Question: mulitfactorial analysis, adjusting for quantitative covariates
4.6 years ago by
Guest User • 12k
Guest User • 12k wrote:
Hi! I would like to use Limma to compare gene expression between two treatment groups (PO vs C). In this analysis I need to adjust for differences in a quantitative covariate (age) between samples. Is the following setup appropriate for this analysis? Do I accurately adjust for age in the final analysis? Ingrid -- output of sessionInfo(): eset<-readExpressionSet("eset.txt","target.txt",header=TRUE) GROUP <- factor(target$GROUP, levels=c("C","PO")) AGE <- factor(target$AGE) design <- model.matrix(~0+GROUP+AGE) colnames(design) <- c("C","PO") fit <- lmFit(eset,design) cont.matrix <- makeContrasts(CvsPO=C-PO,levels=design) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) topTable(fit2, n=100, coef="CvsPO", adjust="BH") -- Sent via the guest posting facility at bioconductor.org.
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