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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 (#2 of 3,132)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

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1982 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

Twitter Demographics

The data shown below were collected from the profiles of 221 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,982 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 <1%
Germany 5 <1%
Spain 5 <1%
Brazil 4 <1%
United Kingdom 4 <1%
Japan 3 <1%
Denmark 2 <1%
Portugal 1 <1%
Sweden 1 <1%
Other 12 <1%
Unknown 1932 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 484 24%
Researcher 418 21%
Student > Master 235 12%
Student > Bachelor 180 9%
Student > Doctoral Student 106 5%
Other 264 13%
Unknown 295 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 640 32%
Biochemistry, Genetics and Molecular Biology 589 30%
Medicine and Dentistry 82 4%
Immunology and Microbiology 62 3%
Computer Science 51 3%
Other 219 11%
Unknown 339 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 174. 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 22 May 2023.
All research outputs
#219,580
of 24,514,423 outputs
Outputs from RNA
#2
of 3,132 outputs
Outputs of similar age
#4,055
of 306,369 outputs
Outputs of similar age from RNA
#2
of 35 outputs
Altmetric has tracked 24,514,423 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 3,132 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. 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 306,369 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 98% of its contemporaries.
We're also able to compare this research output to 35 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 97% of its contemporaries.