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RNA. 2016 Jun;22(6):839-51. doi: 10.1261/rna.053959.115. Epub 2016 Mar 28.

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

Author information

1
Division of Computational Biology, College of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom.
2
Division of Computational Biology, College of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom Division of Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom.
3
Division of Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom.
4
Edinburgh Genomics, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom.
5
Division of Plant Sciences, College of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom.
6
Division of Computational Biology, College of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom Division of Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom.

Abstract

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.

KEYWORDS:

RNA-seq; benchmarking; differential expression; experimental design; replication; statistical power; yeast

PMID:
27022035
PMCID:
PMC4878611
DOI:
10.1261/rna.053959.115
[Indexed for MEDLINE]
Free PMC Article

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