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limma: Linear Models for Microarray Data
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“This approach accounts for both the within and between group variation when testing for differences between groups.”
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“Next, filtered read counts from all groups were normalized together by the trimmed mean of the M-values normalization method (TMM) to reduce the likelihood of unequal variances between groups [101].”
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“The limma package is a core component of Bioconductor, an R-based open-source software development project in statistical genomics (1,2).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [2]
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Metadata
- Authors
- Smyth, G. K.
- Corresponding author
- Smyth, G. K.
- ORCID
- Smyth, G. K.
- Venue
- Statistics for Biology and Health
- Publisher
- Springer-Verlag
- Published
- 2005-12-29
- Pages
- 397-420
- OpenAlex
- W1546952499
- Cited by
- 5,796
- References
- 9
- Citations by year
- 2026: 95 · 2025: 115 · 2024: 177 · 2023: 212 · 2022: 258 · 2021: 316 · 2020: 329 · 2019: 270 · 2018: 266 · 2017: 356 · 2016: 387 · 2015: 559 · 2014: 529 · 2013: 433 · 2012: 365
- FWCI
- 195.35
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- limma, differential gene expression, microarray data, linear models, empirical Bayes, factorial design, time-course experiments, biological replication
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