I understand the explanation when we set the design as ~ cell + dex, in which, we want to study the difference between dex treatment among cells. After which, a reduction (in the model) would be done for cell. But I find it hard to understand how will the program (with a fixed algorithm) will be able to show two different sets of p-values (or padj values) if we flip the design. I.e., ~cell + dex and ~ dex + cell, then reduce ~ cell in either scenarios. My interest is to study the treatment.
I am tempted to use Walt test since I am only testing for one condition (treatment), but my colleague (who is a more experienced bioinformatician) strongly advised me to focus on LRT instead. I made the effort to test both tests, and noticed slight changes in the padj values; LRT seems to show borderline significance in genes I am interested in, whereas Walt shows borderline non-significance. This is of course, only observed in this current datasets.
Should I then trust my instincts to use Walt, or follow a more experienced member and focus on LRT?