Differential expression analysis for sequence count data
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Motivation: High throughput nucleotide sequencing provides quantitative readouts in assays for RNA expression (RNA-Seq), protein-DNA binding (ChIP-Seq), cell counting. Statistical inference of differential signal in these data needs to take into account their natural variability throughout the dynamic range. When the number of replicates is small, error modeling is needed to achieve statistical power. Results: We propose an error model that uses the negative binomial distribution, with variance and mean linked by local regression, to model the null distribution of the count data. The method controls type-I error and provides good detection power.Availability: A free open-source R/Biondonductor software package, called "DESeq", is available from "http://www-huber.embl.de/users/anders/DESeq":http://www-huber.embl.de/users/anders/DESeq
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Almost all top-attention genes except markers were identified as differentially expressed genes using DESeq, as potential novel markers.
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- Authors
- Anders, S., & Huber, W.
- Affiliations
- European Molecular Biology Laboratory (ES)
- Corresponding author
- Anders, Simon
- Venue
- Nature Precedings
- Published
- 2010-03-15
- ISSN
- 1756-0357 (ISSN-L)
- OpenAlex
- W3021135130
- Cited by
- 328
- References
- 22
- Citations by year
- 2026: 28 · 2025: 33 · 2024: 67 · 2023: 44 · 2022: 52 · 2021: 49 · 2020: 17 · 2019: 8 · 2018: 11 · 2017: 4 · 2016: 1 · 2015: 2 · 2014: 4 · 2013: 2 · 2012: 4
- Type
- posted content
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Nature Precedings (publishedVersion, cc-by)
- Nature Precedings (submittedVersion)
- Funders
- European Commission
- 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, count data, negative binomial distribution, local regression, error modeling, type I error control, statistical power, RNA-seq, ChIP-seq, cell counting, DESeq
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