Hi all!

I am analyzing RNA-seq data with DESeq2, and I have two confounding variables. One is the batch number and the other is whether cells were washed or not.

The data is comprised of samples of two different cell-lines that are un/treated with dox and un/modified with Luc/pax5/pax5-ita gene. These samples come from two batches, where the first contain only one type of cell line(NAM6) and the second batch contains two(NAM6/MHHCAL_2). This an abridged version of the metadata table

To study the effect of different combinations of modifications and treatments in the cell lines, I modified the table as follows

As you can see, I have combined the mod and treatment columns into one column called sample_group_simple. As well as combining the cohort and cell_line columns into cell_line_cohort column. Finally, the following design for the analysis.

~ cell_line_cohort + cell_line_cohort:sample_group_simple

Unfortunately, the complex enough situation got more complicated when we found out that only a subset of samples have been washed by PBS. This variable is confounding the batch variable since all samples of the first batch have been washed, unlike the second one. As a result, any attempt to account for it in the analysis design leads to a not-full-rank model matrix. The final table looks like this

My question is how can I test for the effect of different combinations of treatment and modifications on different cell lines, while accounting for the two confounders, namely PBS and cell_line_cohort?

Thanks in advance, Mohamed

If a nuisance variable is confounded with another nuisance variable, you can just combine the two:

nuisance <- factor(paste(nuisance1, nuisance2))

And then just use this one variable.