limma: Linear Models for Microarray Data
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The limma linear model approach accounts for both the within and between group variation when testing for differences between groups.
“This approach accounts for both the within and between group variation when testing for differences between groups.”
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.
“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].”
limma is a core component of Bioconductor.
“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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- 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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