I have questions with limma-voom RNA-Seq data analysis. My project has three factor: A (16 levels), B(three levels) and C(two levels), where A is random effect while B and C are fixed effects.
I would like to test the main effects of B and C, as well as A. To test the effect B, I used the following coding.
Group <- factor(paste(Design$A,Design$B,Design$C,Design$plateID,sep=".")) design <- model.matrix(~B+C) y <- DGEList(counts=T,group=Group) ## T is the count data y <- calcNormFactors(y,method="RLE") v <- voom(y,design,plot=TRUE) corfit <- duplicateCorrelation(v,design,block=Design$A) corfit$consensus fitRan <- lmFit(v,design,block=Design$A,correlation=corfit$consensus) fitRan <- eBayes(fitRan) topTable(fitRan,coef=c(2,3)) ## for testing the effect of factor B, which has three levels.
1. Is the coding above correct for testing the main effect of B?
2. How to test the random effect A?