Dear all,

thanks a lot for supporting such nice packages. I would like to know if limma fit function could be used with smaller set of genes or other metabolites quantified by liquid chromatography. I use limma in genes lists with thousand of genes, and never used with smaller features. I wonder if the moderated t-test could be used with only hundreds of features measured in small sample sets.

For instance, 2  or more groups; 4 biological replicates each, 150 genes/metabolites. Can we use empirical Bayes moderation in this situation? If yes that would be great, since limma provides an excelent tool to overcome unequal variances and normality deviation in cases like that.

Tks.

I think I recall an instance where Gordon said that the empirical Bayes squeezing employed by limma could theoretically work with as few as 4 genes. I believe a few hundred should be fine.

To add to Ryan's answer; some testing suggests that, with 150 features, limma does okay:

design <- model.matrix(~c(1,1,1,1,0,0,0,0))
ngenes <- 150
p.out <- scale.out <- df.out <- list()
for (i in 1:1000) {
    s2 <- 10/rchisq(ngenes, 10)
    y <- matrix(rnorm(ngenes*8, sd=sqrt(s2)), ncol=8)
    fit <- lmFit(y, design)
    fit <- eBayes(fit)
    p.out[[i]] <- fit$p.value[,2]
}
hist(unlist(p.out))

... which gives a uniform distribution of p-values, as expected under the null hypothesis. This result also holds if the shrinkage parameters (fit$s2.prior and fit$df.prior) are not precisely estimated, which was a pleasant surprise to me. I guess that the true values of the shrinkage parameters are not important, as long as the empirical variance distribution within each iteration is modelled well. Which makes sense, as the variances are just nuisance parameters when the aim is to detect differential expression.