clr {minet} | R Documentation |
clr
takes the mutual information matrix as input in order to return the infered network - see details.
clr( mim )
mim |
A square matrix whose i,j th element is the mutual information
between variables X_i and X_j - see build.mim . |
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.
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") )
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.
data(syn.data) mim <- build.mim(discretize(syn.data)) net <- clr(mim)