build.mim {minet}R Documentation

Build Mutual Information Matrix

Description

The inference methods implemented in this package require at first the computation of the mutual information matrix (MIM), a square matrix whose (i,j) element is the mutual information between variables Xi and Xj. build.mim returns the MIM according to the mutual information estimator estimator.

Usage

build.mim(data, estimator="empirical")

Arguments

data Gene expression dataset or any dataset where columns contain variables/features and rows contain outcomes/samples.
estimator Mutual information estimator. The package implements four estimators : "gaussian", "empirical", "shrink","millermadow" (default:"empirical") - see details. The data must be discrete in order to use empirical, shrink or miller-madow estimators - see disc.

Details

  • "gaussian" : This estimator assumes normally distributed variables.
  • "empirical" : This estimator computes the entropy of the empirical probability distribution.
  • "millermadow" : This is the Miller-Madow asymptotic bias corrected empirical estimator.
  • "shrink" : This is a shrinkage estimate of the entropy of a Dirichlet probability distribution.

    Value

    build.mim returns the mutual information matrix.

    Author(s)

    P.E.Meyer, F.Lafitte, G.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.

    J. Beirlant, E. J. Dudewica, L. Gyofi, and E. van der Meulen. Nonparametric entropy estimation : An overview. Journal of Statistics, 1997.

    Jean Hausser. Improving entropy estimation and the inference of genetic regulatory networks. Master thesis of the National Institute of Applied Sciences of Lyon, 2006.

    See Also

    clr.net, aracne.net, mr.net

    Examples

      data(syn.data)
      #mutual information estimator
      estimator="empirical"
      #number of bins used to discretize
      nb.bins = sqrt(nrow(syn.data))
      mim <- build.mim(disc(syn.data,nbins=nb.bins),estimator)
    

    [Package minet version 1.1.3 Index]