DESeq2: 5 Conditions (B vs C) vs (D vs B)
Consider a setup with 5 Conditions A-E, where E is the untreated condition and hence the reference label.
I want to know which genes are upregulated in B vs C but are downregulated in D vs B. All samples should be compared to E (the untreated condition).
coldata condition A_1 "A" A_2 "A" A_3 "A" B_1 "B" B_2 "B" B_3 "B" C_1 "C" C_2 "C" C_3 "C" D_1 "D" D_2 "D" D_3 "D" E_1 "E" E_2 "E" E_3 "E" dds_f <- DESeqDataSetFromMatrix(countData = data_f, colData = coldata, design = ~ condition) dds_f$condition <- relevel(dds_f$condition, ref = "E") dds_f <- DESeq(dds_f) resultsNames(dds_f)  "Intercept" "condition_A_vs_E" "condition_B_vs_E" "condition_C_vs_E" "condition_D_vs_E" upd <- results(dds_f, listValues = c(1, -1/2), contrast = list(c("condition_B_vs_E"), c("condition_C_vs_E", "condition_D_vs_E"))) rn <- rownames(upd[!is.na(upd$padj) & upd$padj <= 0.05 & upd$log2FoldChange >= 1, ])
My questions are:
1.) I want to find genes which are downregulated in (
condition_B_vs_E) but upregulated in (
2.) Is this the right comparison (
condition_C_vs_E) vs (
3.) Does the contrast I choose answer this question?
*2.) or does one has to interpret this contrast like this: On average
condition_B_vs_E is smaller or higher than
Meaning that it could happen that this could result in a positive log2 fold change even though the gene expression levels of
condition_D_vs_E are actually higher than in
Which is because of the average of
condition_C_vs_E, which in this case contains very low expressed