@@ -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: 1.11.55

+Version: 1.11.56

 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>

deleted file mode 100644

Binary files a/data/sample.CNSet.rda and /dev/null differ

deleted file mode 100644

Binary files a/data/sample.CNSet2.rda and /dev/null differ

@@ -73,11 +73,11 @@ log-scale the variance is rougly constant for CA, CB > 0).

 }

 \examples{

-data(sample.CNSet)

-Ns(sample.CNSet)[1:5, , ]

-corr(sample.CNSet)[1:5, , ]

-meds <- medians(sample.CNSet)

-mads(sample.CNSet)[1:5, , ,]

-tau2(sample.CNSet)[1:5, , ,]

+data(cnSetExample)

+Ns(cnSetExample)[1:5, , ]

+corr(cnSetExample)[1:5, , ]

+meds <- medians(cnSetExample)

+mads(cnSetExample)[1:5, , ,]

+tau2(cnSetExample)[1:5, , ,]

 }

 \keyword{manip}

@@ -39,8 +39,8 @@ calculateRBaf(object, batch.name)

 \examples{

-data(sample.CNSet)

-baf.lrr <- calculateRBaf(sample.CNSet, "SHELF")

+data(cnSetExample)

+baf.lrr <- calculateRBaf(cnSetExample, "SHELF")

 hist(baf.lrr[["baf"]], breaks=100)

 hist(baf.lrr[["lrr"]], breaks=100)

@@ -88,36 +88,36 @@ elements and all of the elements in the LinearModelParameter slot.

 \examples{

 ## Version 1.6* of crlmm used CNSetLM objects.

-data(sample.CNSet)

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

+data(cnSetExample)

+all(isCurrent(cnSetExample)) ## is the cnSet object current?

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

 ## calculating allele-specific copy number

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

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

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

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

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

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

+(cb <- CB(cnSetExample, 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(sample.CNSet))[1:5]

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

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

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

 ## note, cb is NA at nonpolymorphic loci

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

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

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

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

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

 ## A shortcut for total copy number

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

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

 all.equal(cn3, cn1)

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

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

 all.equal(cn4, cn2)

 ## markers 1-5, all samples

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

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

 ## all markers, samples 1-5

-cn6 <- totalCopynumber(sample.CNSet, j=1:2)

+cn6 <- totalCopynumber(cnSetExample, j=1:2)

 }

 \keyword{manip}

@@ -34,14 +34,14 @@ R. Scharpf

 }

 \examples{

-data(sample.CNSet2)

-table(batch(sample.CNSet2))

-sample.index <- which(batch(sample.CNSet2) == "CUPID")

+data(cnSetExample2)

+table(batch(cnSetExample2))

+sample.index <- which(batch(cnSetExample2) == "CUPID")

 ## A single SNP

-pr <- predictionRegion(sample.CNSet2[1:4, sample.index], copyNumber=0:4)

-gt <- calls(sample.CNSet2[1:4, sample.index])

+pr <- predictionRegion(cnSetExample2[1:4, sample.index], copyNumber=0:4)

+gt <- calls(cnSetExample2[1:4, sample.index])

 lim <- c(6,13)

-xyplot(B~A|snpid, data=sample.CNSet2[1:4, sample.index],

+xyplot(B~A|snpid, data=cnSetExample2[1:4, sample.index],

        predictRegion=pr,

        panel=ABpanel,

        pch=21,

@@ -50,12 +50,12 @@ xyplot(B~A|snpid, data=sample.CNSet2[1:4, sample.index],

 ## multiple SNPs, prediction regions for 3 batches

 \dontrun{

-	tab <- table(batch(sample.CNSet2))

+	tab <- table(batch(cnSetExample2))

 	bns <- names(tab)[tab > 50]

-	sample.index <- which(batch(sample.CNSet2) %in% bns[1:3])

-	pr <- predictionRegion(sample.CNSet2[1:10, sample.index], copyNumber=0:4)

-	gt <- as.integer(calls(sample.CNSet2[1:10, sample.index]))

-	xyplot(B~A|snpid, data=sample.CNSet2[1:10, sample.index],

+	sample.index <- which(batch(cnSetExample2) %in% bns[1:3])

+	pr <- predictionRegion(cnSetExample2[1:10, sample.index], copyNumber=0:4)

+	gt <- as.integer(calls(cnSetExample2[1:10, sample.index]))

+	xyplot(B~A|snpid, data=cnSetExample2[1:10, sample.index],

 	       predictRegion=pr,

 	       panel=ABpanel,

 	       pch=21,

@@ -63,20 +63,17 @@ xyplot(B~A|snpid, data=sample.CNSet2[1:4, sample.index],

 	       xlim=c(6,12), ylim=c(6,12))

 	## nonpolymorphic markers

-	data(sample.CNSet2)

-	tab <- table(batch(sample.CNSet2))

+	data(cnSetExample2)

+	tab <- table(batch(cnSetExample2))

 	bns <- names(tab)[tab > 50]

-	sample.index <- which(batch(sample.CNSet2)%in%bns[1:3])

-	np.index <- which(!isSnp(sample.CNSet2))[1:10]

-	taus <- tau2(sample.CNSet)[np.index, , , ]

-	##trace(predictionRegion, browser, signature=c("CNSet", "integer"))

-	pr <- predictionRegion(sample.CNSet2[np.index, sample.index],

+	sample.index <- which(batch(cnSetExample2)%in%bns[1:3])

+	np.index <- which(!isSnp(cnSetExample2))[1:10]

+	taus <- tau2(cnSetExample)[np.index, , , ]

+	pr <- predictionRegion(cnSetExample2[np.index, sample.index],

 			       copyNumber=0:4)

-	##trace(posteriorProbability, browser, signature="CNSet")

-	pp <- posteriorProbability(sample.CNSet2[np.index, sample.index],

+	pp <- posteriorProbability(cnSetExample2[np.index, sample.index],

 				   predictRegion=pr,

 				   copyNumber=0:4)

-	pm <- calculatePosteriorMean(sample.CNSet2[np.index, sample.index], posteriorProb=pp)

 }

 }

 \keyword{dplot}