Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• RNA (Cambridge), 2016-06, Vol.22 (6), p.839-851

Ort / Verlag

United States: Cold Spring Harbor Laboratory Press

Erscheinungsjahr

2016

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.