Hi, I'm a beginner at microarray data. I want to use the patient's baseline gene expression to predict their 3-month response to treatment. In my imagination, the first step is to find differentially expressed genes between responders and non-responders from their baseline expression. I found a lot of studies using LIMMA package to find differentially expressed genes.
Here is my code.
Treat <- factor(paste(data$response, data$month,sep=".")) factor<- data$age design <- model.matrix(~0+Treat+factor) corfit <- duplicateCorrelation(data ,design,block=data$id) fit <- lmFit(data,design,block=data$id,correlation=corfit$consensus) cm <- makeContrasts( res1vsres0ForM0 = Treat1.0-Treat0.0, levels=design) fit2 <- contrasts.fit(fit, cm) fit3 <- eBayes(fit2)
After reading some tutorials, I think the formula of the linear model in my study will be Gene Expression at baseline = b0 + b1 Response + b2 Age. (I want to adjust their age.) However, it seems a little bit weird to predict their baseline expression by their response 3-month later. Does this mean my study is not suitable for limma? If so, does it make sense to use logistic regression to find differentially expressed genes? Response = b0 + b1 Gene Expression at baseline + b2 Age
I would appreciate it if you could give me some suggestions. Thank you.