RNA-seq differential expression studies: more sequence or more replication?
Open accessAbstract
Motivation: RNA-seq is replacing microarrays as the primary tool for gene expression studies. Many RNA-seq studies have used insufficient biological replicates, resulting in low statistical power and inefficient use of sequencing resources. Results: We show the explicit trade-off between more biological replicates and deeper sequencing in increasing power to detect differentially expressed (DE) genes. In the human cell line MCF7, adding more sequencing depth after 10 M reads gives diminishing returns on power to detect DE genes, whereas adding biological replicates improves power significantly regardless of sequencing depth. We also propose a cost-effectiveness metric for guiding the design of large-scale RNA-seq DE studies. Our analysis showed that sequencing less reads and performing more biological replication is an effective strategy to increase power and accuracy in large-scale differential expression RNA-seq studies, and provided new insights into efficient experiment design of RNA-seq studies. Availability and implementation: The code used in this paper is provided on: http://home.uchicago.edu/∼jiezhou/replication/. The expression data is deposited in the Gene Expression Omnibus under the accession ID GSE51403. Contact: kpwhite@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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Increasing sequencing depth can improve statistical power for lowly expressed genes, and for any given sample there is a sequencing level at which further power improvement is best achieved by increasing replicate number.
“Increasing sequencing depth also can improve statistical power for lowly expressed genes [10, 194], and for any given sample there exists a level of sequencing at which power improvement is best achieved by increasing the number of replicates [195].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [195]
In most experiments, biological variability is greater than technical variability, so biological replicates are of greater value than technical replicates.
“In most experiments, biological variability is greater than technical variability, so biological replicates are of greater value than technical replicates174,175,219.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [219]
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- Authors
- Liu, Y., Zhou, J., & White, K. P.
- Affiliations
- University of Chicago (US)
- Corresponding author
- White, Kevin P.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-12-04
- Volume
- 30
- Issue
- 3
- Pages
- 301-304
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3904521
- OpenAlex
- W2127372456
- Cited by
- 497
- References
- 5
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 23 · 2023: 26 · 2022: 30 · 2021: 49 · 2020: 50 · 2019: 46 · 2018: 44 · 2017: 49 · 2016: 65 · 2015: 52 · 2014: 23
- FWCI
- 20.78
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Chicago Community Trust
- Chicago Biomedical Consortium
- National Institute of General Medical Sciences (P50GM081892)
- 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, differential gene expression, biological replicates, sequencing depth, statistical power, experimental design, cost-effectiveness, MCF-7 cells
- MeSH terms
- Humans, Reproducibility of Results, Sequence Analysis, RNA, Sample Size, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, MCF-7 Cells
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