This paper presents a nice comparison of many RNAseq expression analysis tools.
An RNA?seq experiment with 48 biological replicates in each of 2 conditions was performed to determine the number of biological replicates required, and to identify the most effective statistical analysis tools for identifying differential gene expression (DGE). When 3 , seven of the nine tools evaluated give true positive rates (TPR) of only 20?40%. For high fold?change genes (|log | 2) the TPR is 85%. 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 85% across all fold?changes requires 20. For future RNA?seq experiments these results suggest 6, rising to 12 when identifying DGE irrespective of fold?change is important. For 12 , superior TPR makes edgeR the leading tool tested. For 12, minimizing false positives is more important and DESeq outperforms the other tools.
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