Browse code

Merge branch 'collab'

* collab:
bump version
made cnSetExample smaller. Fix notes
Trying to revert bad commit
remove cn-functions. update description
comment most of cn-functions.r
Resaved rdas
update data/cnSetExample.rda and data/cnSetExample2.rda
bump version
coercion method from CNSet to oligoSnpSet makes integer matrices of BAFs and lrr's
import ff_or_matrix from oligoClasses. bump dependency on oligoClasses version. Use library(oligoClasses) in some of the crlmm examples.
Cleaning pkg loading process: work still required
move Biobase and methods to imports

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/crlmm@64324 bc3139a8-67e5-0310-9ffc-ced21a209358

Rob Scharp authored on 23/03/2012 03:34:50
Showing13 changed files

... ...
@@ -1,21 +1,21 @@
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: 1.13.12
5
-Date: 2010-12-10
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
4
+Version: 1.13.14
5
+Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry
7 6
 Maintainer: Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.EDU.AU>
8 7
 Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms
9 8
 License: Artistic-2.0
10
-Depends: R (>= 2.13.0),
11
-         methods,
12
-         Biobase (>= 2.15.0),
13
-         oligoClasses (>= 1.17.34)
14
-Imports: affyio (>= 1.19.2),
9
+Depends: R (>= 2.14.0)
10
+Imports: methods,
11
+         Biobase (>= 2.15.4),
12
+         oligoClasses (>= 1.17.36),
13
+         BiocGenerics,
14
+         affyio (>= 1.23.2),
15 15
          ellipse,
16
-         genefilter (>= 1.33.0),
16
+         genefilter (>= 1.37.1),
17 17
          mvtnorm,
18
-         preprocessCore (>= 1.13.4),
18
+         preprocessCore (>= 1.17.7),
19 19
          splines,
20 20
          stats,
21 21
          SNPchip,
... ...
@@ -41,10 +41,8 @@ Collate: AllGenerics.R
41 41
 	 crlmmGT2.R
42 42
          crlmm-illumina.R
43 43
 	 snprma-functions.R
44
-	 cn-functions.R
45 44
          utils.R
46 45
          zzz.R
47 46
 	 test_crlmm_package.R
48 47
 LazyLoad: yes
49 48
 biocViews: Microarray, Preprocessing, SNP, Bioinformatics, CopyNumberVariants
50
-Packaged: 2011-04-30 17:10:59 UTC; biocbuild
51 49
\ No newline at end of file
... ...
@@ -1,56 +1,68 @@
1 1
 useDynLib("crlmm", .registration=TRUE)
2 2
 
3
-##---------------------------------------------------------------------------
4
-## Biobase
5
-##---------------------------------------------------------------------------
6
-importClassesFrom(Biobase, AnnotatedDataFrame, AssayData, eSet, SnpSet,
7
-		  NChannelSet, MIAME, Versioned, VersionedBiobase,
8
-		  Versions)
9
-importMethodsFrom(Biobase, annotation, "annotation<-",
10
-                  annotatedDataFrameFrom, assayData, "assayData<-",
11
-                  combine, dims, experimentData, "experimentData<-",
12
-                  fData, featureData, "featureData<-", featureNames,
13
-                  fvarMetadata, fvarLabels, pData, "pData<-", phenoData,
14
-                  "phenoData<-", protocolData, "protocolData<-",
15
-                  pubMedIds, rowMedians, sampleNames, snpCall,
16
-                  snpCallProbability,
17
-		  "snpCall<-", "snpCallProbability<-", storageMode,
18
-                  "storageMode<-", updateObject, varLabels)
19
-importFrom(Biobase, assayDataElement, assayDataElementNames,
20
-           assayDataElementReplace, assayDataNew, classVersion,
21
-           validMsg)
22
-
23
-##---------------------------------------------------------------------------
24
-## oligoClasses
25
-##---------------------------------------------------------------------------
26
-importClassesFrom(oligoClasses, SnpSuperSet, AlleleSet, CNSet)##, ff_or_matrix)
27
-importMethodsFrom(oligoClasses, allele, calls, "calls<-", confs,
28
-		  "confs<-", cnConfidence, "cnConfidence<-", isSnp,
29
-		  chromosome, position, A, B,
30
-		  "A<-", "B<-", open, close, flags,
31
-		  openff, closeff,
32
-		  batchStatistics, "batchStatistics<-", updateObject,
33
-		  checkOrder)
34
-
35
-importFrom(oligoClasses, chromosome2integer, celfileDate, list.celfiles,
36
-           copyNumber, initializeBigMatrix, initializeBigVector, isPackageLoaded)
37
-
38
-
39
-importFrom(graphics, abline, axis, layout, legend, mtext, par, plot,
40
-           polygon, rect, segments, text, points, boxplot, lines)
41
-
42
-importFrom(lattice, xyplot, simpleKey, panel.grid, panel.xyplot, lrect, ltext,
43
-	   lpoints, panel.number, lpolygon)
44
-
45
-importFrom(grDevices, grey)
46
-importFrom(affyio, read.celfile.header, read.celfile)
47
-importFrom(preprocessCore, normalize.quantiles.use.target, normalize.quantiles)
48
-importFrom(utils, data, packageDescription, setTxtProgressBar, txtProgressBar)
49
-importFrom(stats, coef, cov, dnorm, kmeans, lm, mad, median, quantile, sd, update)
50
-importFrom(genefilter, rowSds)
51
-importFrom(mvtnorm, dmvnorm)
3
+importClassesFrom(Biobase, AssayData, eSet)
4
+
5
+importClassesFrom(methods, ANY, character, formula, integer, list,
6
+                  matrix, oldClass)
7
+importFrom(methods, setOldClass)
8
+
9
+## importClassesFrom(oligoClasses, CNSet, CNSetLM, ff_matrix,
10
+##                   ff_or_matrix, oligoSnpSet)
11
+importClassesFrom(oligoClasses, CNSet, oligoSnpSet, ff_or_matrix)
12
+##setOldClass(ff_or_matrix)
13
+
14
+importMethodsFrom(Biobase, annotatedDataFrameFrom, annotation,
15
+                  assayData, experimentData, featureData,
16
+                  "featureData<-", featureNames, "featureNames<-",
17
+                  pData, "pData<-", phenoData, "phenoData<-",
18
+                  protocolData, "protocolData<-", rowMedians,
19
+                  sampleNames, snpCall, "snpCall<-",
20
+                  snpCallProbability, "snpCallProbability<-",
21
+                  storageMode, "storageMode<-", varLabels)
22
+
23
+importMethodsFrom(BiocGenerics, cbind, colnames, Filter, get,
24
+                  intersect, lapply, ncol, NCOL, nrow, NROW, order,
25
+                  paste, pmax, pmin, rbind, rownames, sapply, setdiff,
26
+                  table, union, unique)
27
+
28
+importMethodsFrom(genefilter, show)
29
+
30
+importMethodsFrom(oligoClasses, A, "A<-", B, batch, batchNames,
31
+                  batchStatistics, "batchStatistics<-", calls,
32
+                  chromosome, close, confs, flags, isSnp, mean, nu,
33
+                  open, phi, "sampleNames<-")
34
+
35
+importFrom(affyio, read.celfile, read.celfile.header)
36
+
37
+importFrom(Biobase, assayDataElement, assayDataElementReplace,
38
+           assayDataNew, copyEnv)
39
+
52 40
 importFrom(ellipse, ellipse)
53
-##importFrom(ff, ffdf, physical.ff, physical.ffdf, ffrowapply)
41
+
42
+importFrom(genefilter, rowSds)
43
+
44
+importFrom(lattice, lpolygon, panel.grid, panel.number, panel.xyplot,
45
+           xyplot)
46
+
47
+importFrom(methods, "@<-", as, callNextMethod, is, new, validObject)
48
+
49
+importFrom(mvtnorm, rmvnorm)
50
+
51
+importFrom(oligoClasses, celfileDate, chromosome2integer, i2p,
52
+           initializeBigMatrix, initializeBigVector, integerMatrix,
53
+	   isPackageLoaded,
54
+           ldPath, ocLapply, ocProbesets, ocSamples,
55
+	   parStatus,
56
+           splitIndicesByLength, splitIndicesByNode)
57
+
58
+importFrom(preprocessCore, normalize.quantiles,
59
+           normalize.quantiles.use.target)
60
+
61
+importFrom(stats, coef, cov, dnorm, kmeans, lm, mad, median, quantile,
62
+           sd)
63
+
64
+importFrom(utils, packageDescription, setTxtProgressBar,
65
+           txtProgressBar)
54 66
 
55 67
 ##----------------------------------------------------------------------------
56 68
 ## export
... ...
@@ -1,4 +1,5 @@
1 1
 setOldClass("ellipse")
2 2
 setOldClass("ffdf")
3
+##setOldClass("ff_matrix")
3 4
 ##setClassUnion("ff_or_matrix", c("ffdf", "ff_matrix", "matrix"))
4 5
 setClass("PredictionRegion", contains="list")
5 6
deleted file mode 100644
... ...
@@ -1,117 +0,0 @@
1
-copynumber <- function(filenames,
2
-		       batch,
3
-		       summaries=c("lrr", "baf", "ca", "cb", "gt", "gtconf"),
4
-		       write=FALSE,
5
-		       outdir=".",
6
-		       onefile=FALSE,
7
-		       rda=TRUE){
8
-	fnamelist <- split(filenames, batch)
9
-	batchlist <- split(batch, batchlist)
10
-	## for each element in list.  Avoid
11
-	foreach(i=fnamelist) %do% simpleusage(filenames=fnameslist[[i]], batch=batchlist[[i]])
12
-}
13
-
14
-simpleusage <- function(filenames, batch, ...){
15
-	object <- genotype(fnamelist, batch, ...)
16
-	## run genotype on each element in fnamelist
17
-	object <- genotypeSummary(object)
18
-	## different than currently implemented
19
-	##    - shrink to saved batch medians, etc.
20
-	##    - shinkage across markers (?)
21
-	object <- shrinkSummary(object)
22
-	results <- array(NA, nrow(object), ncol(object), length(summaries))
23
-	if(c("ca", "cb") %in% summaries){
24
-		## estimateCnParameters
25
-		##results <- estimatecnParameters(...)
26
-	} else results <- NULL
27
-	is.baf <- "baf" %in% summaries || "lrr" %in% summaries
28
-	if(is.baf){
29
-		results2 <- calculateRtheta(object) ## returns list
30
-		## keep as list, or coerce to array
31
-	}
32
-	if(write){
33
-		##write2(, onefile=onefile)
34
-	}
35
-	return(results)
36
-}
37
-
38
-
39
-imputeTheta <- function(ia, ib, theta, S=100){
40
-##	y <- rbind(y1, y2)
41
-	y <- theta
42
-	N <- nrow(y); p <- ncol(y)
43
-
44
-	mu0 <- apply(y, 2, mean, na.rm=TRUE)
45
-	sd0 <- mu0/5
46
-	L0 <- matrix(.1, p, p); diag(L0) <- 1; L0 <- L0*outer(sd0, sd0)
47
-	nu0 <- p+2; S0 <- L0
48
-
49
-	## starting values
50
-	Sigma <- S0
51
-	Y.full <- y
52
-	O <- 1*(!is.na(y))
53
-	if(all(O[1, ] == 0)){
54
-		return(rep(NA, length(ia)))
55
-	}
56
-	for(j in seq_len(p)) Y.full[is.na(Y.full[, j]), j] <- mu0[j]
57
-	## Gibbs sampler
58
-	##THETA <- SIGMA <- Y.MISS <- NULL
59
-	## problems:  approx. 90 observations for the means in the other batches
60
-	##   -- only 3 observations for the mean in the current batch.
61
-##	THETA <- matrix(NA, iter, p)
62
-##	SIGMA <- matrix(NA, iter, p^2)
63
-	Y.MISS <- matrix(NA, S, sum(O[1,]==0))
64
-	bafs <- matrix(NA, S, length(a))
65
-	for(s in seq_len(S)){
66
-		## update lambda
67
-		ybar <- apply(Y.full, 2, mean)
68
-		Ln <- solve(solve(L0) + N*solve(Sigma))
69
-		mun <- Ln%*%(solve(L0)%*%mu0 + N*solve(Sigma)%*%ybar)
70
-		lambda <- rmvnorm(1, mun, Ln)
71
-
72
-		##update sigma
73
-		Sn <- S0+(t(Y.full)-c(lambda))%*%t(t(Y.full)-c(lambda))
74
-		Sigma <- solve(rwish(nu0+N, solve(Sn)))
75
-
76
-		## update missing data (only care about the first row.)
77
-		a <- O[1, ] == 1
78
-		b <- O[1, ] == 0
79
-		iSa <- solve(Sigma[a,a])
80
-		beta.j <- Sigma[b,a]%*%iSa
81
-		Sigma.j <- Sigma[b,b]-Sigma[b,a]%*%iSa%*%Sigma[a,b]
82
-		lambda.j <- lambda[b]+beta.j%*%(t(Y.full[1,a, drop=FALSE]-lambda[a]))
83
-		Y.full[1,b] <- rmvnorm(1, lambda.j, Sigma.j)
84
-		##SIGMA[s, ] <- c(Sigma)
85
-		Y.MISS[s, ] <- Y.full[1, O[1, ]==0]
86
-	}
87
-	na.cols <- which(is.na(y[1, ]))
88
-	THETA <- matrix(NA, S, 3)
89
-	THETA[, na.cols] <- Y.MISS
90
-	if(length(na.cols) < length(ia))
91
-		THETA[, -na.cols] <- y[1, -na.cols]
92
-
93
-	obs.theta <- atan2(ib, ia)*2/pi
94
-	theta.aa <- matrix(THETA[, 1], S, 3)
95
-	theta.ab <- matrix(THETA[, 2], S, 3)
96
-	theta.bb <- matrix(THETA[, 3], S, 3)
97
-	lessAA <- obs.theta < theta.aa
98
-	lessAB <- obs.theta < theta.ab
99
-	lessBB <- obs.theta < theta.bb
100
-	grAA <- !lessAA ## >= theta.aa
101
-	grAB <- !lessAB ## >= theta.ab
102
-	grBB <- !lessBB ## >= theta.bb
103
-	##not.na <- !is.na(theta.aa)
104
-	I1 <- grAA & lessAB
105
-	I2 <- grAB & lessBB
106
-	##mu <- apply(Y.MISS, 2, mean)
107
-	##bf <- matrix(NA, S, length(ia))
108
-	bf <- rep(NA, S*length(ia))
109
-	I1 <- as.logical(I1); I2 <- as.logical(I2)
110
-	bf[I1] <- 0.5 * as.numeric(((obs.theta-theta.aa)/(theta.ab-theta.aa)))[I1]
111
-	bf[I2] <- as.numeric((.5 * (obs.theta - theta.ab) / (theta.bb - theta.ab)))[I2] + 0.5
112
-	bf[as.logical(lessAA)] <- 0
113
-	bf[as.logical(grBB)] <- 1
114
-	bf <- matrix(bf, S, 3, byrow=TRUE)
115
-	pm.bf <- apply(bf, 2, mean)
116
-	return(pm.bf)
117
-}
... ...
@@ -1896,85 +1896,85 @@ isCall <- function(G, call){
1896 1896
 	G.call
1897 1897
 }
1898 1898
 
1899
-computeSummary <- function(object, G.call, call, I, allele, Ns, index){
1900
-	k <- match("grandMean", batchNames(object))
1901
-	stats <- summaryStats(G.call, I, FUNS=c("rowMedians", "rowMAD"))
1902
-	Ns[, k] <- rowSums(G.call, na.rm=TRUE)
1903
-	updateStats(stats, Ns, object, call, allele, index)
1904
-	gc()
1905
-	return()
1906
-}
1899
+##computeSummary <- function(object, G.call, call, I, allele, Ns, index){
1900
+##	k <- match("grandMean", batchNames(object))
1901
+##	stats <- summaryStats(G.call, I, FUNS=c("rowMedians", "rowMAD"))
1902
+##	Ns[, k] <- rowSums(G.call, na.rm=TRUE)
1903
+##	updateStats(stats, Ns, object, call, allele, index)
1904
+##	gc()
1905
+##	return()
1906
+##}
1907 1907
 
1908
-updateTau <- function(object, tau2, G.call, call, I, allele, index){
1909
-	k <- match("grandMean", batchNames(object))
1910
-	logI <- log2(I)
1911
-	rm(I); gc()
1912
-	tau2[, k] <- summaryStats(G.call, logI, FUNS="rowMAD")^2
1913
-	if(call==1 & allele=="A"){
1914
-		tau2A.AA(object)[index, ] <- tau2
1915
-	}
1916
-	if(call==1 & allele=="B"){
1917
-		tau2B.AA(object)[index, ] <- tau2
1918
-	}
1919
-	if(call==3 & allele=="A"){
1920
-		tau2A.BB(object)[index, ] <- tau2
1921
-	}
1922
-	if(call==3 & allele=="B"){
1923
-		tau2B.BB(object)[index, ] <- tau2
1924
-	}
1925
-	NULL
1926
-}
1908
+##updateTau <- function(object, tau2, G.call, call, I, allele, index){
1909
+##	k <- match("grandMean", batchNames(object))
1910
+##	logI <- log2(I)
1911
+##	rm(I); gc()
1912
+##	tau2[, k] <- summaryStats(G.call, logI, FUNS="rowMAD")^2
1913
+##	if(call==1 & allele=="A"){
1914
+##		tau2A.AA(object)[index, ] <- tau2
1915
+##	}
1916
+##	if(call==1 & allele=="B"){
1917
+##		tau2B.AA(object)[index, ] <- tau2
1918
+##	}
1919
+##	if(call==3 & allele=="A"){
1920
+##		tau2A.BB(object)[index, ] <- tau2
1921
+##	}
1922
+##	if(call==3 & allele=="B"){
1923
+##		tau2B.BB(object)[index, ] <- tau2
1924
+##	}
1925
+##	NULL
1926
+##}
1927 1927
 
1928
-updateCors <- function(cors, G.call, I){
1929
-	k <- match("grandMean", batchNames(object))
1930
-	cors[, k] <- rowCors(I[[1]]*G.call, I[[2]]*G.call, na.rm=TRUE)
1931
-	if(call==1){
1932
-		corrAA(object)[index, ] <- cors
1933
-	}
1934
-	if(call==2){
1935
-		corrAB(object)[index, ] <- cors
1936
-	}
1937
-	if(call==3){
1938
-		corrBB(object)[index, ] <- cors
1939
-	}
1940
-}
1928
+##updateCors <- function(cors, G.call, I){
1929
+##	k <- match("grandMean", batchNames(object))
1930
+##	cors[, k] <- rowCors(I[[1]]*G.call, I[[2]]*G.call, na.rm=TRUE)
1931
+##	if(call==1){
1932
+##		corrAA(object)[index, ] <- cors
1933
+##	}
1934
+##	if(call==2){
1935
+##		corrAB(object)[index, ] <- cors
1936
+##	}
1937
+##	if(call==3){
1938
+##		corrBB(object)[index, ] <- cors
1939
+##	}
1940
+##}
1941 1941
 
1942
-updateStats <- function(stats, Ns, object, call, allele, tau2, index){
1943
-	if(call==1){
1944
-		Ns.AA(object)[index, ] <- Ns
1945
-		if(allele=="A"){
1946
-			medianA.AA(object)[index, k] <- stats[, 1]
1947
-			madA.AA(object)[index, k] <- stats[, 2]
1948
-			updateTau(object, tau2, G.call, call, I, allele, index)
1949
-		} else {
1950
-			medianB.AA(object)[index, k] <- stats[, 1]
1951
-			madB.AA(object)[index, k] <- stats[, 2]
1952
-			updateTau(object, tau2, G.call, call, I, allele, index)
1953
-		}
1954
-	}
1955
-	if(call==2){
1956
-		Ns.AB(object)[index, ] <- Ns
1957
-		if(allele=="A"){
1958
-			medianA.AB(object)[index, k] <- stats[, 1]
1959
-			madA.AB(object)[index, k] <- stats[, 2]
1960
-		} else {
1961
-			medianB.AB(object)[index, k] <- stats[, 1]
1962
-			madB.AB(object)[index, k] <- stats[, 2]
1963
-		}
1964
-	}
1965
-	if(call==3){
1966
-		Ns.BB(object)[index, ] <- Ns
1967
-		if(allele=="A"){
1968
-			medianA.BB(object)[index, k] <- stats[, 1]
1969
-			madA.BB(object)[index, k] <- stats[, 2]
1970
-			updateTau(object, tau2, G.call, call, I, allele, index)
1971
-		} else {
1972
-			medianB.BB(object)[index, k] <- stats[, 1]
1973
-			madB.BB(object)[index, k] <- stats[, 2]
1974
-			updateTau(object, tau2, G.call, call, I, allele, index)
1975
-		}
1976
-	}
1977
-}
1942
+##updateStats <- function(stats, Ns, object, call, allele, tau2, index){
1943
+##	if(call==1){
1944
+##		Ns.AA(object)[index, ] <- Ns
1945
+##		if(allele=="A"){
1946
+##			medianA.AA(object)[index, k] <- stats[, 1]
1947
+##			madA.AA(object)[index, k] <- stats[, 2]
1948
+##			updateTau(object, tau2, G.call, call, I, allele, index)
1949
+##		} else {
1950
+##			medianB.AA(object)[index, k] <- stats[, 1]
1951
+##			madB.AA(object)[index, k] <- stats[, 2]
1952
+##			updateTau(object, tau2, G.call, call, I, allele, index)
1953
+##		}
1954
+##	}
1955
+##	if(call==2){
1956
+##		Ns.AB(object)[index, ] <- Ns
1957
+##		if(allele=="A"){
1958
+##			medianA.AB(object)[index, k] <- stats[, 1]
1959
+##			madA.AB(object)[index, k] <- stats[, 2]
1960
+##		} else {
1961
+##			medianB.AB(object)[index, k] <- stats[, 1]
1962
+##			madB.AB(object)[index, k] <- stats[, 2]
1963
+##		}
1964
+##	}
1965
+##	if(call==3){
1966
+##		Ns.BB(object)[index, ] <- Ns
1967
+##		if(allele=="A"){
1968
+##			medianA.BB(object)[index, k] <- stats[, 1]
1969
+##			madA.BB(object)[index, k] <- stats[, 2]
1970
+##			updateTau(object, tau2, G.call, call, I, allele, index)
1971
+##		} else {
1972
+##			medianB.BB(object)[index, k] <- stats[, 1]
1973
+##			madB.BB(object)[index, k] <- stats[, 2]
1974
+##			updateTau(object, tau2, G.call, call, I, allele, index)
1975
+##		}
1976
+##	}
1977
+##}
1978 1978
 
1979 1979
 crlmmCopynumber <- function(object,
1980 1980
 			    MIN.SAMPLES=10,
... ...
@@ -14,7 +14,7 @@ getCor <- function(object, gt){
14 14
 getTau2 <- function(object, gt){
15 15
 	bs <- batchStatistics(object)
16 16
 	nma <- paste("tau2A", gt, sep=".")
17
-	nma <- paste("tau2B", gt, sep=".")
17
+	nmb <- paste("tau2B", gt, sep=".")
18 18
 	tau2.a <- assayDataElement(bs, nma)
19 19
 	tau2.b <- assayDataElement(bs, nmb)
20 20
 	cbind(tau2.a, tau2.b)
... ...
@@ -11,6 +11,8 @@ cnSet2oligoSnpSet <- function(object){
11 11
 	is.lds <- ifelse(is(calls(object), "ff_matrix") | is(calls(object), "ffdf"), TRUE, FALSE)
12 12
 	if(is.lds) stopifnot(isPackageLoaded("ff"))
13 13
 	b.r <- calculateRBaf(object)
14
+	b <- integerMatrix(b.r[[1]], 1000)
15
+	r <- integerMatrix(b.r[[2]], 100)
14 16
 ##	if(is.lds){
15 17
 ##		## initialize a big matrix for raw copy number
16 18
 ##		message("creating an ff object for storing total copy number")
... ...
@@ -438,36 +440,36 @@ setMethod("calculatePosteriorMean", signature(object="CNSet"),
438 440
 		  return(pm)
439 441
 	  })
440 442
 
441
-		  .bivariateCenter <- function(nu, phi){
442
-			  ##  lexical scope for mus, CA, CB
443
-			  if(CA <= 2 & CB <= 2 & (CA+CB) < 4){
444
-				  mus[,1, ] <- log2(nu[, 1, ] + CA *
445
-						    phi[, 1, ])
446
-				  mus[,2, ] <- log2(nu[, 2, ] + CB *
447
-						    phi[, 2, ])
448
-			  } else { ## CA > 2
449
-				  if(CA > 2){
450
-					  theta <- pi/4*Sigma[,2,]
451
-					  shiftA <- CA/4*phi[, 1, ] * cos(theta)
452
-					  shiftB <- CA/4*phi[, 1, ] * sin(theta)
453
-					  mus[, 1, ] <- log2(nu[, 1, ] + 2 * phi[,1,]+shiftA)
454
-					  mus[, 2, ] <- log2(nu[, 2, ] + CB *phi[,2,]+shiftB)
455
-				  }
456
-				  if(CB > 2){
457
-					  ## CB > 2
458
-					  theta <- pi/2-pi/4*Sigma[,2,]
459
-					  shiftA <- CB/4*phi[, 2, ] * cos(theta)
460
-					  shiftB <- CB/4*phi[, 2, ] * sin(theta)
461
-					  mus[, 1, ] <- log2(nu[, 1, ] + CA*phi[,1,]+shiftA)
462
-					  mus[, 2, ] <- log2(nu[, 2, ]+ 2*phi[,2,]+shiftB)
463
-				  }
464
-				  if(CA == 2 & CB == 2){
465
-					  mus[, 1, ] <- log2(nu[, 1, ] + 1/2*CA*phi[,1,])
466
-					  mus[, 2, ] <- log2(nu[, 2, ]+ 1/2*CB*phi[,2,])
467
-				  }
468
-			  }
469
-			  mus
470
-		  }
443
+##.bivariateCenter <- function(nu, phi){
444
+##			  ##  lexical scope for mus, CA, CB
445
+##			  if(CA <= 2 & CB <= 2 & (CA+CB) < 4){
446
+##				  mus[,1, ] <- log2(nu[, 1, ] + CA *
447
+##						    phi[, 1, ])
448
+##				  mus[,2, ] <- log2(nu[, 2, ] + CB *
449
+##						    phi[, 2, ])
450
+##			  } else { ## CA > 2
451
+##				  if(CA > 2){
452
+##					  theta <- pi/4*Sigma[,2,]
453
+##					  shiftA <- CA/4*phi[, 1, ] * cos(theta)
454
+##					  shiftB <- CA/4*phi[, 1, ] * sin(theta)
455
+##					  mus[, 1, ] <- log2(nu[, 1, ] + 2 * phi[,1,]+shiftA)
456
+##					  mus[, 2, ] <- log2(nu[, 2, ] + CB *phi[,2,]+shiftB)
457
+##				  }
458
+##				  if(CB > 2){
459
+##					  ## CB > 2
460
+##					  theta <- pi/2-pi/4*Sigma[,2,]
461
+##					  shiftA <- CB/4*phi[, 2, ] * cos(theta)
462
+##					  shiftB <- CB/4*phi[, 2, ] * sin(theta)
463
+##					  mus[, 1, ] <- log2(nu[, 1, ] + CA*phi[,1,]+shiftA)
464
+##					  mus[, 2, ] <- log2(nu[, 2, ]+ 2*phi[,2,]+shiftB)
465
+##				  }
466
+##				  if(CA == 2 & CB == 2){
467
+##					  mus[, 1, ] <- log2(nu[, 1, ] + 1/2*CA*phi[,1,])
468
+##					  mus[, 2, ] <- log2(nu[, 2, ]+ 1/2*CB*phi[,2,])
469
+##				  }
470
+##			  }
471
+##			  mus
472
+##		  }
471 473
 
472 474
 ## for a given copy number, return a named list of bivariate normal prediction regions
473 475
 ##   - elements of list are named by genotype
... ...
@@ -214,7 +214,7 @@ setMethod("annotatedDataFrameFrom", "ffdf", Biobase:::annotatedDataFrameFromMatr
214 214
 
215 215
 ## Document this...
216 216
 getBAF <- function(theta, canonicalTheta)
217
-    .Call('normalizeBAF', theta, ct)
217
+    .Call('normalizeBAF', theta, canonicalTheta)
218 218
 
219 219
 
220 220
 validCEL <- function(celfiles){
221 221
Binary files a/data/cnSetExample.rda and b/data/cnSetExample.rda differ
... ...
@@ -31,6 +31,7 @@ R. Scharpf
31 31
 }
32 32
 
33 33
 \examples{
34
+library(oligoClasses)
34 35
 if(require(hapmapsnp6)){
35 36
   path <- system.file("celFiles", package="hapmapsnp6")
36 37
   cels <- list.celfiles(path, full.names=TRUE)
... ...
@@ -11,7 +11,9 @@
11 11
 
12 12
 }
13 13
 \details{
14
-  This object was created from the copynumber vignette in inst/scripts.
14
+  This object was created from the copynumber vignette in
15
+  inst/scripts. A subset of markers was selected to keep the package
16
+  size small.
15 17
 }
16 18
 \usage{
17 19
 
... ...
@@ -82,6 +82,7 @@ crlmm2(filenames, row.names=TRUE, col.names=TRUE,
82 82
 }
83 83
 \examples{
84 84
 ## this can be slow
85
+library(oligoClasses)
85 86
 if (require(genomewidesnp6Crlmm) & require(hapmapsnp6)){
86 87
   path <- system.file("celFiles", package="hapmapsnp6")
87 88
 
... ...
@@ -34,6 +34,7 @@ R. Scharpf
34 34
 }
35 35
 
36 36
 \examples{
37
+library(oligoClasses)
37 38
 data(cnSetExample2)
38 39
 table(batch(cnSetExample2))
39 40
 sample.index <- which(batch(cnSetExample2) == "CUPID")