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I want to use edgeR to detect differential expression. For this I
first read the bam file with this function:
getCounts <- function(alignmentName, tx){
fileName <-
paste("/data/WntData/tophat/",alignmentName,".sorted.bam", sep="")
alignment <- readBamGappedAlignments(fileName)
newReadNames <- gsub("([0-9(MT|X|Y)])","chr\\1",rname(alignment))
alignment <- GRanges(seqnames=newReadNames,ranges=IRanges(start=star
t(alignment),end=end(alignment)), strand=strand(alignment))
alignmentCounts <- suppressWarnings(countOverlaps(tx,alignment))
}
Then I create a table of raw counts by using this command:
rawCountTable <- data.frame(polyPlus=polyPlusCounts,
polyMin=polyMinCounts)
Then I follow the tutorial from:
http://cgrlucb.wikispaces.com/edgeR+Tutorial
So to build the edgeR object, I have this code:
y <- DGEList(counts=rawCountTable, group=groups)
y <- calcNormFactors(y)
y <- estimateCommonDisp(y)
y <- estimateTagwiseDisp(y)
When executing this last function, I get this error:
Error in t.default(object$counts) : argument is not a matrix
When I use check the object$counts with class(y$counts), this is a
matrix!
What am I doing wrong now?
On google I only found people with old versions, who didn't use the
estimateCommonDisp function...
I hope someone can help me with this question.
-- output of sessionInfo():
> sessionInfo()
R version 2.15.1 (2012-06-22)
Platform: x86_64-suse-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=C LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats graphics grDevices utils datasets
methods
[8] base
other attached packages:
[1] edgeR_3.0.6 limma_3.14.3 VennDiagram_1.5.1
[4] RMySQL_0.9-3 Rsamtools_1.10.2 Biostrings_2.26.2
[7] GenomicFeatures_1.10.1 AnnotationDbi_1.20.3 pasilla_0.2.14
[10] DESeq_1.10.1 lattice_0.20-6 locfit_1.5-8
[13] DEXSeq_1.4.0 Biobase_2.18.0 BiocInstaller_1.8.3
[16] cummeRbund_2.0.0 Gviz_1.2.1 rtracklayer_1.18.1
[19] GenomicRanges_1.10.5 IRanges_1.16.4 fastcluster_1.1.7
[22] reshape2_1.2.2 ggplot2_0.9.3 RSQLite_0.11.2
[25] DBI_0.2-5 BiocGenerics_0.4.0
loaded via a namespace (and not attached):
[1] annotate_1.36.0 biomaRt_2.14.0 biovizBase_1.6.0
bitops_1.0-5
[5] BSgenome_1.26.1 cluster_1.14.2 colorspace_1.2-0
dichromat_1.2-4
[9] digest_0.6.0 genefilter_1.40.0 geneplotter_1.36.0
gtable_0.1.2
[13] Hmisc_3.10-1 hwriter_1.3 labeling_0.1
MASS_7.3-18
[17] memoise_0.1 munsell_0.4 parallel_2.15.1 plyr_1.8
[21] proto_0.3-9.2 RColorBrewer_1.0-5 RCurl_1.95-3
scales_0.2.3
[25] splines_2.15.1 statmod_1.4.16 stats4_2.15.1
stringr_0.6.2
[29] survival_2.36-14 tcltk_2.15.1 tools_2.15.1
XML_3.95-0.1
[33] xtable_1.7-0 zlibbioc_1.4.0
--
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