Calculating Sample Size Estimates for RNA Sequencing Data
Open accessAbstract
Background: Given the high technical reproducibility and orders of magnitude greater resolution than microarrays, next-generation sequencing of mRNA (RNA-Seq) is quickly becoming the de facto standard for measuring levels of gene expression in biological experiments. Two important questions must be taken into consideration when designing a particular experiment, namely, 1) how deep does one need to sequence? and, 2) how many biological replicates are necessary to observe a significant change in expression? Results: Based on the gene expression distributions from 127 RNA-Seq experiments, we find evidence that 91% ± 4% of all annotated genes are sequenced at a frequency of 0.1 times per million bases mapped, regardless of sample source. Based on this observation, and combining this information with other parameters such as biological variation and technical variation that we empirically estimate from our large datasets, we developed a model to estimate the statistical power needed to identify differentially expressed genes from RNA-Seq experiments. Conclusions: Our results provide a needed reference for ensuring RNA-Seq gene expression studies are conducted with the optimally sample size, power, and sequencing depth. We also make available both R code and an Excel worksheet for investigators to calculate for their own experiments.
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The exact statistical power depends on the differential expression method, and software packages can provide theoretical power estimates over a range of variables given the within-group variance.
“The exact power will depend on the method used for differential expression analysis, and software packages exist that provide a theoretical estimate of power over a range of variables, given the within-group variance of the samples, which is intrinsic to the experiment [190, 191].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [190]
Many guidelines for DGE and network analysis are based on theoretical models and empirical assessments, but most use data lacking the spatial, temporal, or disease-relevant complexities of CNS or post-mortem tissue.
“For DGE and network analysis, there are many studies with guidelines that are based on theoretical models and empirical assessments40,173–176, but most studies use data from experiments that do not have the spatial, temporal or disease-relevant complexities that occur in studies of the CNS or post-mortem tissue.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [173]
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- Authors
- Hart, S. N., Therneau, T. M., Zhang, Y., Poland, G. A., & Kocher, J.-P.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Hart, Steven N.
- Venue
- Journal of Computational Biology
- Publisher
- SAGE Publications
- Published
- 2013-08-20
- Volume
- 20
- Issue
- 12
- Pages
- 970-978
- ISSN
- 1066-5277 (ISSN-L)
- 1557-8666
- PMC
- PMC3842884
- OpenAlex
- W2023562174
- Cited by
- 341
- References
- 20
- Citations by year
- 2026: 22 · 2025: 23 · 2024: 32 · 2023: 58 · 2022: 32 · 2021: 35 · 2020: 21 · 2019: 30 · 2018: 29 · 2017: 17 · 2016: 19 · 2015: 14 · 2014: 9
- FWCI
- 6.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Allergy and Infectious Diseases (U01AI089859)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- RNA sequencing, sample size determination, statistical power, sequencing depth, biological replicates, differentially expressed genes, gene expression, technical variability, biological variation, gene expression distribution
- MeSH terms
- Algorithms, Animals, Humans, Models, Biological, RNA, Messenger, Sample Size, Gene Expression Profiling, High-Throughput Nucleotide Sequencing
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