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

2015/05/31 by Nicholas J. Schurch, Pieta Schofield, Marek Gierliński +9 · 1 citation
Biochemistry, Genetics and Molecular Biology · #q-bio.GN

paper · pdf · doi:10.1261/rna.053959.115

21 Pages and 4 Figures in main text. 9 Figures in Supplement attached to PDF. Revision to correct a minor error in the abstract

arxiv created 2015/06/08 · arxiv updated 2016/03/30

Abstract

An RNA-seq experiment with 48 biological replicates in each of 2 conditions was performed to determine the number of biological replicates (n_r) required, and to identify the most effective statistical analysis tools for identifying differential gene expression (DGE). When n_r=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)|\gt2) the TPR is \gt85 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 \gt85% across all fold-changes requires n_r\gt20. For future RNA-seq experiments these results suggest n_r\gt6, rising to n_r\gt12 when identifying DGE irrespective of fold-change is important. For 6 \lt n_r \lt 12, superior TPR makes edgeR the leading tool tested. For n_r ≥12, minimizing false positives is more important and DESeq outperforms the other tools.

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