My experimental design is 18 samples: three different treatments (cytokines A, B,C) each of which has its own mock/control each in triplicate.
I would like to know the genes that are differentially expressed across all cytokine treatments. I would also like to know the genes differentially expressed in response to each cytokine treatment. I am not trying to find out the genes that are only induced in A, but not B or C (etc).
Is the best option to perform each comparison individually and then manually look for the things that overlap, or is this something I can do within the design of my experiment?
At the moment my columns are set up like:
samp condition cyto CytoA_treat treated A CytoA_untreat untreated A CytoB_treat treated B CytoB_untreat untreated B CytoC_treat treated C CytoC_untreat untreated C
If I set up my design like:
ddsTxi <- DESeqDataSetFromTximport(txi, colData = samps_matx, design = ~ cyto + condition) results(ddsTxi, name = "condition_treated_vs_untreated")
I think this gets me the answer to my first question ("Which genes are differentially expressed in all treatments?") but from there I don't know how to perform the individual comparisons. I read about interaction terms, but as I understand it adding one in is not what I want.
DESeqDataSetFromTximport(txi, colData = samps_matx, design = ~ cyto + condition + cyto:condition) results(ddsTxi, contrast=list( c("condition_untreated_vs_treated","cytoB.conditiontreated")
From the DESeq2 vignette it seems like this would tell me the additional effects of cytokine B treatment as compared to A, which isn't exactly what I want.
Maybe the best bet is to just make two DESeq objects and have one where I make a compound factor of "condition.treatment" and then individually compare those groups with the contrast argument?
A final option, since these data are all from the same cell type, is to merge the "untreated" samples and compare each cytokine treatment to this aggregate 'super mock'. I can see from the PCA, though, that the mocks from each of these experiments cluster by cytokine group (so there are batch effects).