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

Overview of attention for article published in RNA, March 2016
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  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#1 of 2,661)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Citations

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1355 Mendeley
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Title
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?
Published in
RNA, March 2016
DOI 10.1261/rna.053959.115
Pubmed ID
Authors

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

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, 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 1,355 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 14 1%
Spain 5 <1%
Germany 5 <1%
United Kingdom 4 <1%
Brazil 4 <1%
Japan 3 <1%
Australia 2 <1%
Denmark 2 <1%
Czechia 1 <1%
Other 12 <1%
Unknown 1303 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 368 27%
Researcher 332 25%
Student > Master 175 13%
Student > Bachelor 100 7%
Student > Doctoral Student 73 5%
Other 184 14%
Unknown 123 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 530 39%
Biochemistry, Genetics and Molecular Biology 401 30%
Medicine and Dentistry 57 4%
Computer Science 40 3%
Immunology and Microbiology 39 3%
Other 134 10%
Unknown 154 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 165. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 August 2020.
All research outputs
#118,065
of 15,923,575 outputs
Outputs from RNA
#1
of 2,661 outputs
Outputs of similar age
#1,938
of 233,154 outputs
Outputs of similar age from RNA
#1
of 99 outputs
Altmetric has tracked 15,923,575 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,661 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 233,154 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.