The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation
Open accessAbstract
A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.
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“This approach is commonly applied in learning error models for bulk RNA-seq in the context of differential expression analysis [22–25], but to our knowledge has not been previously applied in this context for single-cell normalization.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [22]
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“Additionally, edgeR [22] introduced GLM algorithms and statistical methods for estimating biological variation on a genewise basis and separating it from technical variation.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [22]
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“We demonstrate that a regularization step, a commmon step in bulk RNA-seq analysis [22, 28] where parameter estimates are pooled across genes with similar mean abundance, can effectively overcome this challenge and yield reproducible models.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [22]
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“and the quasi-likelihood framework within edgeR to handle the pronounced variability of the single cell expression states.”
From Epigenetic regulation of brain region-specific microglia clearance activity (2018) · cites this paper as [82]
In the citing paper
“We used the negative binomial (NB) model implemented in edgeR (3.10.0)79–83 for our single cell differential expression analysis to explicitly account for the sparse count matrix”
From Epigenetic regulation of brain region-specific microglia clearance activity (2018) · cites this paper as [82]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
Self-citationA Simple Sequentially Rejective Multiple Test Procedure
(1979) · Scandinavian Journal of Statistics · doi:10.2307/4615733 · Cited by 21,526
(2005) · Springer eBooks · doi:10.1007/0-387-27255-0_9 · Cited by 20,333
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Cited by
Sorted by citations
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Self-citationvoom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155
Self-citationHafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479
The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote
Liao, Y., Smyth, G. K., & Shi, W. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt214 · Cited by 3,064
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Metadata
- Authors
- McCarthy, D. J., Chen, Y., & Smyth, G. K.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Smyth, Gordon K.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-01-28
- Volume
- 40
- Issue
- 10
- Pages
- 4288-4297
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC3378882
- OpenAlex
- W2130116522
- Cited by
- 5,195
- References
- 70
- Citations by year
- 2026: 203 · 2025: 339 · 2024: 496 · 2023: 527 · 2022: 680 · 2021: 828 · 2020: 640 · 2019: 463 · 2018: 354 · 2017: 237 · 2016: 157 · 2015: 110 · 2014: 92 · 2013: 47 · 2012: 18
- FWCI
- 34.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Medical Research Council
- National Health and Medical Research Council
- 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
- differential gene expression, RNA-seq read counts, biological variation, technical variability, empirical Bayes, edgeR, generalized linear models, paired design, carcinoma, profile likelihood, count data analysis, DNA sequencing, ChIP-seq, DNA methylation
- MeSH terms
- Algorithms, Bayes Theorem, Carcinoma, Squamous Cell, Mouth Neoplasms, Genetic Variation, Linear Models, Sequence Analysis, RNA, Gene Expression Profiling, High-Throughput Nucleotide Sequencing
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