I am new to using DESeq2 and would appreciate some advice for a somewhat complex experimental design (for a non-statistician anyway). I have a dataset with 3 factor variables, each with 2 levels, as follows:
trt: control, treatment
geno: A, B
time: 6h, 12h (no time 0h)
Each combination of factor levels was replicated 3 times, giving 2 x 2 x 2 x 3 = 24 samples total.
Biologically, I want to find the genes that respond differently to treatment in the two genotypes; this can be a constant effect over time or not. Statistically, I figure that I should be looking for genes with significant geno x trt effect over time points, and also genes with significant geno x trt x time effect. Other ideas would be appreciated here.
Assuming the above reasoning makes sense, does the following code/design accomplish the goals above?
dds2 <- DESeqDataSetFromMatrix(countData = cts2,
colData = coldata,
design = ~ geno + trt + time + geno:trt + geno:time + trt:time + geno:trt:time)
dds2 <- DESeq(dds2, test="LRT", reduced = ~ geno + trt + time + geno:time + trt:time)
Also, what would the log2 fold change mean in this case?
Thanks in advance!