Dear List,
We are looking at tag-based RNASeq data, and after running popular
packages for finding differential expression (edgeR, and DEGseq) we were
looking that the actual counts for the significant ones.
We are observing a somewhat extreme variance within each group for those
(say one sample with high count for gene X while others have zero count).
For example, gene X flagged as differentially expressed has the
following counts (adjusted p-value with DGESeq is 9.401479e-10):
0 grp_A
0 grp_A
0 grp_A
92207 grp_B
0 grp_B
0 grp_B
The underlying binomial is obviously not like the almost-Gaussian
assumed in microarrays/t-test-like approaches, but this kind of outcome
is somehow intriguing me. Do people here have experience to share
regarding how well such gene hold through the qPCR verification step ?
Laurent
PS: In case the sessionInfo() matters
R version 2.12.1 (2010-12-16)
Platform: x86_64-pc-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_DK.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_DK.UTF-8 LC_COLLATE=en_DK.UTF-8
[5] LC_MONETARY=C LC_MESSAGES=en_DK.UTF-8
[7] LC_PAPER=en_DK.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_DK.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] DESeq_1.2.1 locfit_1.5-6 lattice_0.19-17 akima_0.5-4
[5] Biobase_2.10.0
loaded via a namespace (and not attached):
[1] annotate_1.28.0 AnnotationDbi_1.12.0 DBI_0.2-5
[4] genefilter_1.32.0 geneplotter_1.28.0 grid_2.12.1
[7] RColorBrewer_1.0-2 RSQLite_0.9-4 splines_2.12.1
[10] survival_2.36-2 tools_2.12.1 xtable_1.5-6
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