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 [cid:image001.jpg@01CC1EA5.94AFAD40] 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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