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

TL;DR

This study evaluates RNA-seq differential gene expression tools using a 48-replicate experiment, revealing how replicate number affects detection accuracy and identifying the most effective tools for various experimental designs.

Contribution

It provides empirical data on the performance of DGE analysis tools across different replicate numbers, guiding optimal experimental design and tool selection.

Findings

Higher replicates improve true positive rates for all genes.

edgeR performs best with 6-12 replicates, while DESeq excels with more than 12.

Achieving high TPR across all fold-changes requires more than 20 replicates.

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 ($|log_{2}(FC)|\gt2$) 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 $n_r \ge12$, minimizing false positives is more important and DESeq outperforms the other tools.