Moderated statistical tests for assessing differences in tag abundance
Open accessAbstract
Motivation: Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. They are sensitive technologies for measuring gene expression on a genomic scale, without the need for prior knowledge of the genome sequence. As the cost of sequencing DNA decreases, the number of DGE datasets is expected to grow dramatically. Various tests of differential expression have been proposed for replicated DGE data using binomial, Poisson, negative binomial or pseudo-likelihood (PL) models for the counts, but none of the these are usable when the number of replicates is very small. Results: We develop tests using the negative binomial distribution to model overdispersion relative to the Poisson, and use conditional weighted likelihood to moderate the level of overdispersion across genes. Not only is our strategy applicable even with the smallest number of libraries, but it also proves to be more powerful than previous strategies when more libraries are available. The methodology is equally applicable to other counting technologies, such as proteomic spectral counts. Availability: An R package can be accessed from http://bioinf.wehi.edu.au/resources/ Contact: smyth@wehi.edu.au Supplementary information: http://bioinf.wehi.edu.au/resources/
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The quasi-likelihood framework in edgeR was used to handle the pronounced variability of single-cell expression states.
“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 [80]
A negative binomial model in edgeR was used to account for the sparse single-cell count matrix.
“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 [80]
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- Authors
- Robinson, M. D., & Smyth, G. K.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Smyth, Gordon K.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-09-19
- Volume
- 23
- Issue
- 21
- Pages
- 2881-2887
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2145416275
- Cited by
- 856
- References
- 28
- Citations by year
- 2026: 9 · 2025: 25 · 2024: 43 · 2023: 44 · 2022: 44 · 2021: 56 · 2020: 62 · 2019: 61 · 2018: 63 · 2017: 63 · 2016: 82 · 2015: 58 · 2014: 70 · 2013: 66 · 2012: 39
- FWCI
- 5.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Health and Medical Research Council (406657)
- 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 › Bioinformatics and Genomic Networks
- Keywords
- differential gene expression, digital gene expression, negative binomial distribution, overdispersion, gene expression counts, spectral counts, R package
- MeSH terms
- Algorithms, Computer Simulation, Data Interpretation, Statistical, Models, Genetic, Signal Processing, Computer-Assisted, Models, Statistical, Poisson Distribution, Likelihood Functions, Sequence Analysis, DNA, Expressed Sequence Tags, Gene Expression Profiling
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