Hi experts,
Does anyone have advice on how to use arrayQualityMetrics to identify sample outliers with DESeq data sets produced using DESeq2? I'm having trouble generating a file that the "arrayQualityMetrics" function will accept using DESeq2 functions ("DESeq" or "varianceStabilizingTransformation" or "rlog"...).
I've tried several things, including:
dds<-DESeqDataSetFromMatrix(countData=countData, colData=colData, design=design)
vsd=varianceStabilizingTransformation(dds, blind=T)
arrayQualityMetrics(vsd, intgroup=c("gen", "conditon"), force=T)
The error message tells me that arrayQualityMetrics can't "find an inherited method for function ‘platformspecific’ for signature ‘"SummarizedExperiment""... the standard output of the DESeq2 functions I'm calling.
My current work-around is to use DESeq(1) to generate the variance stabilized data, calling arrayQualityMetrics on that, and proceeding with DESeq2 omitting the sample outliers. I worry about this strategy since DESeq2 estimates dispersions differently.
library(DESeq)
cds=newCountDataSet(countData,colData)
cds = estimateDispersions(estimateSizeFactors(cds), method="blind")
Vst = varianceStabilizingTransformation(cds)
arrayQualityMetrics(Vst, intgroup = c("gen", "condition"))
Any tips?
Thanks in advance!
Rachel
sessionInfo()
R version 3.1.1 (2014-07-10)
Platform: x86_64-apple-darwin10.8.0 (64-bit)
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] DESeq_1.18.0 lattice_0.20-29 locfit_1.5-9.1
[4] Biobase_2.26.0 arrayQualityMetrics_3.22.0 DESeq2_1.6.3
[7] RcppArmadillo_0.4.600.0 Rcpp_0.11.3 GenomicRanges_1.18.3
[10] GenomeInfoDb_1.2.4 IRanges_2.0.1 S4Vectors_0.4.0
[13] BiocGenerics_0.12.1
loaded via a namespace (and not attached):
[1] acepack_1.3-3.3 affy_1.44.0 affyio_1.34.0 affyPLM_1.42.0
[5] annotate_1.44.0 AnnotationDbi_1.28.1 base64_1.1 base64enc_0.1-2
[9] BatchJobs_1.5 BBmisc_1.8 beadarray_2.16.0 BeadDataPackR_1.18.0
[13] BiocInstaller_1.16.1 BiocParallel_1.0.0 Biostrings_2.34.1 brew_1.0-6
[17] Cairo_1.5-5 checkmate_1.5.1 cluster_1.15.3 codetools_0.2-10
[21] colorspace_1.2-4 DBI_0.3.1 digest_0.6.8 fail_1.2
[25] foreach_1.4.2 foreign_0.8-62 Formula_1.1-2 gcrma_2.38.0
[29] genefilter_1.48.1 geneplotter_1.44.0 ggplot2_1.0.0 grid_3.1.1
[33] gridSVG_1.4-2 gtable_0.1.2 Hmisc_3.14-6 hwriter_1.3.2
[37] illuminaio_0.8.0 iterators_1.0.7 latticeExtra_0.6-26 limma_3.22.1
[41] MASS_7.3-37 munsell_0.4.2 nnet_7.3-8 plyr_1.8.1
[45] preprocessCore_1.28.0 proto_0.3-10 RColorBrewer_1.1-2 reshape2_1.4.1
[49] RJSONIO_1.3-0 rpart_4.1-8 RSQLite_1.0.0 scales_0.2.4
[53] sendmailR_1.2-1 setRNG_2013.9-1 splines_3.1.1 stringr_0.6.2
[57] survival_2.37-7 SVGAnnotation_0.93-1 tools_3.1.1 vsn_3.34.0
[61] XML_3.98-1.1 xtable_1.7-4 XVector_0.6.0 zlibbioc_1.12.0
Perfect. I forgot you would need to add as.data.frame. good save.