[BioC] edgeR: effect of 'outlier' tags on differential expression calls

[Gordon K Smyth]

Dear Alessandro,

You seem to giving examples of miRs that are expressed at a high degree is 
just one sample.  The easiest way to deal with such miRs, if you really 
don't want to detect them, is to filter out miRs that fail to be expressed 
to a reasonable degree in at least four samples (since your groups are of 
size four).  See for example pages 24-25 of the edgeR user's guide, where 
this is done for the Dclk1 mouse case study.  We often suggest cpm>1 for 
at least m samples, where m is the minimum group size.

Another obvious thing to do is to examine an MDS plot to identify outlier 
samples.

I also have to point out that the output in your email cannot be correct, 
at least the output cannot all be from the same R session.  The 
sessionInfo() output says edgeR 2.6.0, but the column headings show that 
the results being present are actually from an earlier version of edgeR.

I'd much rather see continuous code and output from one session, rather 
than output snippets, without quite knowing how they were obtained.

Best wishes
Gordon

-------------- original message --------------
[BioC] edgeR: effect of 'outlier' tags on differential expression calls
alessandro.guffanti at genomnia.com alessandro.guffanti at genomnia.com
Tue Apr 24 12:48:22 CEST 2012

Dear colleagues: I am using edgeR to examine differential expression on 
small RNA data

I noticed this problem also when working with SAGE datasets: when just one 
of the samples is clearly an outlier, like you can see below for sample 7 
(the comparison is 1-4 versus 5-8), there is a call of significant 
differential expression which seems to be inappropriate, or at least it 
should be reexamined.

How can we diagnose these situations before checking manually the tag 
counts for all the significant differential expression calls ? Please note 
that these are tumoral samples, so an high sample by sample variability is 
expected in principle..

Thanks a lot in advance,

Alessandro


miRNA_ID 	1.mirna 	2.mirna 	3.mirna 	4.mirna 
5.mirna 	6.mirna
7.mirna 	8.mirna
hsa-miR-515-3p 	3 	1 	1 	1 	1 	7 	1601 	3
hsa-miR-518e 	4 	0 	1 	0 	1 	2 	1715 	2
hsa-miR-520d-3p 	0 	0 	0 	0 	0 	1 
243 	0
hsa-miR-519c-3p 	0 	0 	0 	0 	0 	1 
248 	0
hsa-miR-520f 	0 	0 	0 	0 	0 	0 	163 	0
hsa-miR-519d 	12 	1 	0 	1 	1 	4 	1754 	1
hsa-miR-520h 	0 	0 	0 	0 	0 	0 	189 	2
hsa-miR-519c-5p 	0 	0 	0 	0 	0 	0 
123 	0
hsa-miR-520g 	16 	1 	1 	4 	2 	4 	1917 	2
hsa-miR-518b 	5 	0 	0 	1 	1 	3 	686 	1
hsa-miR-517a 	100 	5 	4 	2 	6 	45 	10024 	3



miRNA_ID 	logConc 	logFC 	P.Value 	adj.P.Val
hsa-miR-515-3p 	-15.09154 	-8.61753 	0.00000 	0.00082
hsa-miR-518e 	-15.30278 	-9.22926 	0.00000 	0.00110
hsa-miR-520d-3p 	-18.23592 	-9.46747 	0.00001 
0.00201
hsa-miR-519c-3p 	-17.98705 	-9.01722 	0.00002 
0.00338
hsa-miR-520f 	-32.04992 	-35.93228 	0.00002 	0.00338
hsa-miR-519d 	-14.46073 	-7.61177 	0.00003 	0.00338
hsa-miR-520h 	-18.02925 	-8.34496 	0.00003 	0.00338
hsa-miR-519c-5p 	-32.25620 	-35.51970 	0.00004 
0.00382
hsa-miR-520g 	-14.16219 	-7.27220 	0.00005 	0.00382
hsa-miR-518b 	-15.70611 	-7.39997 	0.00006 	0.00382
hsa-miR-517a 	-11.74423 	-7.21374 	0.00006 	0.00382



R version 2.15.0 (2012-03-30)
Platform: x86_64-pc-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] edgeR_2.6.0  limma_3.12.0


-- 

Alessandro Guffanti - Head, Bioinformatics, Genomnia srl
   Via Nerviano, 31 - 20020 Lainate, Milano, Italy
      Ph: +39-0293305.702 Fax: +39-0293305.777
              http://www.genomnia.com
"When you're curious, you find lots of interesting things to do."
(Walt Disney)

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