How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?
RNA, March 2016
Nicholas J. Schurch, Pietá Schofield, Marek Gierliński, Christian Cole, Alexander Sherstnev, Vijender Singh, Nicola Wrobel, Karim Gharbi, Gordon G. Simpson, Tom Owen-Hughes, Mark Blaxter, Geoffrey J. Barton, Schurch, Nicholas J, Schofield, Pietá, Gierliński, Marek, Cole, Christian, Sherstnev, Alexander, Singh, Vijender, Wrobel, Nicola, Gharbi, Karim, Simpson, Gordon G, Owen-Hughes, Tom, Blaxter, Mark, Barton, Geoffrey J, Pieta Schofield, Schurch NJ, Schofield P, Gierliński M, Cole C, Sherstnev A, Singh V, Wrobel N, Gharbi K, Simpson GG, Owen-Hughes T, Blaxter M, Barton GJ
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, eight 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 eight tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining three tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that more than six biological replicates should be used, rising to more than 12 when it is important to identify SDE genes for all fold changes. If less than 12 replicates are used, a superior combination of true positive and false positive performances makesedgeRthe leading tool. For higher replicate numbers, minimizing false positives is more important andDESeqmarginally outperforms the other tools.
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