@@ -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: 1.11.24

+Version: 1.11.25

 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>

@@ -27,6 +27,7 @@ importMethodsFrom(oligoClasses, allele, calls, "calls<-", confs,

 		  "confs<-", cnConfidence, "cnConfidence<-", isSnp,

 		  chromosome, position, A, B,

 		  "A<-", "B<-", open, close, flags,

+		  openff, closeff,

 		  batchStatistics, "batchStatistics<-", updateObject)

 importFrom(oligoClasses, chromosome2integer, celfileDate, list.celfiles,

            copyNumber, initializeBigMatrix, initializeBigVector, isPackageLoaded)

@@ -46,21 +47,7 @@ importFrom(genefilter, rowSds)

 importFrom(mvtnorm, dmvnorm)

 importFrom(ellipse, ellipse)

 importFrom(ff, ffdf, physical.ff, physical.ffdf, ffrowapply)

-## It is important not to import these classes from oligoClasses

-## Doing so causes the following errors:

-## N.AA(container)[index, ] <- someMatrix

-##Error in function (classes, fdef, mtable)  :

-##  unable to find an inherited method for function "medianA.AA<-", for signature "CNSet", "ff_matrix"

-##importClassesFrom(oligoClasses, ffdf, ff_matrix)

 importClassesFrom(oligoClasses, ff_matrix, ffdf)

-## Important to export these classes

-##exportClasses(ff_or_matrix, ff_matrix, ffdf)

-##exportClasses(ff_or_matrix)

-##---------------------------------------------------------------------------

-## lattice imports

-##---------------------------------------------------------------------------

-##import(panel.number, panel.grid, panel.xyplot, lpoints, lsegments, lrect, ltext)

-

 exportMethods(lines)

 exportMethods(CA, CB)

 export(crlmm,

@@ -1,120 +1,74 @@

+

 setMethod("Ns", signature(object="AssayData"),

 	  function(object, ...){

-		  dotArgs <- names(list(...))

-		  missing.i <- !("i" %in% dotArgs)

-		  missing.j <- !("j" %in% dotArgs)

-		  if(!missing.j) j <- list(...)[["j"]]

 		  batchnames <- sampleNames(object)

-		  if(!missing.j) batchnames <- batchnames[j]

-		  if(missing.i & missing.j) stop("Must specify either the rows i or batches j")

-		  is.ff <- is(assayDataElement(object, "N.AA"), "ff") | is(assayDataElement(object, "N.AA"), "ffdf")

-		  if(is.ff){

-			  open(assayDataElement(object, "N.AA"))

-			  open(assayDataElement(object, "N.AB"))

-			  open(assayDataElement(object, "N.BB"))

-		  }

-		  N.AA <- as.matrix(assayDataElement(object, "N.AA")[...])

-		  N.AB <- as.matrix(assayDataElement(object, "N.AB")[...])

-		  N.BB <- as.matrix(assayDataElement(object, "N.BB")[...])

-		  if(is.ff){

-			  close(assayDataElement(object, "N.AA"))

-			  close(assayDataElement(object, "N.AB"))

-			  close(assayDataElement(object, "N.BB"))

-		  }

+		  N.AA <- assayDataElement(object, "N.AA")

+		  N.AB <- assayDataElement(object, "N.AB")

+		  N.BB <- assayDataElement(object, "N.BB")

 		  res <- array(NA, dim=c(nrow(N.AA), 3, ncol(N.AA)))

 		  dimnames(res)[[2]] <- c("AA", "AB", "BB")

 		  dimnames(res)[[3]] <- batchnames

-		  res[, "AA", ] <- N.AA

-		  res[, "AB", ] <- N.AB

-		  res[, "BB", ] <- N.BB

+		  res[, "AA", ] <- N.AA[,]

+		  res[, "AB", ] <- N.AB[,]

+		  res[, "BB", ] <- N.BB[,]

 		  return(res)

 	  })

 setMethod("corr", signature(object="AssayData"),

 	  function(object, ...){

-		  dotArgs <- names(list(...))

-		  missing.i <- !("i" %in% dotArgs)

-		  missing.j <- !("j" %in% dotArgs)

-		  if(!missing.j) j <- list(...)[["j"]]

 		  batchnames <- sampleNames(object)

-		  if(!missing.j) batchnames <- batchnames[j]

-

-		  ##if(missing.i & missing.j) stop("Must specify either the rows i or batches j")

-		  is.ff <- is(assayDataElement(object, "corrAA"), "ff") | is(assayDataElement(object, "corrAA"), "ffdf")

-		  if(is.ff){

-			  open(assayDataElement(object, "corrAA"))

-			  open(assayDataElement(object, "corrAB"))

-			  open(assayDataElement(object, "corrBB"))

-		  }

-		  corrAA <- as.matrix(assayDataElement(object, "corrAA")[...])

-		  corrAB <- as.matrix(assayDataElement(object, "corrAB")[...])

-		  corrBB <- as.matrix(assayDataElement(object, "corrBB")[...])

-		  if(is.ff){

-			  close(assayDataElement(object, "corrAA"))

-			  close(assayDataElement(object, "corrAB"))

-			  close(assayDataElement(object, "corrBB"))

-		  }

+		  corrAA <- assayDataElement(object, "corrAA")

+		  corrAB <- assayDataElement(object, "corrAB")

+		  corrBB <- assayDataElement(object, "corrBB")

 		  res <- array(NA, dim=c(nrow(corrAA), 3, ncol(corrAA)))

 		  dimnames(res)[[2]] <- c("AA", "AB", "BB")

 		  dimnames(res)[[3]] <- batchnames

-		  res[, "AA", ] <- corrAA

-		  res[, "AB", ] <- corrAB

-		  res[, "BB", ] <- corrBB

+		  res[, "AA", ] <- corrAA[,]

+		  res[, "AB", ] <- corrAB[,]

+		  res[, "BB", ] <- corrBB[,]

 		  return(res)

 	  })

 setMethod("medians", signature(object="AssayData"),

 	  function(object, ...){

-		  dotArgs <- names(list(...))

-		  missing.i <- !("i" %in% dotArgs)

-		  missing.j <- !("j" %in% dotArgs)

-		  if(!missing.j) j <- list(...)[["j"]]

 		  batchnames <- sampleNames(object)

-		  if(!missing.j) batchnames <- batchnames[j]

-		  if(missing.i & missing.j) stop("Must specify either the rows i or batches j")

-		  medianA.AA <- as.matrix(assayDataElement(object, "medianA.AA")[...])

-		  medianA.AB <- as.matrix(assayDataElement(object, "medianA.AB")[...])

-		  medianA.BB <- as.matrix(assayDataElement(object, "medianA.BB")[...])

-		  medianB.AA <- as.matrix(assayDataElement(object, "medianB.AA")[...])

-		  medianB.AB <- as.matrix(assayDataElement(object, "medianB.AB")[...])

-		  medianB.BB <- as.matrix(assayDataElement(object, "medianB.BB")[...])

+		  medianA.AA <- assayDataElement(object, "medianA.AA")

+		  medianA.AB <- assayDataElement(object, "medianA.AB")

+		  medianA.BB <- assayDataElement(object, "medianA.BB")

+		  medianB.AA <- assayDataElement(object, "medianB.AA")

+		  medianB.AB <- assayDataElement(object, "medianB.AB")

+		  medianB.BB <- assayDataElement(object, "medianB.BB")

 		  res <- array(NA, dim=c(nrow(medianA.AA), 2, 3, ncol(medianA.AA)))

 		  dimnames(res)[[2]] <- c("A", "B")

 		  dimnames(res)[[3]] <- c("AA", "AB", "BB")

 		  dimnames(res)[[4]] <- batchnames

-		  res[, "A", "AA", ] <- medianA.AA

-		  res[, "A", "AB", ] <- medianA.AB

-		  res[, "A", "BB", ] <- medianA.BB

-		  res[, "B", "AA", ] <- medianB.AA

-		  res[, "B", "AB", ] <- medianB.AB

-		  res[, "B", "BB", ] <- medianB.BB

+		  res[, "A", "AA", ] <- medianA.AA[,]

+		  res[, "A", "AB", ] <- medianA.AB[,]

+		  res[, "A", "BB", ] <- medianA.BB[,]

+		  res[, "B", "AA", ] <- medianB.AA[,]

+		  res[, "B", "AB", ] <- medianB.AB[,]

+		  res[, "B", "BB", ] <- medianB.BB[,]

 		  return(res)

 })

 setMethod("mads", signature(object="AssayData"),

 	  function(object, ...){

-		  dotArgs <- names(list(...))

-		  missing.i <- !("i" %in% dotArgs)

-		  missing.j <- !("j" %in% dotArgs)

 		  batchnames <- sampleNames(object)

-		  if(!missing.j) j <- list(...)[["j"]]

-		  if(!missing.j) batchnames <- batchnames[j]

-		  if(missing.i & missing.j) stop("Must specify either the rows i or batches j")

-		  madA.AA <- as.matrix(assayDataElement(object, "madA.AA")[...])

-		  madA.AB <- as.matrix(assayDataElement(object, "madA.AB")[...])

-		  madA.BB <- as.matrix(assayDataElement(object, "madA.BB")[...])

-		  madB.AA <- as.matrix(assayDataElement(object, "madB.AA")[...])

-		  madB.AB <- as.matrix(assayDataElement(object, "madB.AB")[...])

-		  madB.BB <- as.matrix(assayDataElement(object, "madB.BB")[...])

+		  madA.AA <- assayDataElement(object, "madA.AA")

+		  madA.AB <- assayDataElement(object, "madA.AB")

+		  madA.BB <- assayDataElement(object, "madA.BB")

+		  madB.AA <- assayDataElement(object, "madB.AA")

+		  madB.AB <- assayDataElement(object, "madB.AB")

+		  madB.BB <- assayDataElement(object, "madB.BB")

 		  res <- array(NA, dim=c(nrow(madA.AA), 2, 3, ncol(madA.AA)))

 		  dimnames(res)[[2]] <- c("A", "B")

 		  dimnames(res)[[3]] <- c("AA", "AB", "BB")

 		  dimnames(res)[[4]] <- batchnames

-		  res[, "A", "AA", ] <- madA.AA

-		  res[, "A", "AB", ] <- madA.AB

-		  res[, "A", "BB", ] <- madA.BB

-		  res[, "B", "AA", ] <- madB.AA

-		  res[, "B", "AB", ] <- madB.AB

-		  res[, "B", "BB", ] <- madB.BB

+		  res[, "A", "AA", ] <- madA.AA[,]

+		  res[, "A", "AB", ] <- madA.AB[,]

+		  res[, "A", "BB", ] <- madA.BB[,]

+		  res[, "B", "AA", ] <- madB.AA[,]

+		  res[, "B", "AB", ] <- madB.AB[,]

+		  res[, "B", "BB", ] <- madB.BB[,]

 		  return(res)

 })

@@ -122,25 +76,19 @@ setMethod("mads", signature(object="AssayData"),

 setMethod("tau2", signature(object="AssayData"),

 	  function(object, ...){

-		  dotArgs <- names(list(...))

-		  missing.i <- !("i" %in% dotArgs)

-		  missing.j <- !("j" %in% dotArgs)

 		  batchnames <- sampleNames(object)

-		  if(!missing.j) j <- list(...)[["j"]]

-		  if(!missing.j) batchnames <- batchnames[j]

-		  if(missing.i & missing.j) stop("Must specify either the rows i or batches j")

-		  tau2A.AA <- as.matrix(assayDataElement(object, "tau2A.AA")[...])

-		  tau2A.BB <- as.matrix(assayDataElement(object, "tau2A.BB")[...])

-		  tau2B.AA <- as.matrix(assayDataElement(object, "tau2B.AA")[...])

-		  tau2B.BB <- as.matrix(assayDataElement(object, "tau2B.BB")[...])

+		  tau2A.AA <- assayDataElement(object, "tau2A.AA")

+		  tau2A.BB <- assayDataElement(object, "tau2A.BB")

+		  tau2B.AA <- assayDataElement(object, "tau2B.AA")

+		  tau2B.BB <- assayDataElement(object, "tau2B.BB")

 		  res <- array(NA, dim=c(nrow(tau2A.AA), 2, 2, ncol(tau2A.AA)))

 		  dimnames(res)[[2]] <- c("A", "B")

 		  dimnames(res)[[3]] <- c("AA", "BB")

 		  dimnames(res)[[4]] <- batchnames

-		  res[, "A", "AA", ] <- tau2A.AA

-		  res[, "A", "BB", ] <- tau2A.BB

-		  res[, "B", "AA", ] <- tau2B.AA

-		  res[, "B", "BB", ] <- tau2B.BB

+		  res[, "A", "AA", ] <- tau2A.AA[,]

+		  res[, "A", "BB", ] <- tau2A.BB[,]

+		  res[, "B", "AA", ] <- tau2B.AA[,]

+		  res[, "B", "BB", ] <- tau2B.BB[,]

 		  return(res)

 })

@@ -62,9 +62,9 @@ rawCopynumber(object,...)

 \value{

 	nu[A/B] and phi[A/B] return matrices of the intercept and

-	slope coefficients, respectively.  

+	slope coefficients, respectively.

-	CA and CB return matrices of allele-specific copy number.  

+	CA and CB return matrices of allele-specific copy number.

 	totalCopynumber (or rawCopynumber) returns a matrix of CA+CB.

@@ -79,35 +79,35 @@ rawCopynumber(object,...)

 \examples{

 ## Version 1.6* of crlmm used CNSetLM objects.

 data(sample.CNSet)

-all(isCurrent(cnSet)) ## is the cnSet object current?

+all(isCurrent(sample.CNSet)) ## is the cnSet object current?

 ## --------------------------------------------------

 ## calculating allele-specific copy number

 ## --------------------------------------------------

 ## copy number for allele A, first 5 markers, first 2 samples

-(ca <- CA(cnSet, i=1:5, j=1:2))

+(ca <- CA(sample.CNSet, i=1:5, j=1:2))

 ## copy number for allele B, first 5 markers, first 2 samples

-(cb <- CB(cnSet, i=1:5, j=1:2))

+(cb <- CB(sample.CNSet, i=1:5, j=1:2))

 ## total copy number for first 5 markers, first 2 samples

 (cn1 <- ca+cb)

 ## total copy number at first 5 nonpolymorphic loci

-index <- which(!isSnp(cnSet))[1:5]

-cn2 <- CA(cnSet, i=index, j=1:2)

+index <- which(!isSnp(sample.CNSet))[1:5]

+cn2 <- CA(sample.CNSet, i=index, j=1:2)

 ## note, cb is NA at nonpolymorphic loci

-(cb <- CB(cnSet, i=index, j=1:2))

+(cb <- CB(sample.CNSet, i=index, j=1:2))

 ## note, ca+cb will give NAs at nonpolymorphic loci

-CA(cnSet, i=index, j=1:2) + cb

+CA(sample.CNSet, i=index, j=1:2) + cb

 ## A shortcut for total copy number

-cn3 <- totalCopynumber(cnSet, i=1:5, j=1:2)

+cn3 <- totalCopynumber(sample.CNSet, i=1:5, j=1:2)

 all.equal(cn3, cn1)

-cn4 <- totalCopynumber(cnSet, i=index, j=1:2)

+cn4 <- totalCopynumber(sample.CNSet, i=index, j=1:2)

 all.equal(cn4, cn2)

 ## markers 1-5, all samples

-cn5 <- totalCopynumber(cnSet, i=1:5)

+cn5 <- totalCopynumber(sample.CNSet, i=1:5)

 ## all markers, samples 1-5

-cn6 <- totalCopynumber(cnSet, j=1:5)

+cn6 <- totalCopynumber(sample.CNSet, j=1:5)

 ## NOTE: subsetting the object before extracting copy number

 ##       can be very inefficient when the data set is very large,

@@ -116,7 +116,7 @@ cn6 <- totalCopynumber(cnSet, j=1:5)

 ##       and all of the elements in the LinearModelParameter slot

 \dontrun{

         ## do not do the following

-	cn <- CA(cnSet[1:5, ], "A")

+	cn <- CA(sample.CNSet[1:5, ], "A")

 }

 }

 \keyword{manip}

@@ -13,8 +13,6 @@ genotype(filenames, cdfName, batch, mixtureSampleSize = 10^5, eps =0.1,

          verbose = TRUE, seed = 1, sns, 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}

@@ -34,7 +32,8 @@ genotype(filenames, cdfName, batch, mixtureSampleSize = 10^5, eps =0.1,

   \item{recallRegMin}{Minimum number of SNP's for regression.}

   \item{gender}{  integer vector (  male = 1, female =2 ) or missing,

   with same length as filenames.  If missing, the gender is predicted.}

-  \item{returnParams}{'logical'. Return recalibrated parameters from crlmm.}

+  \item{returnParams}{'logical'. Return recalibrated parameters from

+    crlmm.}

   \item{badSNP}{'numeric'. Threshold to flag as bad SNP (affects batchQC)}

 }

@@ -91,7 +90,6 @@ if (require(ff) & require(genomewidesnp6Crlmm) & require(hapmapsnp6)){

   ## the filenames with full path...

   ## very useful when genotyping samples not in the working directory

   cels <- list.celfiles(path, full.names=TRUE)

-

   ## Note: one would need at least 10 CEL files for copy number estimation

   ## To use less RAM, specify a smaller argument to ocProbesets

   ocProbesets(50e3)

@@ -108,9 +106,6 @@ if (require(ff) & require(genomewidesnp6Crlmm) & require(hapmapsnp6)){

   gender <- c("female", "female", "male")

   gender[gender == "female"] <- 2

   gender[gender == "male"] <- 1

-\dontrun{

-  cnSet2 <- (cnSet <- genotype(cels, cdfName="genomewidesnp6", batch=batch, gender=as.integer(gender)))

-}

   dim(cnSet)

   table(isSnp(cnSet))

 }

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

 \name{genotypes}

 \alias{genotypes}

-%- Also NEED an '\alias' for EACH other topic documented here.

+

 \title{

   The possible genotypes for an integer copy number.

 }

@@ -10,15 +10,12 @@

 \usage{

 genotypes(copyNumber)

 }

-%- maybe also 'usage' for other objects documented here.

+

 \arguments{

   \item{copyNumber}{

-    Integer (0-4 allowed).

+    Integer (0-4 allowed).}

 }

-}

-\details{

-}

 \value{

   Character vector.

 }

@@ -27,11 +24,10 @@ genotypes(copyNumber)

 R. Scharpf

 }

-

 \examples{

+

 for(i in 0:4) print(genotypes(i))

+

 }

-% Add one or more standard keywords, see file 'KEYWORDS' in the

-% R documentation directory.

-\keyword{manip}

-x

+

+\keyword{manip}

\ No newline at end of file

@@ -27,7 +27,7 @@ posteriorProbability(object, predictRegion, copyNumber = 0:4, w)

   \item{w}{ numeric vector of prior probabilities for each of the copy

     number states.  Must be the same length as \code{copyNumber} and sum

-    to 1.

+    to 1.}

 }

 \details{