I have a question about the proper design for DEseq2, when there is an siControl as well as siRNA for a target gene, then there is a treated and a vehicle for both conditions.
I setup the factors and design like so:
> dds$condition <- factor(dds$condition, levels =c("siControl","siTarget")) > dds$condition <- relevel(dds$condition, ref= "siControl") > dds$treatment <- factor(dds$treatment, levels = c("vehicle","treated")) > dds$treatment <- relevel(dds$treatment, ref ="vehicle”) > design(dds) <- ~ condition + treatment + condition:treatment
At first it appeared to be giving me what I thought I wanted, differences in the siTarget population upon treatment, controlling for the siControl and Vehicle condition. But then I realized that the target gene is not on the list of DE genes, even though there is a clear knockdown. I think this is because the target gene is also down in the Vehicle condition.
I can compare just siTarget vs siControl, but then this list is dominated by genes that are different in the Vehicle condition. I can also compare treated vs. vehicle, but then there is a combination of siControl and siTarget genes in the list.
Ideally what I would like to do is normalize the treated condition to the untreated condition, and then look for differences between siControl and siTarget.
Is this possible with DESeq? Is there something wrong with this approach? Is there a better way to do this analysis?
Any help or advice would be very much appreciated.