LIMMA: Linear Models for Microarray and RNA-seq Data

A software package for the analysis of gene expression studies, especially the use of linear models for analysing designed experiments and the assessment of differential expression. The analysis methods apply to many different technologies, including microarrays, RNA-seq, quantitative PCR and many protein technologies.

LIMMA is available as part of Bioconductor project. To install from the R command line, type

> source("")
> biocLite("limma")
> biocLite("statmod")

Comprehensive documentation is distributed with the package. The Limma User's Guide is also available as a link from the Bioconductor limma package page. Help using LIMMA can be obtained by posting questions or problems to the Bioconductor support site.

Related Packages

LIMMA is a command driven package but menu driven interfaces are also available. See limmaGUI for two-colour arays or affylmGUI for Affymetrix arrays.

Citing limma

Limma implements a body of methodological research by the authors and co-workers. Please cite the appropriate articles when you use results from the software in a publication. Such citations are the main means by which the authors receive professional credit for their work.

The limma software package itself can be cited as:

Ritchie, ME, Phipson, B, Wu, D, Hu, Y, Law, CW, Shi, W, and Smyth, GK (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43 (7), e47. [Full Text]

The above article reviews the overall capabilities of the limma package, both new and old.

Other articles describe the statistical methodology behind particular functions of the package. If you use limma for differential expression analysis, using the functions lmFit, eBayes and topTable, please cite:

Phipson, B, Lee, S, Majewski, IJ, Alexander, WS, and Smyth, GK (2016). Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Annals of Applied Statistics 10, 946-963. [PubMed] [Full text]

If you use the voom function for RNA-seq analysis, please cite:

Law, CW, Chen, Y, Shi, W, and Smyth, GK (2014). Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 15, R29. [Full Text] [Supplementary Information]

If you use the backgroundCorrect function, please cite

Ritchie, ME, Silver, J, Oshlack, A, Holmes, M, Diyagama, D, Holloway, A, and Smyth, GK (2007). A comparison of background correction methods for two-colour microarrays. Bioinformatics 23, 2700-2707. [Full Text] [Supplementary Information]

For normalization of two-colour microarray data, using the read.maimages, normalizeWithinArrays or normalizeBetweenArrays functions, please cite:

Smyth, G. K., and Speed, T. P. (2003). Normalization of cDNA microarray data. Methods 31, 265-273. (Preprint PDF)

If you use the duplicateCorrelation function, please cite

Smyth, G. K., Michaud, J., and Scott, H. (2005). Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21(9), 2067-2075. [Full Text] [Supplementary Information] [Preprint PDF] [Errata] [Faculty1000Prime]

If you estimate array quality weights using arrayWeights, arrayWeightsSimple or arrayWeightsQuick, please cite:

Ritchie, M. E., Diyagama, D., Neilson, J., van Laar, R., Dobrovic, A., Holloway, A., and Smyth, G. K. (2006). Empirical array quality weights in the analysis of microarray data. BMC Bioinformatics 7, Article 261. [Full Text] [Supplementary Information]

If you use the lmscFit function for separate channel analysis of two-channel microarrays, please cite:

Smyth, GK, and Altman, NS (2013). Separate-channel analysis of two-channel microarrays: recovering inter-spot information. BMC Bioinformatics 14, 165. [Full Text]

The construction of design matrices for a number of standard experimental designs is described by:

Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420. [Publisher web site] [Preprint PDF] (Published 8 August 2005)

Data Sets Used in the User's Guide