Small-sample estimation of negative binomial dispersion, with applications to SAGE data
Abstract
We derive a quantile-adjusted conditional maximum likelihood estimator for the dispersion parameter of the negative binomial distribution and compare its performance, in terms of bias, to various other methods. Our estimation scheme outperforms all other methods in very small samples, typical of those from serial analysis of gene expression studies, the motivating data for this study. The impact of dispersion estimation on hypothesis testing is studied. We derive an "exact" test that outperforms the standard approximate asymptotic tests.
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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 [79]
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 [79]
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- Authors
- Robinson, M. D., & Smyth, G. K.
- Affiliations
- The University of Melbourne (AU)
- Walter and Eliza Hall Institute of Medical Research (AU)
- Corresponding author
- Robinson, M. D.
- Venue
- Biostatistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-07-11
- Volume
- 9
- Issue
- 2
- Pages
- 321-332
- ISSN
- 1465-4644 (ISSN-L)
- 1468-4357
- OpenAlex
- W2141425631
- Cited by
- 1,073
- References
- 12
- Citations by year
- 2026: 19 · 2025: 34 · 2024: 43 · 2023: 66 · 2022: 60 · 2021: 71 · 2020: 75 · 2019: 91 · 2018: 117 · 2017: 105 · 2016: 87 · 2015: 83 · 2014: 80 · 2013: 60 · 2012: 38
- FWCI
- 2.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biostatistics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council
- National Health and Medical Research Council (406657)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- small sample estimation, serial analysis of gene expression, bias, hypothesis testing, exact tests, count data
- MeSH terms
- Biometry, Humans, Regression Analysis, Research Design, RNA, Messenger, Stochastic Processes, Weights and Measures, Gene Library, Bias, Binomial Distribution, Likelihood Functions, Information Storage and Retrieval, Sample Size, Expressed Sequence Tags, Gene Expression Profiling
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