Hello!

I applied DESeq2 to model my two-factor gene expression data: each factor has 2 levels - A0 and A1, B0 and B1.

I found no significant genes for the interactions term A:B (cutoff: FDR < 0.05).

However, the number of genes with significant changes by B1 relative to B0 was ~5,000 for the additive model (~ A + B) and ~230 for the interaction model (~ A + B + A:B)

(no significant genes observed for the A0 vs A1 in the additive model)

My question is that: if the number of DEG could decrease from 5,000 to 230 by adding an interaction term, I may expect many genes affected by interaction between A and B. Right? But I still found no significant genes due to interaction. So, does this suggest that interaction between A and B may affect many genes but the model, and data, have no enough power to reject the NULL model statistically?

I am trying to look at the data to see what could explain this difference. Could anyone please share your suggestion and comments? Let me know if anything else I could provide. Thanks!

P

I'm a little confused by what you did here.

You said that you found no signficiant genes for the interaction model. But then in the next paragraph you say you found ~230 for the interaction model? When you say you found no/230 genes for the interaction model, so you mean testing for significance of the interaction term in the interaction model? Or for the significance main effects in the interaction model?

I.e. you need to tell us the contrasts as well as just the designs.

Thanks. It's mine. It should be:

When using ~ A + B,

I found 5,000 significant genes for B.

When using ~ A + B + A:B,

I found 230 significant genes for B but 0 significant genes for A:B.