Limma: how to interpret logFC for interaction contrast
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Natalia • 0
Last seen 3.5 years ago

Hi all, I'm wondering how to interpret the logFC of an interaction term correctly. As an example, in the limma user guide section 9.5. (Interaction Models: 2 × 2 Factorial Designs), sub-section 9.5.2 (Analysing as for a Single Factor), a contrast matrix is generated as follows:

> cont.matrix <- makeContrasts(
+     SvsUinWT=WT.S-WT.U,
+     SvsUinMu=Mu.S-Mu.U,
+     Diff=(Mu.S-Mu.U)-(WT.S-WT.U),
+     levels=design)
> fit2 <-, cont.matrix)
> fit2 <- eBayes(fit2)

(abbreviations: Mu=mutated, WT=wild-type, S=stimulated, U=unstimulated)

So the interaction contrast Diff=(Mu.S-Mu.U)-(WT.S-WT.U) can be used to identify genes that respond to stimulation differently between mutant and wild-type samples, i.e. two factors (genotype and treatment) are combined.

Now suppose we use topTable for this "Diff" contrast, returning a log2-fold-change of 1.58 (= 3-fold upregulation) for a certain gene. Would that logFC value refer to genotype (3-fold upregulation in mutant vs. wt), or refer to treatment (3-fold upregulation in stimulated vs. unstimulated), or to both (upregulation in mutant-vs-wt AND stimulated-vs-unstimulated)?

limma logFC factor interaction • 1.2k views
Entering edit mode
Last seen 2 days ago
United States

It means that when you calculate (Mu.S-Mu.U)-(WT.S-WT.U), the value you get is 1.58. Which can mean any number of things. For instance the difference in the first set of parentheses could be 1.58 and 0 in the second. Or 0 in the first and -1.58 in the second. Or any set of numbers that you can plug in to that formula to get 1.58 back out.

If you only have a handful of significant genes, the best idea IMO is to just plot them. An alternative is to do a heatmap of your significant genes, which can be useful to show the genes with similar expression patterns. Or you can use kmeans to get groups of genes that have a similar pattern.

Entering edit mode

Thank you James for the explanation and the visualization suggestions! I already plotted some heatmaps, and will try kmeans as well.


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