That seems to be exactly my same problem, so I am including here Gordon's 
answer.

Actually filtering at the cpm level worked quite nicely to ameliorate the 
situation - look at the
latest update (May 2011) of the User Manual to see a neat example of the 
procedure.


Alessandro

--

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.

--

[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

-----------------------------------------------------
Alessandro Guffanti - Bioinformatics, Genomnia srl
Via Nerviano, 31 - 20020 Lainate, Milano, Italy
Ph: +39-0293305.702 Fax: +39-0293305.777
http://www.genomnia.com
"If you can dream it, you can do it" (Walt Disney)

-----Original Message-----
 From: Simon Melov <smelov@buckinstitute.org>
 To: "bioconductor@r-project.org" <bioconductor@r-project.org>
 Date: Mon, 7 May 2012 12:19:19 -0700
 Subject: [BioC] edgeR outlier question


I have a reasonable RNASeq data set of 10 biological replicates of a control 
group versus 10 biological replicates experimental I've gone through the 
edgeR workflow, and get a nice list of about 1000 genes differentially 
expressed due to the experimental manipulation. I input the data based on 
total reads per gene (I'd like to get to exons too, but first things first). 
The data is obtained via a paired end strategy, so its pretty good quality. 
The number of reads per sample (library) is about 10 million reads each. My 
question is, as I go through list of significant genes which are 
differentially expressed between the two groups  (normalized via the 
workflow), ranked by BH FDR down to 0.05, I see genes being judged as 
differentially expressed which have very low expression in most samples, yet 
are thrown off by 1 or 2 values, thereby achieving statistical significance. 
For example, a gene might have between 1 and 2 counts per million reads in 
one group, and be basically the !
  same in the other group, but one of the values is perhaps at a 1000 or so 
counts, which seems to throw off the entire group, thereby becoming 
"significant".

 Shouldn't edgeR take into account this sort of biological variation within 
a group and account for it in assessing significance? Its clear that in the 
above example, that sample is an outlier, and therefore the variance is so 
high, so it shouldn't be ranked as being differentially expressed. I 
filtered the data by applying the criteria of at least 1 count per sample, 
and I have to have at least 8 samples per group which have this. Should 
there be an additional filtering criteria to exclude these outliers? or 
doesn't edgeR take into account this sort of situation (I thought it did).

 Am I doing something wrong here?

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