Cited by 286Gene expression and cancer classificationGenomics and Phylogenetic StudiesSingle-cell and spatial transcriptomics

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A comparison of statistical methods for detecting differentially expressed genes from RNA‐seq data

Kvam, Vanessa M., Liu, Peng, Si, Yaqing·2012·American Journal of Botany

DOI 10.3732/ajb.1100340

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Abstract

RNA‐Seq technologies are quickly revolutionizing genomic studies, and statistical methods for RNA‐seq data are under continuous development. Timely review and comparison of the most recently proposed statistical methods will provide a useful guide for choosing among them for data analysis. Particular interest surrounds the ability to detect differential expression (DE) in genes. Here we compare four recently proposed statistical methods, edgeR, DESeq, baySeq, and a method with a two‐stage Poisson model (TSPM), through a variety of simulations that were based on different distribution models or real data. We compared the ability of these methods to detect DE genes in terms of the significance ranking of genes and false discovery rate control. All methods compared are implemented in freely available software. We also discuss the availability and functions of the currently available versions of these software.

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Authors: Kvam, V. M., Liu, P., & Si, Y.
Affiliations: Iowa State University (US)
Corresponding author: Liu, Peng
ORCID: Liu, Peng
Si, Yaqing
Venue: American Journal of Botany
Publisher: Wiley
Published: 2012-02
Volume: 99
Issue: 2
Pages: 248-256
ISSN: 0002-9122 (ISSN-L)
1537-2197
DOI: 10.3732/ajb.1100340
OpenAlex: W2114405789
Cited by: 281
References: 30
Citations by year: 2026: 3 · 2025: 6 · 2024: 13 · 2023: 12 · 2022: 15 · 2021: 12 · 2020: 22 · 2019: 13 · 2018: 20 · 2017: 18 · 2016: 23 · 2015: 36 · 2014: 40 · 2013: 32 · 2012: 16
FWCI: 15.93
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: American Journal of Botany (publishedVersion)
PubMed (publishedVersion)
Funders: National Science Foundation (0701736)
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, edgeR, DESeq, false discovery rate, comparison of statistical methods
MeSH terms: Base Sequence, Computer Simulation, Models, Genetic, ROC Curve, Software, Reproducibility of Results, Poisson Distribution, Genes, Plant, Sequence Analysis, RNA, Gene Expression Regulation, Plant, RNA, Plant, Computational Biology, Databases, Nucleic Acid

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A comparison of statistical methods for detecting differentially expressed genes from RNA‐seq data

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