minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information

• Published in: BMC bioinformatics Vol. 9; no. 1; p. 461
• Main Authors: Meyer, Patrick E, Lafitte, Frédéric, Bontempi, Gianluca
• Format: Journal Article Web Resource
• Language: English
• Published: England BioMed Central Ltd 29.10.2008; BioMed Central; BMC
• Subjects: Algorithms; Computational biology; Computational Biology - methods; Data Interpretation, Statistical; DNA microarrays; False Positive Reactions; Gene Expression Profiling - methods; Genetic transcription; Internet; Life sciences; Models, Statistical; Oligonucleotide Array Sequence Analysis; Pattern Recognition, Automated - methods; Programming Languages; Reproducibility of Results; ROC Curve; Sciences du vivant; Software; Transcription, Genetic; Belgium
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-9-461

This paper presents the R/Bioconductor package minet (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 minet (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. The package minet 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.