I have a 2-factor design with 2 levels each:
population: fish from MDPL or MDPP condition: freshwater (FW) or transfer experimental treatments
> ExpDesign population condition MDPL_FW_1 MDPL FW MDPL_FW_2 MDPL FW MDPL_FW_3 MDPL FW MDPL_transfer_1 MDPL transfer MDPL_transfer_2 MDPL transfer MDPL_transfer_3 MDPL transfer MDPP_FW_1 MDPP FW MDPP_FW_2 MDPP FW MDPP_FW_3 MDPP FW MDPP_transfer_1 MDPP transfer MDPP_transfer_2 MDPP transfer MDPP_transfer_3 MDPP transfer
Our question is:
Which genes change in expression due to condition (is the condition effect different across populations?) in each population (MDPP and MDPL)?
I’m not very familiar with using interaction terms. After consulting the vignette and the
?results section on 'Using interaction terms', I still can’t decide for our particular question, do we want to look at the main effects of population (and/or condition) PLUS the interaction term or ONLY the interaction term? This DESeq2 multiple interaction terms 3-factor design in particular helped to explain how to get at the interaction terms and main effects, but I’m still not sure what is the best approach for our question.
Here is our model:
dds <- DESeqDataSetFromTximport(txi.salmon, ExpDesign, ~population + condition + population:condition) dds$population<-relevel(dds$population,ref="MDPP") dds<-DESeq(dds,betaPrior=FALSE)
4 contrasts in results:
> matrix(resultsNames(dds)) [,1] [1,] "Intercept" [2,] "population_MDPL_vs_MDPP" [3,] "condition_transfer_vs_FW" [4,] "populationMDPL.conditiontransfer"
I have tried this:
Which comparison would be most appropriate to extract from the object?
I think it might be best to use the first one with main effects of population and the interaction term, but I’m not sure why. If we only look at the interaction term, the log fold change numerator population vs. denominator condition does not make sense to me.
Any insight or advice you might have would be greatly appreciated.
Thank you, Lisa