DESeq Standard Comparison or Multifactorial / Model Fitting?

[carmeyeii]

Hi,

I'm analyzing a dataset with DESeq, which consists of two time-points and two genotypes. I am not very knowledgeable in statistics, sadly, and I am a bit lost as to whether I should use DESeq under the "standard" comparison mode, or make use of the more "advanced" GLM regressions facet.

The comparisons are between :

GenotypeA/Timepoint1  VS  GenotypeA/Timepoint2
GenotypeB/Timepoint1  VS  GenotypeB/Timepoint1

GenotypeA/Timepoint1  VS  GenotypeB/Timepoint1
GenotypeA/Timepoint2  VS  GenotypeB/Timepoint2

My understanding is that although my experimental design involves two factors (genotype and timepoint), since my comparisons are only changing one factor at a time, I have nothing to do performing Linear Regressions / Model Fitting, but I am not sure of this. Perhaps it would make sense to use the multi-factorial design analysis if I were to compare

GenotypeA/Timpepoint1 VS GenotypeB/Timepoint 2

?

Also, what if I want to create a PCA plot of the libraries? Is that a sensible reason to use a GLM fit?

Thanks, and sorry for the extreme confusion.

Carmen

[Wolfgang Huber]

Dear Carmen

you are right that for the first set of comparisons you mention pairwise comparisons of the relevant sets of samples is good enough.

Linear models / ANOVA type of analyses will become relevant if you want to deconvolve the combined effects of genotype and time on gene expression, and/or if you want to see if the effects of these factors just add up, or interact in more subtle ways. If you want to follow up this type of question, an elementary book about ANOVA or talking to a statistician might be enlightening.

Best wishes
Wolfgang