I have 6 subjects 1-6, and would like to identify the differentially expressed genes in condition A vs.B and considering the gender effect.
Here is my coldata:
subject gender condition 1 m A 1 m B 2 m A 2 m B 3 f A 3 f B 4 f A 4 f B 5 m A 5 m B 6 f A 6 f B
I think the condition is nested in the subject, right? But when I tried the following design formula, DESeq2 returned
Error in checkFullRank(modelMatrix)
dds <- DESeqDataSetFromMatrix(countData = acts, colData = coldata, design = ~ subject + gender + condition + condition:subject)
Although the following combinations can be processed:
design = ~ subject + condition
design = ~ condition + gender
But how do I modify my design formula to consider paired samples, gender effects, and conditions (or any interaction) at the same time? Thanks!