I am using limma to find differentially expressed genes between 2 tumor types. My gene expression matrix has 31 samples in one group and 59 samples in the other, and 58581 gene expression measurements.
These are the R commands I've run (having previously defined df as my expression matrix, and design and cont.matrix with my sample grouping info):
y <- DGEList(df)
v <- voom(y, design, plot=FALSE)
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=cont.matrix)
efit <- eBayes(vfit)
Now I would like to get significantly differentially expressed genes.
If I run: summary(decideTests(efit))
I get 645 genes down in my test group and 1116 genes up in my test group.
However, if I run: summary(classifyTestsP(efit))
I get 3362 genes down in my test group and 3730 genes up in my test group.
In both cases, the gene numbers of course change if I adjust the p.value away from default, but the decideTests() result is still very different from the classifyTestsP() result.
I have read all the documentation I can find on decideTests() and classifyTests() but I still have 2 questions:
1) What type of test does decideTests() call by default? Here: https://www.rdocumentation.org/packages/limma/versions/3.28.14/topics/classifyTests it says "The functions described here are called by decideTests. Most users should use decideTests rather than using these functions directly." but it does not say which function is being called by decideTests by default, or why I would want to use decideTests() rather than using one of the other functions directly.
2) What is the exact function of classifyTestsP()? According to this: https://support.bioconductor.org/p/5604/ since I only have 1 contrast in my linear model, classifyTestsP() should not be conducting any p value adjustment, so why is it so different from decideTests()? I also cannot find the "documentation entry for classifyTestsP()" referred to in the answer to the linked question.
If you are able to point me to some documentation that will answer my questions, I would be very grateful. Thank you.