build.mim {minet} | R Documentation |
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
.
build.mim(data, estimator="empirical")
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 . |
build.mim
returns the mutual information matrix.
P.E.Meyer, F.Lafitte, G.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="empirical" #number of bins used to discretize nb.bins = sqrt(nrow(syn.data)) mim <- build.mim(disc(syn.data,nbins=nb.bins),estimator)