@@ -60,7 +60,6 @@ exportMethods(open, "[", show, lM, lines)

 export(crlmm, 

        crlmmCopynumber, 

        crlmmIllumina, 

-##       ellipse, 

        genotype, 

        readIdatFiles, 

        readIdatFiles2,

@@ -70,8 +69,7 @@ export(crlmm,

        genotype2, genotypeLD,

        batch,

        crlmmCopynumber2, crlmmCopynumberLD)

-##export(initializeParamObject, biasAdjNP2)

-export(construct)

+

@@ -1,9 +1,9 @@

 All: copynumber crlmmDownstream clean

-copynumber: copynumber.tex

+copynumber: copynumber.pdf

 	cp -p ../scripts/copynumber.pdf .

-crlmmDownstream: crlmmDownstream.tex

+crlmmDownstream: crlmmDownstream.pdf

 	cp -p ../scripts/crlmmDownstream.pdf .

 clean:

new file mode 100644

@@ -0,0 +1,88 @@

+setClassUnion("integerOrMissing", c("integer", "missing", "numeric"))

+setGeneric("totalCopyNumber", function(object, i, j, ...) standardGeneric("totalCopyNumber"))

+setMethod("totalCopyNumber",

+	  signature=signature(object="CNSet", i="integerOrMissing", j="integerOrMissing"),

+	  function(object, i, j, ...){

+	if(missing(i) & missing(j)){

+		if(inherits(CA(object), "ff") | inherits(CA(object), "ffdf")) stop("Must specify i and/or j for ff objects")

+	}

+	if(missing(i) & !missing(j)){

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

+		cn.total <- as.matrix(CA(object)[, j])

+		if(length(snp.index) > 0){

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

+			snps <- (1:nrow(cn.total))[i %in% snp.index]

+			cn.total[snps, ] <- cn.total[snps, j] + cb				

+		}

+	}

+	if(!missing(i) & missing(j)){

+		snp.index <- intersect(which(isSnp(object)), i)

+		cn.total <- as.matrix(CA(object)[i, ])

+		if(length(snp.index) > 0){

+			cb <- as.matrix(CB(object)[snp.index, ])

+			snps <- (1:nrow(cn.total))[i %in% snp.index]

+			cn.total[snps, ] <- cn.total[snps, ] + cb				

+		}

+	}

+	if(!missing(i) & !missing(j)){

+		snp.index <- intersect(which(isSnp(object)), i)		

+		cn.total <- as.matrix(CA(object)[i, j])

+		if(length(snp.index) > 0){

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

+			snps <- (1:nrow(cn.total))[i %in% snp.index]

+			cn.total[snps, ] <- cn.total[snps, ] + cb

+		}

+	}

+	cn.total <- cn.total/100

+	dimnames(cn.total) <- NULL

+	return(cn.total)

+})

+

+

+constructFeatureData <- function(gns, cdfName){

+	pkgname <- paste(cdfName, "Crlmm", sep="")	

+	path <- system.file("extdata", package=pkgname)

+	load(file.path(path, "cnProbes.rda"))

+	load(file.path(path, "snpProbes.rda"))

+	cnProbes$chr <- chromosome2integer(cnProbes$chr)

+	cnProbes <- as.matrix(cnProbes)

+	snpProbes$chr <- chromosome2integer(snpProbes$chr)

+	snpProbes <- as.matrix(snpProbes)

+	mapping <- rbind(snpProbes, cnProbes, deparse.level=0)

+	mapping <- mapping[match(gns, rownames(mapping)), ]

+	isSnp <- 1L-as.integer(gns %in% rownames(cnProbes))

+	mapping <- cbind(mapping, isSnp, deparse.level=0)

+	stopifnot(identical(rownames(mapping), gns))

+	colnames(mapping) <- c("chromosome", "position", "isSnp")

+	new("AnnotatedDataFrame",

+	    data=data.frame(mapping),

+	    varMetadata=data.frame(labelDescription=colnames(mapping)))	

+}

+constructAssayData <- function(np, snp, object, storage.mode="environment", order.index){

+	stopifnot(identical(snp$gns, featureNames(object)))

+	gns <- c(featureNames(object), np$gns)

+	sns <- np$sns

+	np <- np[1:2]

+	snp <- snp[1:2]

+	stripnames <- function(x) {

+		dimnames(x) <- NULL

+		x

+	}

+	np <- lapply(np, stripnames)

+	snp <- lapply(snp, stripnames)

+	A <- rbind(snp[[1]], np[[1]], deparse.level=0)[order.index, ]

+	B <- rbind(snp[[2]], np[[2]], deparse.level=0)[order.index, ]

+	gt <- stripnames(calls(object))

+	emptyMatrix <- matrix(integer(), nrow(np[[1]]), ncol(A))

+	gt <- rbind(gt, emptyMatrix, deparse.level=0)[order.index,]

+	pr <- stripnames(snpCallProbability(object))

+	pr <- rbind(pr, emptyMatrix, deparse.level=0)[order.index, ]

+	emptyMatrix <- matrix(integer(), nrow(A), ncol(A))	

+	aD <- assayDataNew(storage.mode,

+			   alleleA=A,

+			   alleleB=B,

+			   call=gt,

+			   callProbability=pr,

+			   CA=emptyMatrix,

+			   CB=emptyMatrix)

+}

@@ -17,37 +17,40 @@

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

 \begin{document}

-\title{Copy number estimation and genotype calling with \Rpackage{crlmm}}

-\date{\today}

+\title{Using \crlmm{} for copy number estimation and genotype calling

+  with Illumina platforms} 

+

+\date{\today} 

+

 \author{Rob Scharpf}

 \maketitle

 Allele-specific copy number estimation in the crlmm package is

 available for several Illumina platforms.  As described in the

-\texttt{copynumber.pdf} vignette, this algorithm works best when there

-are a sufficient number of samples such that AA, AB, and BB genotypes

-are observed at most loci. For small studies (e.g., fewer than 50

-samples), there will be a large number of SNPs that are

+\texttt{copynumber} vignette, copy number estimation in \crlmm{} works

+best when there are a sufficient number of samples such that AA, AB,

+and BB genotypes are observed at most loci. For small studies (e.g.,

+fewer than 50 samples), there will be a large number of SNPs that are

 monomorphic. For monomorphic SNPs, the estimation problem becomes more

 difficult and alternative strategies that estimate the relative total

-copy number (as to absolute allele-specific copy number) may be

-preferable.  In the following code, we list the platforms for which

-annotation packages are available for computations performed by

-\crlmm{}. Next we create a directory where output files will be stored

-and indicate the directory that contains the IDAT files that will be

-used in our analysis.

+copy number may be preferable.  In addition to installing \crlmm{},

+one must also install the appropriate annotation package for the

+Illumina platform.  In the following code, we list the platforms for

+which annotation packages are currently available. Next we create a

+directory where output files will be stored and indicate the directory

+that contains the IDAT files that will be used in our analysis.

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

 options(width=70)

 options(continue=" ")

 @ 

-<<>>=

+<<libraries>>=

 library(crlmm)

 crlmm:::validCdfNames()

-outdir <- "/thumper/ctsa/snpmicroarray/rs/ProcessedData/crlmm/trunk/illumina_vignette"

+outdir <- "/thumper/ctsa/snpmicroarray/rs/data/hapmap/crlmmVignette/release/illumina"

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

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

-cdfName <- "human370v1c"

 @ 

 To perform copy number analysis on the Illumina platform, several

@@ -69,106 +72,200 @@ scan date of the arrays for later use.  The scan dates can be pulled

 from the RG object and added to the \Robject{SnpSet} returned by

 \Rfunction{crlmmIllumina} as illustrated below.

+%\texttt{copynumber.pdf} vignette, we provide an option to preprocess

+%and genotype using ordinary matrices (large RAM, but quicker access)

+%or \Robject{ff} objects (low RAM, but more I/O overhead). In the

+%analysis below, the sample size is small (n=43) and we use matrices.

+%(Users wanting the \Rpackage{ff} implementation should try

+%\Rfunction{crlmm:::crlmmIllumina2} with the same arguments.)

+

 <<samplesToProcess>>=

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

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

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

-##Check that files exist

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

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

-myfun <- function(outdir, filenames, samplesheet, arrayInfoColNames, cdfName, cnFile, snpFile){

-	if(missing(cnFile)) cnFile <- file.path(outdir, "cnFile.rda")

-	if(missing(snpFile)) snpFile <- file.path(outdir, "snpFile.rda")

-	RG <- readIdatFiles(sampleSheet=samplesheet, 

-			    path=dirname(filenames), 

-			    arrayInfoColNames=arrayInfoColNames,

-			    saveDate=TRUE)	

-	crlmmResult <- crlmmIllumina(RG=RG, 

-				     cdfName="human370v1c", 

-				     sns=pData(RG)$ID, 

-				     returnParams=TRUE,

-				     cnFile=cnFile,

-				     snpFile=snpFile,

-				     save.it=TRUE)	

-	protocolData(crlmmResult)$ScanDate <- protocolData(RG)$ScanDate

-	range(protocolData(crlmmResult)$ScanDate)

-	return(crlmmResult)

+@ 

+

+<<samplesToProcess2, echo=FALSE>>=

+if(!exists("crlmmResult")){

+	if(!file.exists(file.path(outdir, "crlmmResult.rda"))){

+		RG <- readIdatFiles(samplesheet, 

+				    path=dirname(arrayNames[1]), 

+				    arrayInfoColNames=list(barcode=NULL, position="SentrixPosition"), 

+				    saveDate=TRUE)

+		crlmmResult <- crlmmIllumina(RG=RG, 

+					     cdfName="human370v1c", 

+					     sns=pData(RG)$ID, 

+					     returnParams=TRUE,

+					     cnFile=file.path(outdir, "cnFile.rda"),

+					     snpFile=file.path(outdir, "snpFile.rda"),

+					     save.it=TRUE)

+		protocolData(crlmmResult)$ScanDate <- protocolData(RG)$ScanDate

+		range(protocolData(crlmmResult)$ScanDate)

+		save(crlmmResult, file=file.path(outdir, "crlmmResult.rda"))

+		rm(RG); gc()

+	} 

 }

-trace(myfun, browser)

-crlmmResult <- checkExists("crlmmResult", path=outdir, FUN=myfun,

-			   outdir=outdir,

-			   filenames=arrayNames,

-			   samplesheet=samplesheet,

-			   arrayInfoColNames=list(barcode=NULL, position="SentrixPosition"),

-			   cdfName=cdfName)

-##if(!exists("crlmmResult")){

-##	if(!file.exists(file.path(outdir, "crlmmResult.rda"))){

-##		RG <- readIdatFiles(samplesheet, 

-##				    path=dirname(arrayNames[1]), 

-##				    arrayInfoColNames=list(barcode=NULL, position="SentrixPosition"), 

-##				    saveDate=TRUE)

-##		crlmmResult <- crlmmIllumina(RG=RG, 

-##					     cdfName="human370v1c", 

-##					     sns=pData(RG)$ID, 

-##					     returnParams=TRUE,

-##					     cnFile=file.path(outdir, "cnFile.rda"),

-##					     snpFile=file.path(outdir, "snpFile.rda"),

-##					     save.it=TRUE)

-##		protocolData(crlmmResult)$ScanDate <- protocolData(RG)$ScanDate

-##		range(protocolData(crlmmResult)$ScanDate)

-##		save(crlmmResult, file=file.path(outdir, "crlmmResult.rda"))

-##		rm(RG); gc()

-##	} else {

-##		message("Loading previously saved crlmm results")

-##		load(file.path(outdir, "snpFile.rda"))

-##		res <- get("res")

-##		load(file.path(outdir, "cnFile.rda"))

-##		cnAB <- get("cnAB")

-##		load(file.path(outdir, "crlmmResult.rda"))

-##	}

-##}

 @ 

-This creates a \Robject{crlmmSetList} object in the \Robject{outdir}

-directory.  The first element of this object contains the

-quantile-normalized A and B intensities.  The second element in the list

-contains the crlmm genotype calls.  

+<<samplesToProcess3, eval=FALSE>>=

+RG <- readIdatFiles(samplesheet, 

+		    path=dirname(arrayNames[1]), 

+		    arrayInfoColNames=list(barcode=NULL, position="SentrixPosition"), 

+		    saveDate=TRUE)

+crlmmResult <- crlmmIllumina(RG=RG, 

+			     cdfName="human370v1c", 

+			     sns=pData(RG)$ID, 

+			     returnParams=TRUE,

+			     cnFile=file.path(outdir, "cnFile.rda"),

+			     snpFile=file.path(outdir, "snpFile.rda"),

+			     save.it=TRUE)

+protocolData(crlmmResult)$ScanDate <- protocolData(RG)$ScanDate

+range(protocolData(crlmmResult)$ScanDate)

+rm(RG); gc()

+@ 

+\noindent Finally, we load a few of the intermediate files that were

+created during the preprocessing and genotyping.

+<<loadIntermediate, eval=FALSE>>=

+load(file.path(outdir, "snpFile.rda"))

+res <- get("res")

+load(file.path(outdir, "cnFile.rda"))

+cnAB <- get("cnAB")

+load(file.path(outdir, "crlmmResult.rda"))

+@ 

-<<SNR>>=

-CHR <- 1

-filename <- paste(outdir, "/crlmmSetList_", CHR, ".rda", sep="")

-load(filename)

-hist(crlmmSetList[[1]]$SNR)

+<<loadIntermediate, eval=TRUE, echo=FALSE>>=

+if(!exists("res")){

+	load(file.path(outdir, "snpFile.rda"))

+	res <- get("res")

+	load(file.path(outdir, "cnFile.rda"))

+	cnAB <- get("cnAB")

+	load(file.path(outdir, "crlmmResult.rda"))

+}

 @ 

-Run update on the CrlmmSetList object to obtain copy number estimates. Estimate

-copy number for chromosome 1.

+After running the crlmm algorithm, we construct a container for

+storing the quantile normalized intensities, genotype calls, and

+allele-specific copy number estimates.  A few helper functions for

+facilitating the construction of this container have been added to the

+inst/scripts directory of this package and can be sourced as follows.

+Documentation for these helper functions will be available in the

+devel version of this package.

+

+<<constructContainer, eval=FALSE>>=

+path <- system.file("scripts", package="crlmm")

+source(file.path(path, "helperFunctions.R"))

+fD <- constructFeatureData(c(res$gns, cnAB$gns), cdfName="human370v1c")

+new.order <- order(fD$chromosome, fD$position)

+fD <- fD[new.order, ]

+aD <- constructAssayData(cnAB, res, crlmmResult, order.index=new.order)

+protocolData(crlmmResult)$batch <- vector("integer", ncol(crlmmResult))

+container <- new("CNSetLM", 

+		 assayData=aD,

+		 phenoData=phenoData(crlmmResult),

+		 protocolData=protocolData(crlmmResult),

+		 featureData=fD,

+		 annotation="human370v1c")

+@ 

-<<estimateCn>>=

-update(filename)

-##Or equivalently,

-##crlmmSetList <- computeCopynumber(crlmmSetList)

-##save(crlmmSetList, file=file.path(outdir, paste("crlmmSetList_", CHR, ".rda", sep="")))

+<<container2,echo=FALSE>>=

+if(!file.exists(file.path(outdir, "cnSet"))){

+	path <- system.file("scripts", package="crlmm")

+	source(file.path(path, "helperFunctions.R"))

+	fD <- constructFeatureData(c(res$gns, cnAB$gns), cdfName="human370v1c")

+	new.order <- order(fD$chromosome, fD$position)

+	fD <- fD[new.order, ]

+	aD <- constructAssayData(cnAB, res, crlmmResult, order.index=new.order)

+	protocolData(crlmmResult)$batch <- vector("integer", ncol(crlmmResult))

+	container <- new("CNSetLM", 

+			 assayData=aD,

+			 phenoData=phenoData(crlmmResult),

+			 protocolData=protocolData(crlmmResult),

+			 featureData=fD,

+			 annotation="human370v1c")

+}

 @ 

-Samples with low signal to noise ratios tend to have a lot of variation

-in the point estimates of copy number.  One may want to exclude these

-samples, or smooth after filtering outliers.  Here we load the

-crlmmSetList object.  See the copynumber.Rnw vignette for example plots.

-<<crlmmSetList>>=

-##reload the updated object

-load(filename)

-cn <- copyNumber(crlmmSetList)

+

+As described in the \texttt{copynumber} vignette, two \R{} functions

+for copy number estimation are available: \Rfunction{crlmmCopynumber}

+and \Rfunction{crlmmCopynumber2}.  The latter requires that the assay

+data elements are represented using \Robject{ff} objects.  As the

+dataset for this vignette is small (43 arrays) and the above steps did

+not make use of the \Robject{ff} features, constructing \Robject{ff}

+objects at this point in the analysis would serve little purpose.  The

+decision to use ordinary matrices or \Robject{ff} objects should be

+decided at the beginning of the analysis and then propogated to both

+the genotyping and copy number estimation steps.  Here, we use the

+\Rfunction{crlmmCopynumber} to estimate copy number.

+

+<<estimateCopynumber, echo=FALSE>>=

+if(!exists("cnSet")){

+	if(!file.exists(file.path(outdir, "cnSet.rda"))){

+		cnSet <- crlmmCopynumber(container, verbose=FALSE)

+		save(cnSet, file=file.path(outdir, "cnSet.rda"))

+	} else load(file.path(outdir, "cnSet.rda"))

+}

+@ 

+

+<<estimateCopynumber, eval=FALSE>>=

+cnSet <- crlmmCopynumber(container, verbose=TRUE)

+@ 

+

+%In the following code, we define helper functions to construct a

+%\Robject{featureData} object that chromosome and physical position

+%annotation for each marker. We then define a constructor for

+%initializing the assay data -- the copy number estimation algorithm

+%requires normalized intensities, genotype calls, and genotype

+%confidence scores.  Note that the order of the construction is

+%important.  In particular, the validity method for the

+%\Robject{CNSetLM} object requires a 'batch' label in the

+%\Robject{protocolData} slot and that 'chromosome', 'position', and

+%'isSnp' labels are present in the \Robject{featureData} slot.  With

+%the object \Robject{cnSet} in hand, one can proceed with the copy

+%number analysis as outlined in the \texttt{copynumber.pdf} vignette.

+%Useful accessors and visualizations of the locus-level estimates are

+%also discussed in the \texttt{copynumber.pdf} vignette.

+

+

+\paragraph{Accessors for extracting the locus-level copy number

+  estimates.}

+

+As an example of how to use accessors to obtain the allele-specific CN

+estimates, the following code chunk extracts the allele-specific copy

+number for polymorphic markers on chromosome 21.

+

+<<acn-accessor>>=

+marker.index <- which(chromosome(cnSet) == 21 & isSnp(cnSet))

+ca <- CA(cnSet)[marker.index, ]/100

+cb <- CB(cnSet)[marker.index, ]/100

+missing.index <- which(rowSums(is.na(ca))==ncol(cnSet))

+ca <- ca[-missing.index, ]

+cb <- cb[-missing.index, ]

+@ 

+\noindent Negating the \Robject{isSnp} function could be used to

+extract the estimates at nonpolymorphic markers. For instance,

+<<monomorphic-accessor>>=

+cn.monomorphic <- CA(cnSet)[which(chromosome(cnSet) == 21 & !isSnp(cnSet)), ]/100

 @ 

-The following helper function can facilitate access to the total copy

-number.

+At polymorphic loci, the total copy number is the sum of the number of

+copies of the A allele and the number of copies for the B allele.  At

+nonpolymorphic loci, the total copy number is the number of copies for

+the A allele.  The helper function totalCopyNumber can be used to

+extract the total copy number for all polymorphic and nonpolymorphic

+markers.  Documentation of the \Rfunction{totalCopyNumber} will be

+available in the devel version of the \Rpackage{oligoClasses}.

 <<copyNumberHelper>>=

-trace(totalCopyNumber, browser, signature="CNSet")

-cn.total2 <- totalCopyNumber(cnSet, i=which(chromosome(cnSet)==1), j=1:20)

+cn.total <- ca+cb

+cn.total2 <- totalCopyNumber(cnSet, i=marker.index)

+cn.total2 <- cn.total2[-missing.index, ]

+dimnames(cn.total) <- NULL

+all.equal(cn.total2, cn.total)

 @ 

 A few simple visualizations may be helpful at this point. The first

@@ -187,14 +284,15 @@ hist(cnSet$SNR, breaks=15)

 <<firstSample, fig=TRUE>>=

 low.snr <- which(cnSet$SNR == min(cnSet$SNR))

 high.snr <- which(cnSet$SNR == max(cnSet$SNR))

-x <- position(cnSet)[chromosome(cnSet) == 1]

+x <- position(cnSet)[marker.index]

+x <- x[-missing.index]

 par(mfrow=c(2,1), las=1, mar=c(0.5, 4, 0.5, 0.5), oma=c(4, 1, 1,1))

 for(j in c(low.snr, high.snr)){

-	cn <- copyNumber(cnSet)[, j]/100

+	cn <- cn.total[, j]

 	cn[cn < 0.05] <- 0.05

 	plot(x,

-	     cn[chromosome(cnSet) == 1],

-	     pch=".", ylab="copy number", xaxt="n", log="y")

+	     cn,

+	     pch=".", ylab="copy number", xaxt="n", ylim=c(0, 6))

 }

 axis(1, at=pretty(x), labels=pretty(x/1e6))

 mtext("Mb", 1, outer=TRUE, line=2)

@@ -208,10 +306,7 @@ followed by a segmentation or hidden markov model.

 <<removeOutliers, fig=TRUE>>=

 par(mfrow=c(2,1), las=1, mar=c(0.5, 4, 0.5, 0.5), oma=c(4, 1, 1,1))

 for(j in c(low.snr, high.snr)){

-	x <- position(cnSet)[chromosome(cnSet)==1]

-	cn <- copyNumber(cnSet)[, j]/100

-	cn[cn < 0.05] <- 0.05

-	##cn <- log(cn)

+	cn <- cn.total[, j]

 	x <- x[!is.na(cn)]

 	cn <- cn[!is.na(cn)]

 	y <- as.numeric(runmed(cn, k=3))

@@ -259,6 +354,10 @@ mtext("Mb", 1, outer=TRUE, line=2)

 %	}

 %}

 %@ 

+\section{Session information}

+<<sessionInfo, results=tex>>=

+toLatex(sessionInfo())

+@ 

 \end{document}

@@ -4,6 +4,7 @@

 \alias{CNSetLM}

 \alias{CNSetLM-class}

 \alias{[,CNSetLM-method}

+\alias{lines,CNSetLM-method}

 \alias{lM,CNSetLM-method}

 \alias{lM<-,CNSetLM,list_or_ffdf-method}

 \alias{open,CNSetLM-method}

@@ -41,10 +42,10 @@ Class \code{"\linkS4class{Versioned}"}, by class "CNSet", distance 6.

 \section{Methods}{

   \describe{

     \item{[}{\code{signature(x = "CNSetLM")}: subset \code{CNSetLM} objects}

+    \item{lines}{\code{signature(x="CNSetLM")}: for drawing prediction regions on A vs B scatterplots}

     \item{lM}{\code{signature(object = "CNSetLM")}: Extract list or

     ffdf object containing linear model parameters}

-##    \item{lM<-}{\code{signature(object = "CNSetLM", value = "list_or_ffdf")}: ... }

-    \item{open}{\code{signature(con = "CNSetLM")}: opens file connects

+   \item{open}{\code{signature(con = "CNSetLM")}: opens file connects

     to ff objects for assayData elements and linear model parameters}

     \item{show}{\code{signature(object = "CNSetLM")}: print method

     for the class }

@@ -54,9 +55,7 @@ Class \code{"\linkS4class{Versioned}"}, by class "CNSet", distance 6.

 \seealso{

 	\code{\linkS4class{SnpSuperSet}}, \code{\linkS4class{CNSet}}

 }

-%	~~objects to See Also as \code{\link{~~fun~~}}, ~~~

-%	or \code{\linkS4class{CLASSNAME}} for links to other classes

-%}

+

 \examples{

 showClass("CNSetLM")

 }

@@ -14,7 +14,7 @@ genotype(filenames, cdfName, batch, mixtureSampleSize = 10^5, eps =

 rep(1/3, 3), DF = 6, SNRMin = 5, recallMin = 10, recallRegMin = 1000,

 gender = NULL, returnParams = TRUE, badSNP = 0.7)

-genotypeLD(filenames, cdfName, batch, mixtureSampleSize = 10^5, eps = 0.1, verbose = TRUE, seed = 1, sns, copynumber = FALSE, probs = rep(1/3, 3), DF = 6, SNRMin = 5, recallMin = 10, recallRegMin = 1000, gender = NULL, returnParams = TRUE, badSNP = 0.7)

+genotypeLD(filenames, cdfName, batch, mixtureSampleSize = 10^5, eps = 0.1, verbose = TRUE, seed = 1, sns, copynumber = TRUE, probs = rep(1/3, 3), DF = 6, SNRMin = 5, recallMin = 10, recallRegMin = 1000, gender = NULL, returnParams = TRUE, badSNP = 0.7)

 }

 \arguments{

   \item{filenames}{ complete path to CEL files}

@@ -1,5 +1,6 @@

 \name{readIdatFiles}

 \alias{readIdatFiles}

+\alias{readIdatFiles2}

 \title{Reads Idat Files from Infinium II Illumina BeadChips}