Hi,

I am comparing 2 conditions ; I would like to find all genes that show less than a 2fold increase in condition A vs B, wether the fold change is positive or negative. (and reciprocally genes that show not a 2fold decrease). Is that possible?

I tried results(dds, lfcThreshold=1, altHypothesis="less") and select padj<0.01, but in fact that select the genes for which lfc(A/B) < -1 is significant, whereas I would like  lfc(A/B) < 1 is significant (that is altHypothesis="less" and altHypothesis="lessAbs").

Does the 1-pValue of results(dds, lfcThreshold=1, altHypothesis="greater") would give me the pValue I am looking for? (sorry if this is a statistical nonsense, I cannot judge myself).

hi Samuel,

We don't have built-in functions for this, but it's easy to compute the p-values manually, e.g. here for beta < lfcThreshold:

res$pvalue <- pnorm(res$log2FoldChange, mean = lfcThreshold, sd = res$lfcSE, lower.tail = TRUE)

And you can directly compute adjusted p-values with p.adjust() or perform independent filtering using the genefilter package:

library(genefilter)
alpha <- 0.1
filter <- res$baseMean
theta <- seq(mean(filter == 0), 1, length=20)
filtPadj <- filtered_p(filter=filter, test=res$pvalue, theta=theta, method="BH")
numRej  <- colSums(filtPadj < alpha, na.rm = TRUE)
res$padj <- filtPadj[, which.max(numRej), drop=TRUE]

Also, for these tests where the alternative hypothesis includes 0, you should set betaPrior=FALSE, because you don't want the prior to support the alternative hypothesis.