[Bioc-sig-seq] RNASeq, differential expression between group, and large variance within groups

[Mads Sønderkær]

I think it looks like a "rouge" tag, since it is not found in all the
replicates (biological or technical?).
Is the gene (or tag) annotated?
Depending on the cutting enzyme, is it the most 3' canonical, or could it be
caused by incomplete digestion?

-----Original Message-----
From: bioc-sig-sequencing-bounces at r-project.org
[mailto:bioc-sig-sequencing-bounces at r-project.org] On Behalf Of Laurent
Gautier
Sent: 21. februar 2011 15:36
To: bioc-sig-sequencing at r-project.org
Subject: [Bioc-sig-seq] RNASeq, differential expression between group, and
large variance within groups

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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