Hi,
Introduction:
I'm analyzing a high replicate tag based mRNAseq (SAGE like) data set. In the 
experiment I have 2x 47 replicates of true biological samples of potato leaves. 
I also have triplicates of libraries originating from the same crushed leaf 
material, but from different library preps. I hope to be able to evaluate 
biological and technical variation from these. I have chosen to use EgdeR for 
the analysis, while it has some very attractive features. One is that it is 
possible to estimate the dispersion.
For now I can conclude that the tagwise dispersion model fits the data better 
than the poisson and the common dispersion model (not suerpringly).

Howerver when calculating the tagwise dispersion, prior.n and used.prop needed 
to be defined. I have looked in the forum and found 1 E-mail regarding prior.n.
My question is:
Is there a way to find the optimal value for prior.n - and how is this 
evaluated? I'm thinking of subtracting the raw variance from the calculated 
tagwise variance, and then look at the absolute SUM (the sum of error). Is this 
a good way to go?

Regarding the d$tagwise.dispersion (here d is a DGEList after 
estimateTagwiseDisp). I believe the object is a vector, but there are no row 
names. My question is now if the order of  d$tagwise.dispersion is the same as 
rownames(d$counts)?

Session info:
R version 2.13.0 (2011-04-13)
Platform: x86_64-pc-mingw32/x64 (64-bit)

other attached packages:
[1] edgeR_2.2.5

Kind regards
Mads Sønderkær

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Mads Sønderkær
PhD Student
Mobile: +45 3053 2492

Aalborg University
Department of Biotechnology, Chemistry and Environmental Engineering
Sohngaardsholmsvej 49
Room 17
9000 Aalborg
Denmark
Phone: +45 9940 8461
www.bio.aau.dk<http://www.bio.aau.dk/>

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