Browse code

Replace sample.CNSet and sample.CNSet2 with cnSetExample and cnSetExample2

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/crlmm@59039 bc3139a8-67e5-0310-9ffc-ced21a209358

Rob Scharp authored on 11/10/2011 12:17:42
Showing 7 changed files

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@@ -1,7 +1,7 @@
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 Package: crlmm
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 Type: Package
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 Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays.
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-Version: 1.11.55
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+Version: 1.11.56
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 Date: 2010-12-10
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 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
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 Maintainer: Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.EDU.AU>
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deleted file mode 100644
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Binary files a/data/sample.CNSet.rda and /dev/null differ
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deleted file mode 100644
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Binary files a/data/sample.CNSet2.rda and /dev/null differ
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@@ -73,11 +73,11 @@ log-scale the variance is rougly constant for CA, CB > 0).
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 }
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 \examples{
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-data(sample.CNSet)
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-Ns(sample.CNSet)[1:5, , ]
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-corr(sample.CNSet)[1:5, , ]
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-meds <- medians(sample.CNSet)
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-mads(sample.CNSet)[1:5, , ,]
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-tau2(sample.CNSet)[1:5, , ,]
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+data(cnSetExample)
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+Ns(cnSetExample)[1:5, , ]
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+corr(cnSetExample)[1:5, , ]
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+meds <- medians(cnSetExample)
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+mads(cnSetExample)[1:5, , ,]
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+tau2(cnSetExample)[1:5, , ,]
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 }
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 \keyword{manip}
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@@ -39,8 +39,8 @@ calculateRBaf(object, batch.name)
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 \examples{
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-data(sample.CNSet)
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-baf.lrr <- calculateRBaf(sample.CNSet, "SHELF")
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+data(cnSetExample)
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+baf.lrr <- calculateRBaf(cnSetExample, "SHELF")
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 hist(baf.lrr[["baf"]], breaks=100)
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 hist(baf.lrr[["lrr"]], breaks=100)
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@@ -88,36 +88,36 @@ elements and all of the elements in the LinearModelParameter slot.
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 \examples{
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 ## Version 1.6* of crlmm used CNSetLM objects.
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-data(sample.CNSet)
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-all(isCurrent(sample.CNSet)) ## is the cnSet object current?
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+data(cnSetExample)
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+all(isCurrent(cnSetExample)) ## is the cnSet object current?
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 ## --------------------------------------------------
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 ## calculating allele-specific copy number
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 ## --------------------------------------------------
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 ## copy number for allele A, first 5 markers, first 2 samples
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-(ca <- CA(sample.CNSet, i=1:5, j=1:2))
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+(ca <- CA(cnSetExample, i=1:5, j=1:2))
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 ## copy number for allele B, first 5 markers, first 2 samples
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-(cb <- CB(sample.CNSet, i=1:5, j=1:2))
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+(cb <- CB(cnSetExample, i=1:5, j=1:2))
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 ## total copy number for first 5 markers, first 2 samples
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 (cn1 <- ca+cb)
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 ## total copy number at first 5 nonpolymorphic loci
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-index <- which(!isSnp(sample.CNSet))[1:5]
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-cn2 <- CA(sample.CNSet, i=index, j=1:2)
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+index <- which(!isSnp(cnSetExample))[1:5]
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+cn2 <- CA(cnSetExample, i=index, j=1:2)
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 ## note, cb is NA at nonpolymorphic loci
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-(cb <- CB(sample.CNSet, i=index, j=1:2))
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+(cb <- CB(cnSetExample, i=index, j=1:2))
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 ## note, ca+cb will give NAs at nonpolymorphic loci
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-CA(sample.CNSet, i=index, j=1:2) + cb
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+CA(cnSetExample, i=index, j=1:2) + cb
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 ## A shortcut for total copy number
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-cn3 <- totalCopynumber(sample.CNSet, i=1:5, j=1:2)
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+cn3 <- totalCopynumber(cnSetExample, i=1:5, j=1:2)
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 all.equal(cn3, cn1)
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-cn4 <- totalCopynumber(sample.CNSet, i=index, j=1:2)
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+cn4 <- totalCopynumber(cnSetExample, i=index, j=1:2)
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 all.equal(cn4, cn2)
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 ## markers 1-5, all samples
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-cn5 <- totalCopynumber(sample.CNSet, i=1:5)
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+cn5 <- totalCopynumber(cnSetExample, i=1:5)
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 ## all markers, samples 1-5
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-cn6 <- totalCopynumber(sample.CNSet, j=1:2)
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+cn6 <- totalCopynumber(cnSetExample, j=1:2)
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 }
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 \keyword{manip}
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@@ -34,14 +34,14 @@ R. Scharpf
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 }
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 \examples{
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-data(sample.CNSet2)
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-table(batch(sample.CNSet2))
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-sample.index <- which(batch(sample.CNSet2) == "CUPID")
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+data(cnSetExample2)
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+table(batch(cnSetExample2))
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+sample.index <- which(batch(cnSetExample2) == "CUPID")
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 ## A single SNP
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-pr <- predictionRegion(sample.CNSet2[1:4, sample.index], copyNumber=0:4)
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-gt <- calls(sample.CNSet2[1:4, sample.index])
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+pr <- predictionRegion(cnSetExample2[1:4, sample.index], copyNumber=0:4)
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+gt <- calls(cnSetExample2[1:4, sample.index])
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 lim <- c(6,13)
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-xyplot(B~A|snpid, data=sample.CNSet2[1:4, sample.index],
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+xyplot(B~A|snpid, data=cnSetExample2[1:4, sample.index],
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        predictRegion=pr,
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        panel=ABpanel,
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        pch=21,
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@@ -50,12 +50,12 @@ xyplot(B~A|snpid, data=sample.CNSet2[1:4, sample.index],
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 ## multiple SNPs, prediction regions for 3 batches
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 \dontrun{
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-	tab <- table(batch(sample.CNSet2))
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+	tab <- table(batch(cnSetExample2))
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 	bns <- names(tab)[tab > 50]
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-	sample.index <- which(batch(sample.CNSet2) %in% bns[1:3])
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-	pr <- predictionRegion(sample.CNSet2[1:10, sample.index], copyNumber=0:4)
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-	gt <- as.integer(calls(sample.CNSet2[1:10, sample.index]))
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-	xyplot(B~A|snpid, data=sample.CNSet2[1:10, sample.index],
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+	sample.index <- which(batch(cnSetExample2) %in% bns[1:3])
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+	pr <- predictionRegion(cnSetExample2[1:10, sample.index], copyNumber=0:4)
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+	gt <- as.integer(calls(cnSetExample2[1:10, sample.index]))
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+	xyplot(B~A|snpid, data=cnSetExample2[1:10, sample.index],
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 	       predictRegion=pr,
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 	       panel=ABpanel,
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 	       pch=21,
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@@ -63,20 +63,17 @@ xyplot(B~A|snpid, data=sample.CNSet2[1:4, sample.index],
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 	       xlim=c(6,12), ylim=c(6,12))
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 	## nonpolymorphic markers
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-	data(sample.CNSet2)
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-	tab <- table(batch(sample.CNSet2))
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+	data(cnSetExample2)
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+	tab <- table(batch(cnSetExample2))
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 	bns <- names(tab)[tab > 50]
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-	sample.index <- which(batch(sample.CNSet2)%in%bns[1:3])
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-	np.index <- which(!isSnp(sample.CNSet2))[1:10]
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-	taus <- tau2(sample.CNSet)[np.index, , , ]
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-	##trace(predictionRegion, browser, signature=c("CNSet", "integer"))
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-	pr <- predictionRegion(sample.CNSet2[np.index, sample.index],
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+	sample.index <- which(batch(cnSetExample2)%in%bns[1:3])
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+	np.index <- which(!isSnp(cnSetExample2))[1:10]
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+	taus <- tau2(cnSetExample)[np.index, , , ]
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+	pr <- predictionRegion(cnSetExample2[np.index, sample.index],
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 			       copyNumber=0:4)
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-	##trace(posteriorProbability, browser, signature="CNSet")
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-	pp <- posteriorProbability(sample.CNSet2[np.index, sample.index],
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+	pp <- posteriorProbability(cnSetExample2[np.index, sample.index],
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 				   predictRegion=pr,
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 				   copyNumber=0:4)
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-	pm <- calculatePosteriorMean(sample.CNSet2[np.index, sample.index], posteriorProb=pp)
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 }
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 }
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 \keyword{dplot}