MIinference: Mutual information based network inference

In saezlab/CNORfeeder: Integration of CellNOptR to add missing links

Mutual information based network inference

Description

This function uses data (CNOlist) to infer a data-driven network using the mutual information based appoaches ARACNe and CLR as implemented in the minet package.

Usage

MIinference(CNOlist, method="ARACNE", PKNgraph=NULL,
            filename="ARACNE")

Arguments

CNOlist	a CNOlist structure, as produced by makeCNOlist
method	a character, the name of the method to be used: ARACNE or CLR. Default, ARACNE
PKNgraph	a network to be used for comparison to assess the directionality of some links. Default is NULL.
filename	name of the sif file saved, default ARACNE

Details

This function transforms the data in a format compatible with minet package, infers the network using aracne or clr as implemented in the minet package and returns the network in the sif format. It is important to notice that mutual information approaches do not allow for determining the directionality of the links thus both directions are considered. The function allows to give as input a network in graph format (graph package, see sif2graph to convert from sif to graph format) to be used as comparison to assess the directionality of some links, e.g. PKN.

Value

sif	the inferred data-driven network in sif format

Author(s)

F.Eduati

References

P. E. Meyer, F. Lafitte and G. Bontempi (2008). MINET: An open source R/Bioconductor Package for Mutual Information based Network Inference. BMC Bioinformatics, 9(1), 2008

See Also

mapDDN2model, sif2graph, model2sif

Examples

data(CNOlistDREAM,package="CellNOptR")
data(DreamModel,package="CellNOptR")
PKNgraph<-sif2graph(model2sif(DreamModel))

method="ARACNE"
#method="CLR"
DDN<-MIinference(CNOlist=CNOlistDREAM, method=method,
                 PKNgraph=PKNgraph, filename=method)

    

saezlab/CNORfeeder documentation built on Feb. 14, 2023, 3:23 p.m.