To assess how well DESeq2 performs for standard analyses in comparison to other current methods, we used a combination of simulations and real data. The negative-binomial-based approaches compared were DESeq (old) [4], edgeR [33], edgeR with the robust option [34], DSS [6] and EBSeq [35]. Other methods compared were the voom normalization method followed by linear modeling using the limma package [36] and the SAMseq permutation method of the samr package [24]. For the benchmarks using real data, the Cuffdiff 2 [28] method of the Cufflinks suite was included. For version numbers of the software used, see Additional file 1: Table S3. For all algorithms returning P values, the P values from genes with non-zero sum of read counts across samples were adjusted using the Benjamini–Hochberg procedure [21].
