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


CONCLUSIONS
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.
Meyer
Patrick E
PE
Machine Learning Group, Computer Science Department, Faculty of Science, Université Libre de Bruxelles, 1050 Brussels, Belgium. pmeyer@ulb.ac.be
Lafitte
Frédéric
F
Bontempi
Gianluca
G
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods; D020407:Internet; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods; D011381:Programming Languages; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software; D014158:Transcription, Genetic