minet {minet}R Documentation

Mutual Information Network

Description

For a given dataset, minet infers the network in two steps. First, the mutual information between all pairs of variables in dataset is computed according to the estimator argument. Then the algorithm given by method considers the estimated mutual informations in order to build the network.

Usage

minet(dataset, method="mrnet", estimator="mi.empirical", 
      disc="equalfreq", nbins=sqrt(nrow(dataset)) )

Arguments

dataset data.frame where columns contain variables/features and rows contain outcomes/samples.
method The name of the inference algorithm : "clr", "aracne" or "mrnet" (default: "mrnet") - see references.
estimator The name of the mutual information estimator : "mi.empirical", "mi.mm", "mi.shrink" or "mi.sg"(default: "mi.empirical") - see build.mim.
disc The name of the discretization method to be used : "equalfreq" or "equalwidth" (default: "equalfreq") - see discretize.
nbins Integer giving the number of bins to be used in the discretization.

Value

minet returns a matrix which is the weighted adjacency matrix of the network. The weights range from 0 to 1 and can be seen as a confidence measure on the presence of the arcs. In order to display the network, load the package Rgraphviz and use the following command:
plot( as( returned.matrix ,"graphNEL") )

Author(s)

Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi

References

Patrick E. Meyer, Kevin Kontos, Frederic Lafitte, and Gianluca Bontempi. Information-theoretic inference of large transcriptional regulatory networks. EURASIP Journal on Bioinformatics and Systems Biology, 2007.

Adam A. Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins, Gustavo Stolovitzky, Riccardo Dalla Favera, and Andrea Califano. Aracne : An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 2006.

Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno, Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J. Collins, and Timothy S. Gardner. Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biology, 2007.

See Also

build.mim, clr, mrnet, aracne

Examples

data(syn.data)
net1 <- minet( syn.data )
net2 <- minet( syn.data, estimator="mi.shrink" )
net3 <- minet( syn.data, method="clr", estimator="mi.sg" )

[Package minet version 1.2.0 Index]