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")