<it>minet</it>: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information

• Main Authors: Bontempi Gianluca, Lafitte Frédéric, Meyer Patrick E
• Format: Article
• Language: English
• Published: BMC 2008-10-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/9/461

<p>Abstract</p> <p>Results</p> <p>This paper presents the R/Bioconductor package <it>minet </it>(version 1.1.6) which provides a set of functions to infer mutual information networks from a dataset. Once fed with a microarray dataset, the package returns a network where nodes denote genes, edges model statistical dependencies between genes and the weight of an edge quantifies the statistical evidence of a specific (e.g transcriptional) gene-to-gene interaction. Four different entropy estimators are made available in the package <it>minet </it>(empirical, Miller-Madow, Schurmann-Grassberger and shrink) as well as four different inference methods, namely relevance networks, ARACNE, CLR and MRNET. Also, the package integrates accuracy assessment tools, like F-scores, PR-curves and ROC-curves in order to compare the inferred network with a reference one.</p> <p>Conclusion</p> <p>The package <it>minet </it>provides a series of tools for inferring transcriptional networks from microarray data. It is freely available from the Comprehensive R Archive Network (CRAN) as well as from the Bioconductor website.</p>