The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor
Open accessAbstract
RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations) while optionally adjusting for other systematic factors that affect the data-collection process. There are a number of subtle yet crucial aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a state-of-the-art computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and, in particular, on two widely used tools, DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4–10 samples) can be <1 h, with computation time <1 d using a standard desktop PC.
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Cites
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Cited by
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Self-citationFusobacterium nucleatum Promotes Chemoresistance to Colorectal Cancer by Modulating Autophagy
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Normalization and microbial differential abundance strategies depend upon data characteristics
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Metadata
- Authors
- Anders, S., McCarthy, D. J., Chen, Y., Okoniewski, M., Smyth, G. K., Huber, W., & Robinson, M. D.
- Venue
- Nature Protocols
- Published
- 2013-08-22
- Volume
- 8
- Issue
- 9
- Pages
- 1765-1786
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W2021880506
- Cited by
- 1,146
- References
- 26
- FWCI
- 38.60
- Type
- journal article
- Language
- en
- Open access
- green
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