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

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 ($|lo{g}_2(FC)|>2$) 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 $n_r>20$. For future RNA-seq experiments these results suggest $n_r>6$, rising to $n_r>12$ when identifying DGE irrespective of fold-change is important. For $6<n_r<12$, superior TPR makes edgeR the leading tool tested. For $n_r\ge 12$, minimizing false positives is more important and DESeq outperforms the other tools.

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

References in corpus (1)

• Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment

Cited by in corpus (2)

• Modeling and analysis of RNA-seq data: a review from a statistical perspective
• A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets