I would like advise about how to make a design for my experiment using DEseq2. In principle my question is straightforward, I'd simply like to find differentially expressed genes between two conditions. However, I've noticed that I have a large batch effect (I have two batches) and also large differences due to gender. Thus, I think I should include the two factors in my design:
> dds <- DESeqDataSetFromTximport(txi, sampleTable, ~BATCH + GENDER + Condition)
...
> resultsNames(dds)
[1] "Intercept" "Batch_2_vs_1"
[3] "GENDER_MALE_vs_FEMALE" "Condition_T_vs_N"
Q1: if my question is which are the DE genes between conditions taking into account the batch and gender effects, which is the better option:
dds <-DESeq(dds)
res <- results(dds)
summary(res, name="Condition_T_vs_N")
or
dds_LRT <- DESeq(dds, test="LRT", reduced=~BATCH + GENDER)
res_LRT <- results(dds_LRT)
summary(res_LRT)
Q2: if I add an interaction term to the design, to which question am I answering?
dds <- DESeqDataSetFromTximport(txi, sampleTable, ~BATCH + GENDER + Condition:GENDER)
Many thanks for your comments, Cinta