Unfortunately, we estimate ~80% of those doing DGE use only 3 replicates. At least 6 replicates should be used to account for variability, bad replicates and dropouts: http://blog.genohub.com/how-many-replicates-are-sufficient-for-differential-gene-expression/
Based on the description in this paper as below, may I say that edgeR is one of best tools to do differentially expression analysis for RNA-Seq data?
1. A key finding of this work is the demonstration that the read-count distribution of the majority of genes is consistent with the negative binomial model. Reassuringly, many of the most widely used RNA-seq DGE tools (e.g., egeR, DESeq, cuffdiff, …
2. Our findings favor the approach implemented in edgeR, where variance for one gene is squeezed towards a common dispersion calculated across all genes.
I agree that replicates are extremely important, especially for capturing biological variability.
However, I think a lot of people do experiments without replicates or only duplicates. In other words, I think the number of researchers using triplicates is less than 80%.
Also, I found the paper to be interesting, but I think 48 replicates was a bit excessive. I think the point probably could have been made just as well with less than half that number.