Entering edit mode
I have an experiment with bulk RNAseq data. I have two different conditions and a control condition. I want to look at the effect of condition A vs condition B after controlling for the impact of the control condition. I think I need my DESeq2 design formula to assess (Condition A- Control) vs (Condition B - Control), but I'm not sure how to reflect that in my DESeq2 design formula. Right now this is my formula:
dds <- DESeqDataSetFromMatrix(countData = Counts2filtered, colData = coldata2, design = ~condition2)
dds <- DESeq(dds)
Condition2 has the levels of Control , Condition A, Condition B, and the comparison I get when I design my results table is condition A vs condition B, but I am unsure what it is using as the control effect/reference levels.
I'm new to bioinformatics so I appreciate the help!
Note that A-C vs B-C is the same as A-B directly, since comparing levels to the same control is identical to comparing the levels directly. Keep that in mind, it might ease the analysis, unless there is a specific control for A and B. Can you post
colData(dds)?colData(dds) is:
So basically if I do design = ~ condition2 with my groups as A, B, and C (control), my comparison of A and B should already be adjusted to the control values?
If you want to know where A and B differ from each other, just compare A to B.