| build.mim {minet} | R Documentation |
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.
build.mim(data, estimator="mi.empirical")
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. |
build.mim returns the mutual information matrix.
Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi
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.
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)