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)