How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

By N. Schurch, Pietá Schofield, Marek Gierlinski, Christian Cole, A. Sherstnev, Vijender Singh, N. Wrobel, Karim Gharbi, Gordon G. Simpson, T. Owen-Hughes and 2 more. RNA: A publication of the RNA Society, 2015

806 citations recorded by the source corpus.

RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%–40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR and DESeq2 the leading tools. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/66d80625401d3fa1e366edcc057191aa7ef741a5

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Citations in the corpus

Polyploidy, the Nucleotype, and Novelty: The Impact of Genome Doubling on the Biology of the Cell (2019, background): “Additionally, given how difficult it is to identify weakly DEGs consistently (Schurch et al. 2016), it is possible that subtle effects of autopolyploidy have gone unnoticed.”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, background): “show that a higher number of replicates is required to identify DE of genes with low fold change and ideally at least 6 replicates per condition should be used [24] .”
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences (2018, methodology): “To investigate the precision of various estimates of LFCs, we used a highly replicated RNA-seq dataset designed for benchmarking [19].”