Number Analysis}

+

+\date{\today}

+

+\author{Rob Scharpf}

+\maketitle

+

+

+\begin{abstract}

+

+  This vignette illustrates the steps required prior to copy number

+  analysis for Infinium platforms.  Specifically, we require

+  construction of a container to store processed forms of the raw

+  data, preprocessing to normalize the arrays, and genotyping using

+  the CRLMM algorithm.  After completing these steps, users can refer

+  to the \verb+copynumber+ vignette.

+

+\end{abstract}

+

+

+\section{Set up}

+

+<<crlmm, results=hide, echo=FALSE>>=

+library(crlmm)

+options(width=70)

+options(continue=" ")

+@

+

+%\textbf{Supported platforms:} The supported Infinium platforms are

+%those for which a corresponding annotation package is available.  The

+%annotation packages contain information on the markers, such as

+%physical position and chromosome, as well as pre-computed parameters

+%estimated from HapMap used during the preprocessing and genotyping

+%steps. Currently supported Infinium platforms are listed in the

+%following code chunk.

+The following codechunk declares a directory for saving \Robject{ff}

+files that will contain the normalized intensities and the genotype

+calls.

+

+<<ldpath,results=hide>>=

+library(ff)

+if(getRversion() < "2.13.0"){

+	rpath <- getRversion()

+} else rpath <- "trunk"

+outdir <- paste("/thumper/ctsa/snpmicroarray/rs/ProcessedData/crlmm/", rpath, "/illumina_vignette", sep="")

+ldPath(outdir)

+dir.create(outdir, recursive=TRUE, showWarnings=FALSE)

+@

+

+We will also store cached computations in the directory \verb+outdir+.

+

+<<cacheSweave, echo=FALSE, results=hide>>=

+library(cacheSweave)

+setCacheDir(outdir)

+@

+

+We declare that \crlmm{} should process 150,000 markers at a time

+and/or 500 samples at a time (when possible) to reduce the memory

+footprint.  As our example dataset in this vignette contains fewer

+than 500 samples, all samples will be processed simultaneously.

+

+<<ram>>=

+ocProbesets(150e3)

+ocSamples(500)

+@

+

+\section{Initializing a container for storing processed data}

+

+This section will initialize a container for storing processed forms

+of the data, including the normalized intensities for the A and B

+alleles and the CRLMM genotype calls and confidence scores.  In

+addition, the container will store information on the markers

+(physical position, chromosome, and a SNP indicator), the batch, and

+the samples (e.g., gender).  To construct this container for Infinium

+platforms, several steps are required.

+

+We begin by specifying the path containing the raw IDAT files for a

+set of samples from the Infinium 370k platform.

+

+<<datadir>>=

+datadir <- "/thumper/ctsa/snpmicroarray/illumina/IDATS/370k"

+@

+

+For Infinium platforms, an Illumina sample sheet containing

+information for reading the raw IDAT files is required. Please refer

+to the BeadStudio Genotyping guide, Appendix A, for additional

+information.  The following code reads in the samplesheet for the IDAT

+files on our local server.

+

+<<samplesheet>>=

+samplesheet = read.csv(file.path(datadir, "HumanHap370Duo_Sample_Map.csv"), header=TRUE, as.is=TRUE)

+@

+

+For the purposes of this vignette, we indicate that we only wish to

+process a subset of the arrays.  For our dataset, the file extensions

+are `Grn.dat' and `Red.idat'.  We store the complete path to the

+filename without the file extension in the \Robject{arrayNames} and

+check that all of the green and red IDAT files exists.

+

+<<subsetArrays>>=

+samplesheet <- samplesheet[-c(28:46,61:75,78:79), ]

+arrayNames <- file.path(datadir, unique(samplesheet[, "SentrixPosition"]))

+all(file.exists(paste(arrayNames, "_Grn.idat", sep="")))

+all(file.exists(paste(arrayNames, "_Red.idat", sep="")))

+arrayInfo <- list(barcode=NULL, position="SentrixPosition")

+@

+

+All supported platforms have a corresponding annotation package.  The

+appropriate annotation package is specified by the platform identifier

+without the \verb+Crlmm+ postfix.

+

+<<cdfname>>=

+cdfName <- "human370v1c"

+@

+

+Next, we construct a character vector that specifies the batch for

+each of the \Sexpr{length(arrayNames)} arrays.  Here, we have a small

+dataset and process the samples in a single batch. Processing the

+samples as a single batch is generally reasonable if the samples were

+processed at similar times (e.g., within a few weeks).

+

+<<batch>>=

+batch <- rep("1", nrow(samplesheet))

+@

+

+Finally, we initialize an object of class \Robject{CNSet} using the

+function \Rfunction{constructInf}.

+

+<<container,cache=TRUE>>=

+cnSet <- constructInf(sampleSheet=samplesheet,

+		      arrayNames=arrayNames,

+		      batch=batch,

+		      arrayInfoColNames=arrayInfo,

+		      cdfName=cdfName,

+		      verbose=TRUE,

+		      saveDate=TRUE)

+@

+

+A concise summary of the object's contents can be viewed with the

+\Rfunction{print} function.

+

+<<cnset>>=

+print(cnSet)

+@

+

+Note that the above object does not yet contain any processed data

+(only \verb+NA+'s).  As the elements of the \verb+assayData+ slot are

+\Robject{ff} objects (not matrices), several \verb+.ff+ files now

+appear in the \verb+outdir+. The \verb+.ff+ files should not be

+removed and can be listed using the \R{} function

+\Rfunction{list.files}.  %For the most part, the \emph{appearance} that

+%the data is stored in memory is preserved.

+

+<<listff>>=

+sapply(assayData(cnSet), function(x) class(x)[1])

+list.files(outdir, pattern=".ff")[1:5]

+@

+

+\section{Preprocessing}

+

+The raw intensities from the Infinium IDAT files are read and

+normalized using the function \Rfunction{preprocessInf}.  The function

+\Rfunction{preprocessInf} returns a \Robject{ff} object containing the

+parameters for the mixture model used by the CRLMM genotyping

+algorithm.

+

+<<preprocess,cache=TRUE>>=

+mixtureParams <- preprocessInf(cnSet=cnSet, sampleSheet=samplesheet, arrayNames=arrayNames, arrayInfoColNames=arrayInfo)

+invisible(open(mixtureParams))

+str(mixtureParams[])

+invisible(close(mixtureParams))

+@

+

+Note that the normalized intensities for the A and B alleles are no

+longer \verb+NA+s and can be inspected using the methods \Rfunction{A}

+and \Rfunction{B}, respectively.

+

+<<intensities>>=

+invisible(open(A(cnSet)))

+invisible(open(B(cnSet)))

+as.matrix(A(cnSet)[1:5, 1:5])

+as.matrix(B(cnSet)[1:5, 1:5])

+invisible(close(A(cnSet)))

+invisible(close(B(cnSet)))

+@

+

+\section{Genotyping}

+

+CRLMM genotype calls and confidence scores are estimated using the

+function \Rfunction{genotypeInf}.

+

+<<genotype,cache=TRUE>>=

+updated <- genotypeInf(cnSet, mixtureParams=mixtureParams)

+print(updated)

+@

+

+The posterior probabilities for the genotype calls in the

+\verb+callProbability+ element of the \verb+assayData+ are stored as

+integers to reduce the file size on disk. The scores can be

+transformed to the probability scale using the \Rfunction{i2p}

+function as illustrated in the following code chunk.

+

+<<callprobs>>=

+invisible(open(snpCallProbability(cnSet)))

+callProbs <- as.matrix(snpCallProbability(cnSet)[1:5, 1:5])

+i2p(callProbs)

+invisible(close(snpCallProbability(cnSet)))

+@

+

+\textbf{Wrapper:} As an alternative to calling the functions

+\Rfunction{constructInf}, \Rfunction{preprocessInf} and

+\Rfunction{genotypeInf} in sequence, a convenience function called

+\Rfunction{genotype.Illumina} is a wrapper for the above functions and

+produces identical results.

+

+<<genotype.Illumina,cache=TRUE,results=hide>>=

+cnSet2 <- genotype.Illumina(sampleSheet=samplesheet,

+			    arrayNames=arrayNames,

+			    arrayInfoColNames=arrayInfo,

+			    cdfName="human370v1c",

+			    batch=batch)

+@

+

+<<checkIdenticalCalls>>=

+invisible(open(calls(cnSet)))

+invisible(open(calls(cnSet2)))

+snp.index <- which(isSnp(cnSet))

+identical(calls(cnSet)[snp.index, 1:20], calls(cnSet2)[snp.index, 1:20])

+invisible(close(calls(cnSet)))

+invisible(close(calls(cnSet2)))

+@

+

+To fully remove the data associated with the \Robject{cnSet2} object,

+one should use the \Rfunction{delete} function in the \Rpackage{ff}

+package followed by the \Rfunction{rm} function.  The following code

+is not evaluated is it would change the results of the cached

+computations in the previous code chunk.

+

+<<delete, eval=FALSE>>=

+lapply(assayData(cnSet2), delete)

+lapply(batchStatistics(cnSet2), delete)

+delete(cnSet2$gender)

+delete(cnSet2$SNR)

+delete(cnSet2$SKW)

+rm(cnSet2)

+@

+

+Users may now proceed to the CopyNumber vignette for copy number

+analyses.

+

+

+

+\section{Session information}

+<<sessionInfo, results=tex>>=

+toLatex(sessionInfo())

+@

+

+

+<<>>=

+cnset.updated <- crlmmCopynumber(cnSet)

+@

+

+<<>>=

+snp.index <- which(chromosome(cnSet)==1)

+cnset2 <- cnSet[snp.index, ]

+trace(crlmmCopynumber, browser)

+object <- crlmmCopynumber(cnset2)

+## elements no longer ff

+@

+

+\end{document}