So I have 250 samples, and each of the samples has a "score" which is a continuous distribution of floating point numbers. I have followed the typical limma-voom workflow (https://ucdavis-bioinformatics-training.github.io/2018-June-RNA-Seq-Workshop/thursday/DE.html) up to the point where it is time to fit the linear model and make contrasts.
My model matrix is
mm <- model.matrix(~score + subtype)
Where subtype is a covariate because we assume cell subtype is a confounding variable. Anyway, I fit the linear model with
fit <- lmFit(y, mm)
Now I want to test to see which genes are correlated with the score variable. However, I am not sure how to make a contrast for this, since I am not contrasting two different variables and am instead measuring gene expression against a continuous variable. Does anyone have any insights here please?
1 answer
There's no need for form a contrast because the score variable is already in the model. A continuous variable is its own contrast. Just run the usual limma pipeline and test for score:
fit <- eBayes(fit)
topTable(fit, coef="score")
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