clr: Context Likelihood or Relatedness Network

In minet: Mutual Information NETworks

Description

clr takes the mutual information matrix as input in order to return the infered network - see details.

Usage

clr( mim, skipDiagonal=1 )

Arguments

mim	A square matrix whose i,j th element is the mutual information between variables X_i and X_j - see build.mim.
skipDiagonal	Skips the diagonal in the calculation of the mean and sd, default=1.

Details

The CLR algorithm is an extension of relevance network. Instead of considering the mutual information I(Xi;Xj) between features Xi and Xj, it takes into account the score sqrt(zi^2+zj^2), where

zi = max( 0, ( I(Xi;Xj)-mean(Xi) )/sd(Xi) )

and mean(Xi) and sd(Xi) are, respectively, the mean and the standard deviation of the empirical distribution of the mutual information values I(Xi,Xk), k=1,...,n.

Value

clr 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 comand plot( as( returned.matrix ,"graphNEL") )

Author(s)

Implementation: P. E. Meyer and J.C.J. van Dam

References

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, aracne, mrnet, mrnetb

Examples

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

Example output

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