build.mim {minet}R Documentation

Build Mutual Information Matrix

Description

build.mim takes the dataset as input and computes the mutual information beetween all pair of variables according to the mutual inforamtion estimator estimator. The results are saved in the mutual information matrix (MIM), a square matrix whose (i,j) element is the mutual information between variables Xi and Xj.

Usage

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

Arguments

data data.frame containing gene expression data or any dataset where columns contain variables/features and rows contain outcomes/samples.
estimator The name of the mutual information estimator. The package implements four estimators : "mi.empirical", "mi.mm", "mi.shrink", "mi.sg" (default:"mi.empirical") - see details. These estimators require discrete data values - see discretize.

Details

  • "mi.empirical" : This estimator computes the entropy of the empirical probability distribution.
  • "mi.mm" : This is the Miller-Madow asymptotic bias corrected empirical estimator.
  • "mi.shrink" : This is a shrinkage estimate of the entropy of a Dirichlet probability distribution.
  • "mi.sg" : This is the Schurmann-Grassberger estimate of the entropy of a Dirichlet probability distribution.

    Value

    build.mim returns the mutual information matrix.

    Author(s)

    Patrick E. Meyer, Frederic Lafitte, Gianluca 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, aracne, mrnet

    Examples

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

    [Package minet version 1.2.0 Index]