Description Details Author(s) References
LIMMA is a library for the analysis of gene expression microarray data, especially the use of linear models for analysing designed experiments and the assessment of differential expression. LIMMA provides the ability to analyse comparisons between many RNA targets simultaneously in arbitrary complicated designed experiments. Empirical Bayesian methods are used to provide stable results even when the number of arrays is small. The normalization and data analysis functions are for two-colour spotted microarrays. The linear model and differential expression functions apply to all microarrays including Affymetrix and other multi-array oligonucleotide experiments.
There are three types of documentation available:
(1) |
The LIMMA User's Guide can be reached through the "User
Guides and Package Vignettes" links at the top of the LIMMA
contents page. The function limmaUsersGuide gives
the file location of the User's Guide. |
(2) | An overview of limma functions grouped by purpose is contained in the numbered chapters at the top of the LIMMA contents page, of which this page is the first. |
(3) | The LIMMA contents page gives an alphabetical index of detailed help topics. |
The function changeLog
displays the record of changes to the package.
Gordon Smyth
Smyth, G. K., Yang, Y.-H., Speed, T. P. (2003). Statistical issues in microarray data analysis. Methods in Molecular Biology 224, 111-136.
Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, Volume 3, Article 3. http://www.statsci.org/smyth/pubs/ebayes.pdf
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, 2005.
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