Cited by 869Gene expression and cancer classificationGenomics and Phylogenetic StudiesBioinformatics and Genomic Networks

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Moderated statistical tests for assessing differences in tag abundance

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Robinson, Mark D., Smyth, Gordon K.·2007·Bioinformatics

DOI 10.1093/bioinformatics/btm453

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Abstract

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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Authors: Robinson, M. D., & Smyth, G. K.
Affiliations: The University of Melbourne (AU)
Corresponding author: Smyth, Gordon K.
ORCID: Robinson, Mark D.
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
DOI: 10.1093/bioinformatics/btm453
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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