clr {minet} | R Documentation

## Context Likelihood or Relatedness Network

### Description

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

### Usage

    clr( mim )

### Arguments

`mim` | A square matrix whose i,j th element is the mutual information between variables X_i and X_j - see `build.mim`.

### 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") )

### 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`

### Examples

    data(syn.data)
    mim <- build.mim(discretize(syn.data))
    net <- clr(mim)

[Package minet version 1.4.0 Index]