* collab:
coercion of CNSet to oligoSnpSet uses lrr/baf instead of ca+cb/genotypes
git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/crlmm@62557 bc3139a8-67e5-0310-9ffc-ced21a209358
Rob Scharp authored on 05/02/2012 15:20:45
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@@ -1,7 +1,7 @@ |
| 1 | 1 |
Package: crlmm |
| 2 | 2 |
Type: Package |
| 3 | 3 |
Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays. |
| 4 |
-Version: 1.13.7 |
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+Version: 1.13.8 |
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| 5 | 5 |
Date: 2010-12-10 |
| 6 | 6 |
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 |
| 7 | 7 |
Maintainer: Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.EDU.AU> |
| ... | ... |
@@ -52,9 +52,15 @@ importFrom(mvtnorm, dmvnorm) |
| 52 | 52 |
importFrom(ellipse, ellipse) |
| 53 | 53 |
##importFrom(ff, ffdf, physical.ff, physical.ffdf, ffrowapply) |
| 54 | 54 |
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-##importClassesFrom(oligoClasses, ff_matrix, ffdf) |
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-##exportMethods(lines) |
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-exportMethods(CA, CB) |
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+##---------------------------------------------------------------------------- |
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+## export |
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+##---------------------------------------------------------------------------- |
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+exportClasses(PredictionRegion) |
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+exportMethods(CA, CB, coerce, |
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+ A, B, corr, nuA, nuB, phiA, phiB, |
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+ predictionRegion, posteriorProbability, |
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+ tau2, Ns, medians, mads, |
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+ xyplot, calculateRBaf) |
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| 58 | 64 |
export(crlmm, |
| 59 | 65 |
crlmmIllumina, |
| 60 | 66 |
crlmmIlluminaV2, |
| ... | ... |
@@ -69,7 +75,4 @@ export(crlmm, |
| 69 | 75 |
genotype.Illumina, |
| 70 | 76 |
crlmmCopynumber2, crlmmCopynumberLD, crlmmCopynumber) |
| 71 | 77 |
export(genotypes, totalCopynumber, rawCopynumber, xyplot) |
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-exportMethods(A, B, corr, nuA, nuB, phiA, phiB, predictionRegion, posteriorProbability, tau2, Ns, medians, mads, |
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- xyplot, calculateRBaf) |
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| 74 | 78 |
export(ABpanel, constructInf, preprocessInf, genotypeInf, validCEL, celDates) |
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-exportClasses(PredictionRegion) |
| ... | ... |
@@ -1,6 +1,62 @@ |
| 1 | 1 |
setMethod("posteriorMean", signature(object="CNSet"), function(object) assayDataElement(object, "posteriorMean"))
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| 2 | 2 |
setReplaceMethod("posteriorMean", signature(object="CNSet", value="matrix"), function(object, value) assayDataElementReplace(object, "posteriorMean", value))
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| 3 | 3 |
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+setAs("CNSet", "oligoSnpSet", function(from, to){
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+ cnSet2oligoSnpSet(from) |
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+}) |
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+ |
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+cnSet2oligoSnpSet <- function(object){
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+ row.index <- seq_len(nrow(object)) |
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+ col.index <- seq_len(ncol(object)) |
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+ is.lds <- ifelse(is(calls(object), "ff_matrix") | is(calls(object), "ffdf"), TRUE, FALSE) |
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+ if(is.lds) stopifnot(isPackageLoaded("ff"))
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+ b.r <- calculateRBaf(object) |
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+## if(is.lds){
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+## ## initialize a big matrix for raw copy number |
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+## message("creating an ff object for storing total copy number")
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+## tcn <- initializeBigMatrix(name="total_cn", nrow(object), ncol(object), vmode="double") |
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+## for(j in 1:ncol(object)){
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+## tcn[, j] <- totalCopynumber(object, i=row.index, j=j) |
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+## } |
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+## } else {
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+## if(ncol(object) > 5){
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+## ##this can be memory intensive, so we try to be careful |
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+## col.index <- splitIndicesByLength(seq(length=ncol(object)), 5) |
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+## tcn <- matrix(NA, nrow(object), ncol(object)) |
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+## dimnames(tcn) <- list(featureNames(object), sampleNames(object)) |
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+## rows <- 1:nrow(object) |
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+## for(i in seq_along(col.index)){
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+## cat(".")
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+## j <- col.index[[i]] |
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+## cnSet <- object[, j] |
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+## tcn[, j] <- totalCopynumber(cnSet, i=row.index, j=1:ncol(cnSet)) |
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+## rm(cnSet); gc() |
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+## } |
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+## cat("\n")
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+## } else {
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+## tcn <- totalCopynumber(object, i=row.index, j=col.index) |
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+## } |
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+## } |
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+## message("Transforming copy number to log2 scale")
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+## tcn[tcn < 0.1] <- 0.1 |
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+## tcn[tcn > 8] <- 8 |
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+## log.tcn <- log2(tcn) |
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+ tmp <- new("oligoSnpSet",
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+ ##copyNumber=log.tcn, |
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+ copyNumber=b.r[[2]], |
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+ ##baf=b.r[[1]], |
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+ call=calls(object), |
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+ callProbability=snpCallProbability(object), |
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+ annotation=annotation(object), |
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+ featureData=featureData(object), |
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+ phenoData=phenoData(object), |
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+ experimentData=experimentData(object), |
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+ protocolData=protocolData(object)) |
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+ tmp <- assayDataElementReplace(tmp, "baf", b.r[[1]]) |
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+ return(tmp) |
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+} |
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+ |
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+ |
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| 4 | 60 |
linearParamElementReplace <- function(obj, elt, value) {
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| 5 | 61 |
storage.mode <- storageMode(batchStatistics(obj)) |
| 6 | 62 |
switch(storage.mode, |