Power analysis and sample size estimation for RNA-Seq differential expression
Open accessAbstract
It is crucial for researchers to optimize RNA-seq experimental designs for differential expression detection. Currently, the field lacks general methods to estimate power and sample size for RNA-Seq in complex experimental designs, under the assumption of the negative binomial distribution. We simulate RNA-Seq count data based on parameters estimated from six widely different public data sets (including cell line comparison, tissue comparison, and cancer data sets) and calculate the statistical power in paired and unpaired sample experiments. We comprehensively compare five differential expression analysis packages (DESeq, edgeR, DESeq2, sSeq, and EBSeq) and evaluate their performance by power, receiver operator characteristic (ROC) curves, and other metrics including areas under the curve (AUC), Matthews correlation coefficient (MCC), and F -measures. DESeq2 and edgeR tend to give the best performance in general. Increasing sample size or sequencing depth increases power; however, increasing sample size is more potent than sequencing depth to increase power, especially when the sequencing depth reaches 20 million reads. Long intergenic noncoding RNAs (lincRNA) yields lower power relative to the protein coding mRNAs, given their lower expression level in the same RNA-Seq experiment. On the other hand, paired-sample RNA-Seq significantly enhances the statistical power, confirming the importance of considering the multifactor experimental design. Finally, a local optimal power is achievable for a given budget constraint, and the dominant contributing factor is sample size rather than the sequencing depth. In conclusion, we provide a power analysis tool ( http://www2.hawaii.edu/~lgarmire/RNASeqPowerCalculator.htm ) that captures the dispersion in the data and can serve as a practical reference under the budget constraint of RNA-Seq experiments.
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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 [176]
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- Authors
- Ching, T., Huang, S., & Garmire, L. X.
- Affiliations
- University of Hawaiʻi at Mānoa (US)
- Corresponding author
- Garmire, Lana X.
- Venue
- RNA
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2014-09-22
- Volume
- 20
- Issue
- 11
- Pages
- 1684-1696
- ISSN
- 1355-8382 (ISSN-L)
- 1469-9001
- PMC
- PMC4201821
- OpenAlex
- W2157710407
- Cited by
- 263
- References
- 45
- Citations by year
- 2026: 17 · 2025: 24 · 2024: 17 · 2023: 23 · 2022: 19 · 2021: 24 · 2020: 28 · 2019: 31 · 2018: 27 · 2017: 20 · 2016: 23 · 2015: 6 · 2014: 3 · 2012: 1
- FWCI
- 7.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- RNA (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institute of General Medical Sciences (P20 COBRE GM103457)
- 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-seq, statistical power, sample size determination, differential gene expression, negative binomial distribution, DESeq2, edgeR, sequencing depth, long intergenic non-coding RNA
- MeSH terms
- Animals, Computer Simulation, Humans, Regression Analysis, Research Design, RNA, Messenger, Software, Reproducibility of Results, Sequence Analysis, RNA, Sample Size, Gene Expression Profiling, Databases, Genetic, Mice, High-Throughput Nucleotide Sequencing
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