LIMMA: Linear Models for Microarray Data
A software package for the analysis of gene expression microarray data, especially the use of linear models for analysing designed experiments and the assessment of differential expression. The package includes pre-processing capabilities for two-colour spotted arrays. The differential expression methods apply to all array platforms and treat Affymetrix, single channel and two channel experiments in a unified way.
LIMMA is available as part of Bioconductor project. To install from the R command line, type
> source("http://www.bioconductor.org/biocLite.R") > 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 sending questions or problems to the Bioconductor mailing list email@example.com.
Limma is an implementation of a body of methodological research by the authors and co-workers. Please cite the appropriate methodological papers 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.
If you use limma for differential expression analysis, using the
Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, No. 1, Article 3. (Online, Tech Report PDF)
The normexp and other background correction features for two-colour microarray data can be cited as
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. [Publisher Full Text] [Supplementary Information]
For normalization of two-colour microarray data, please cite:
Smyth, G. K., and Speed, T. P. (2003). Normalization of cDNA microarray data. Methods 31, 265-273. (PDF)
The above article describes the functions
etc, including the use of spot quality weights.
If you use limma with duplicate spots or technical replication
duplicateCorrelation, please cite
Smyth, G. K., Michaud, J., and Scott, H. (2005). The use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21(9), 2067-2075. (Supplementary Information, Online, PDF, Errata)
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 for microarray data. BMC Bioinformatics 7, 261. (Online, Supplementary Information)
The limma software itself can be cited as:
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, PDF)
The above article describes the software package in the
context of the Bioconductor project and surveys the range of
experimental designs for which the package can be used, including
spot-specific dye-effects. The pre-processing capabilities of the
package are also described but more briefly, with examples of
background correction, spot quality weights and filtering with
control spots. This article is also the best current reference for
normexp background correction
Finally, if you are using one of the menu-driven interfaces to the software, please cite the appropriate one of
Wettenhall, J. M., and Smyth, G. K. (2004). limmaGUI: a graphical user interface for linear modeling of microarray data. Bioinformatics 20, 3705-3706. (Online)
Wettenhall, J. M., Simpson, K. M., Satterley, K., and Smyth, G. K. (2006). affylmGUI: a graphical user interface for linear modeling of single channel microarray data. Bioinformatics 22, 897 - 899. (Online)