The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
A comparison of statistical methods for detecting differentially expressed genes from RNA‐seq data
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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“Most comparison studies have focused on simulated datasets [56, 208, 209] or on samples to which exogenous RNA (‘spike-in’) has been added in known quantities [63, 196].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [208]
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A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794
Ten years of next-generation sequencing technology
van Dijk, E., Auger, H., Jaszczyszyn, Y., & Thermes, C. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.07.001 · Cited by 1,755
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A comparison of methods for differential expression analysis of RNA-seq data
Soneson, C., & Delorenzi, M. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-91 · Cited by 887
ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq
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Metadata
- Authors
- Kvam, V. M., Liu, P., & Si, Y.
- Affiliations
- Iowa State University (US)
- Corresponding author
- Liu, Peng
- ORCID
- Venue
- American Journal of Botany
- Publisher
- Wiley
- Published
- 2012-02
- Volume
- 99
- Issue
- 2
- Pages
- 248-256
- ISSN
- 0002-9122 (ISSN-L)
- 1537-2197
- 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
- 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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