minet {minet} | R Documentation |
For a given dataset, minet
infers a network using the algorithm given by method
on the mutual information matrix.
The mutual information between all pairs of variables in dataset
is computed
according to the estimator
argument.
minet(dataset, method="mrnet", estimator="empirical", disc.method="equalfreq", nbins=sqrt(nrow(dataset)) )
dataset |
Columns contain variables/features, rows contain outcomes/samples. |
method |
The package implements the following algorithms : clr, aracne, mrnet - see references. |
estimator |
The package implements the following estimators : empirical, millermadow, shrink, gaussian - see build.mim . |
disc.method |
Two discretization methods can be used : "equalfreq" or "equalwidth" - see disc . |
nbins |
The number of bins to be used to discretize the data. |
minet
returns a matrix which is the weighted adjacency matrix of the network. The weights range from 0 to 1 and can be seen
as a confidence measure on the presence of the arcs. In order to display the network, load the package Rgraphviz and use the following command:
plot( as( returned.matrix ,"graphNEL") )
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.
Adam A. Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins, Gustavo Stolovitzky, Riccardo Dalla Favera, and Andrea Califano. Aracne : An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 2006.
Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno, Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J. Collins, and Timothy S. Gardner. Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biology, 2007.
build.mim
, clr.net
, mr.net
, aracne.net
data(syn.data) net1 <- minet( syn.data ) net2 <- minet( syn.data, estimator="shrink" ) net3 <- minet( syn.data, method="mrnet", estimator="gaussian" )