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

Abstract

Results: 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.

Conclusion: 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.

Figure 3

Precision-Recall curves plotted with show.pr(table).

Figure 1

The four steps in the minet function (discretization disc, mutual information matrix build.mim, inference mrnet, aracne, clr and normalization norm.

Figure 2

Graph generated with minet and plotted with Rgraphviz.