I am analyzing rna-seq data in the format of counts. There is batch effect revealed by PCA.
One method I tried called RUVseq, it estimated the variation basing on control genes, and then added it to design matrix. I don't know why simply adding a continuous variable will work.
Here is an example, the experiment setting is:
samples groups A 1 B 1 C 1 D 2 E 2 F 2 G 3 H 3 I 3
Then RUVseq methods could estimate the unwanted variation, for example,
B <- (-0.37670272 , 2.44136463, -0.79533912, -0.05487747, 0.25014132, 0.61824329 -0.17262350 ,-2.22390027, -1.26361438)
Then combine them together:
samples groups B A 1 -0.37670272 B 1 2.44136463 C 1 -0.79533912 D 2 -0.05487747 E 2 0.25014132 F 2 0.61824329 G 3 -0.17262350 H 3 -2.22390027 I 3 -1.26361438
Make a design matrix to fit glm, an example of design is
model.matrix( ~ 0 + groups + B)
My question is why this works? The coefficient of continuous variable means how many the read counts of genes will change of one unit of B changed, right? Then why B could be used for correcting the batch effect?