[BioC] edgeR: tagwise dispersion in 2-factorial vs. 1-factorial design

[Henning Wildhagen]

Hi,

i am analysing a two-factorial RNA-seq experiment with edgeR. The design of my study has two factors, genotype and treatment. 
Genotype has three levels (A,B,C), "treatment" has two levels ("control", "stress"). The first and most important question that i want to answer is which transcripts are affected by treatment in each of the three genotypes. I did this analysis by specifying a two-factorial model and subsequently selecting coefficients/contrasts to test for the treatment effect genotype-wise.
Of course, this type of analysis can also be done in a 1-factorial way, i.e. by defining three separate DGEList-objects for each genotype and then performing an exactTest for the treatment effect for each of the three DGEList-objects/genotypes. For one of the genotypes, say "A", the latter analysis gives approximately 60% more DE genes compared to the DE-analysis based on the 2-factorial model. For the other two genotypes, the number of DE genes is almost the same in the two analyses. 
My first guess was, that this finding this related to the differences in the estimation of the tagwise dispersion. In the two-factorial analysis, one and the same dispersion estimate per transcript is used to test for DE. In the 1-factorial analysis, three dispersion estimates are calculated per transcript, one for each genotype. When comparing the distributions of genotype-wise dispersion estimates of the 1-factorial analysis with the "common" tagwise dispersion of the 2-factorial model, i see that the median is higher and the range of the 95%tiles is wider for genotypes B, C and the "common" dispersion of the 2-factorial model, compared to genotype "A". 
Now my question is which analysis is more reliable, the 2-factorial or the 1-factorial?

Thanks for any help or comments on this problem,

Henning 

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Dr. Henning Wildhagen
Forest Research Institute Baden-Württemberg
Freiburg, Germany
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