This is a test version of Biostars. For the public version, visit https://www.biostars.org.
different results with normal fit and contrast matrix fit

I have a data set with loge base expression values of 90 normal and 10 tumor sample.

mydesign<- cbind(Tumor=c(rep(1,90),rep(0,10)),Control=c(rep(0,90),rep(1,10)))

Now, I proceeded with two way:

fit <- lmFit(data, design=mydesign)
fit <- eBayes(fit)
topTable(fit)

numGenes <- nrow(data)
case1.list <- topTable(fit,number=numGenes)

or

cont.matrix <- makeContrasts(TumorvsControl=Tumor-Control, levels=mydesign)
fit2 <- lmFit(data, design=mydesign)
fit2 <- contrasts.fit(fit2, cont.matrix)
fit2 <- eBayes(fit2)

case2.list <- topTable(fit2,number=numGenes)

However, when I searched for a particular gene, the results are quite contrasting. e.g.

case1.list["ENSG1",]
                  Tumor  Control AveExpr        F      P.Value    adj.P.Val
ENSG1 1.53268 5.134342 1.87246 1802.497 1.115791e-83 4.074213e-83

case2.list ["ENSG1",]
                    logFC AveExpr         t      P.Value    adj.P.Val        B
ENSG1 -3.601662 1.87246 -29.42565 2.183333e-53 3.353327e-49 110.5354

Could anyone please advice that with given tumor and control estimation, how log FC were obtained?

r limma

"contrast" in limma means something similar to "What conditions I want to compare".

2 answers

This ends up not being a limma question, but more of a question of what a model matrix is and how it's used.

Case 1 is asking, "Is the signal in the 'Tumour' group greater than 0?" That is unlikely to be a biologically interesting question to ask.

Case 2 is asking, "Is there a difference between 'Tumour' and 'Control'?" That is a much more biologically interesting question to ask.

I would strongly discourage you from manually making model matrices until you're more familiar with them (and even then it's rarely more convenient to do so).

This should be the accepted answer.

Please read the documentation for limma. Start with Chapter 9 on page 40.

Log in to answer this question.