Evaluation of tools for differential gene expression analysis by RNA-seq on a 48 biological replicate experiment

An RNA-seq experiment with 48 biological replicates in each of 2 conditions was performed to determine the number of biological replicates (nᵣ) required, and to identify the most effective statistical analysis tools for identifying differential gene expression (DGE). When nᵣ=3, seven of the nine tools evaluated give true positive rates (TPR) of only 20 to 40 percent. For high fold-change genes (|log₂ (FC) |2) the TPR is 85 percent. Two tools performed poorly; over- or under-predicting the number of differentially expressed genes. Increasing replication gives a large increase in TPR when considering all DE genes but only a small increase for high fold-change genes. Achieving a TPR 85% across all fold-changes requires nᵣ20. For future RNA-seq experiments these results suggest nᵣ6, rising to nᵣ12 when identifying DGE irrespective of fold-change is important. For 6 nᵣ 12, superior TPR makes edgeR the leading tool tested. For nᵣ 12, minimizing false positives is more important and DESeq outperforms the other tools.