| 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(dataset, estimator = "mi.mm", disc = "none", nbins = sqrt(NROW(dataset)))
dataset |
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 for discrete data: "mi.empirical", "mi.mm", "mi.shrink", "mi.sg" (default:"mi.empirical") - see details. "pearson", "spearman" and "kendall" can be used for continuous data but in that case build.mim computes the rho-square matrix. This matrix leads to the same networks than those based on mutual information if the variables are normally distributed. |
disc |
The name of the discretization method to be used :"equalfreq", "equalwidth" or "globalequalwidth" (default : "equalfreq") - see infotheo package. |
nbins |
Integer specifying the number of bins to be used for the discretization if disc is set properly. By default the number of bins is set to sqrt(N) where N is the number of samples. |
build.mim returns the mutual information matrix.
Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi
Patrick E. Meyer, Frederic Lafitte, and Gianluca Bontempi. minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information. BMC Bioinformatics, Vol 9, 2008.
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) mim <- build.mim(syn.data,estimator="spearman")