After a day of googling, I've decided that it'd be better to ask the question here.
So the experiment is I have bulk RNA seq data from 3 patients: A, B, C. And their RNA seq data is obtained for pre-treatment, treatment cycle 1, treatment cycle 2, treatment cycle 3.
So in total I have 12 samples of bulk RNA seq:
A.PreTreat -> A.Cycle1 -> A.Cycle2 -> A.Cycle3
B.PreTreat -> B.Cycle1 -> B.Cycle2 -> B.Cycle3
C.PreTreat -> C.Cycle1 -> C.Cycle2 -> C.Cycle3
I want to get a differential gene list between different cycles (i.e. cycle 3 to pretreatment, cycle 3 to cycle 2) using
model.matrix(), lmFit(), makeContrasts(), contrasts.fit(), eBayes(), all of which are in the limma package.
Here is my minimal working example.
library(limma) # Already normalized expression set: rows are genes, columns are the 12 samples` normalized_expression <- matrix(data=sample(1:100), nrow=10, ncol=12) colnames(normalized_expression) <- c("A.PreTreat", "A.Cycle1", "A.Cycle2", "A.Cycle3", "B.PreTreat", "B.Cycle1", "B.Cycle2", "B.Cycle3", "C.PreTreat", "C.Cycle1", "C.Cycle2", "C.Cycle3") patient_and_treatment <- factor(colnames(normalized_expression), levels = colnames(normalized_expression)) design.matrix <- model.matrix(~0 + patient_and_treatment) colnames(design.matrix) <- patient_and_treatment fit <- lmFit(normalized_expression, design.matrix) # I want to get a contrast matrix to get differential genes between cycle 3 treatment and pre-treatment in all patients contrast.matrix <- makeContrasts("A.Cycle3+B.Cycle3+C.Cycle3-A.PreTreat-B.PreTreat-C.PreTreat", levels = levels(patient_and_treatment)) # Outputs Error of no residual degree of freedom fit2 <- eBayes( contrasts.fit( fit, contrast.matrix ) ) # Want to run but cannot summary(decideTests(fit2))
So far I am stuck on no residual degree of freedom error.
I am not even sure if this is the statistically right way in limma to address my question of getting differential gene list between cycle 3 treatment to pre-treatment in all patients.
Any help will be greatly appreciated.