Dear Bioconductor Community,
in a previous thread( https://support.bioconductor.org/p/64372/#64413) i have asked about the implementation of a paired analysis, and Mr. James W. MacDonald kindly provided me an suggestion of a paired limma analysis, which i used on the code below :
library(limma) conditions <- data.trusted.eset$condition condition <- factor(conditions, levels(condition)[c(2,1)]) pairs <- factor(rep(1:13, each = 2)) design <- model.matrix(~condition+pairs) fit <- lmFit(data.trusted.eset, design) fit2 <- eBayes(fit)
In the next step i wanted to implement a multifactorial analysis in limma to evaluate the interaction between two factors, condition(control & cancer tissue) & meta_factor(metastatic & non-metastatic cancer), in order to evaluate possible DE genes between metastatic cancer and non-metastatic cancer samples:
f <- paste(data.trusted.eset$condition, data.trusted.eset$Meta_factor, sep=".") f <- factor(f) pairs <- factor(rep(1:13, each = 2)) design <- model.matrix(~0 +f +pairs) fit <- lmFit(data.trusted.eset, design) head(names$coefficients) contrast.matrix <- makeContrasts(M.CvsC="fCancer.1-fCancer.0", M.CvsControl="fCancer.1-fNormal.0", levels=design) fitb <- contrasts.fit(fit, contrast.matrix) fit2 <- eBayes(fitb)
My first question is if it is wrong for the statistical model that i didnt use an intercept term(because with the intercept term i couldnt use the coefficient "fCancer.0" for the contrast.matrix) ? And secondly, is it also important as with the first analysis with just one factor, to include the term pairs to get meaningful results ? My last question is that with topTable, i get only 4 genes with adj.p,Val < 0.05. Is that necessarily wrong or it might illustrate possible small differences between metastatic and non-metastatic tumors ?
Thank you for your patience(& please excuse me for any "begginer" questions, as i have only using R & Bioconductor for a few months).