[BioC] EdgeR estimateTagwiseDisp()

[Jetse [guest]]

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=start(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  

--
Sent via the guest posting facility at bioconductor.org.