aracne: Algorithm for the Reconstruction of Accurate Cellular...

In minet: Mutual Information NETworks

Description

This function takes the mutual information matrix as input in order to return the infered network according to the Aracne algorithm. This algorithm applies the data processing inequality to all triplets of nodes in order to remove the least significant edge in each triplet.

Usage

aracne( mim, eps=0 )

Arguments

mim	A square matrix whose i,j th element is the mutual information between variables X_i and X_j - see build.mim.
eps	Numeric value indicating the threshold used when removing an edge : for each triplet of nodes (i,j,k), the weakest edge, say (ij), is removed if its weight is below min{(ik),(jk)}-eps - see references.

Details

The Aracne procedure starts by assigning to each pair of nodes a weight equal to their mutual information. Then, the weakest edge of each triplet is interpreted as an indirect interaction and is removed if the difference between the two lowest weights is above a threshold eps.

Value

aracne returns a matrix which is the weighted adjacency matrix of the network. In order to display the network, load the package Rgraphviz and use the following command:
plot( as( returned.matrix ,"graphNEL") )

References

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.

Patrick E. Meyer, Frederic Lafitte and Gianluca Bontempi. minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information. BMC Bioinformatics, Vol 9, 2008.

See Also

build.mim, clr, mrnet, mrnetb

Examples

data(syn.data)
mim <- build.mim(syn.data,estimator="spearman")
net <- aracne(mim)

Example output

minet documentation built on Nov. 8, 2020, 6 p.m.