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08232016, 07:04 AM  #1 
Senior Member
Location: California Join Date: Jul 2014
Posts: 198

Interpretation of interaction term in DESeq2
Hi all,
I'm having some difficulty understanding how to interpret the results of the interaction term in a DESeq2 setup. Simply put, I have an experiment with two variables  Condition (A or B), and Treatment (Untreated or Treated). I'd like to know a few things: 1) in Condition A, what is the Treatment effect? 2) in Condition B, what is the Treatment effect? 3) what is the difference in Treatment effect between Condition A and Condition B? For the first two, I used the Code:
factor(paste0(...)) For the 3rd question, an interaction term seems appropriate, e.g.: Code:
design = ~ Condition*Treatment Code:
results(dds, name=c("ConditionB.TreatmentUntreated")) On the flip side, let's say ~450 genes were significant in comparison #1 and ~1000 genes in comparison #2 above. Why aren't the genes in the setdiff of these two lists, significant in the interaction term contrast? Am I thinking about this all wrong? 
08232016, 02:28 PM  #2 
Devon Ryan
Location: Freiburg, Germany Join Date: Jul 2011
Posts: 3,480

A significant interaction in no way implies that there must be a significant effect within either condition. A setdiff in no way implies a significant interaction.
For your "setdiff" example, suppose you're using a pvalue cutoff of 0.05. If you have a pvalue of 0.049 in condition A and 0.05 in condition B then you're saying that there should be a significant interaction as well (there's normally no significant difference between such pvalues). On the flip side, suppose condition A and condition B both have pvalues of 0.2, but their direction of changes are in opposite directions. Then the interaction effect is quite likely to be significant. 
08232016, 02:47 PM  #3 
Senior Member
Location: California Join Date: Jul 2014
Posts: 198

Devon, thanks for the quick answer and clear explanation. I see what you're saying, I am just having trouble accepting that there would so little overlap between the two comparisons!
To follow up, I took a closer look at the genes that turned up as significant in the interaction and interestingly they all seem to have extremely large differences in log2FC with low baseMeans (see attached). That led me to wonder what statistical test is actually used to derive a pvalue for the interaction terms..? 
08232016, 02:53 PM  #4 
Devon Ryan
Location: Freiburg, Germany Join Date: Jul 2011
Posts: 3,480

I'm pretty sure it's doing a Wald test (that's the most straightforward way do handle interactions). In recent versions of DESeq2, there's a function called plotCounts() or something like that. Use it to plot the actual counts and get a better idea about whether these are real.

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