mrnetb.wrap: mrnetb wrapper function

In netbenchmark: Benchmarking of several gene network inference methods

Description

Default wrapper function for the MRNETB network inference algorithm

Usage

    mrnetb.wrap(data)

Arguments

data	Numeric matrix with the microarray dataset to infer the network. Columns contain variables and rows contain samples.

Details

mrnetb takes the mutual information matrix as input in order to infer the network using the maximum relevance/minimum redundancy criterion combined with a backward elimination and a sequential replacement - see references. This method is a variant of mrnet.

Value

mrnetb.wrap returns a matrix which is the weighted adjacency matrix of the network inferred by mrnetb algorithm. The wrapper uses the "spearman" correlation (can be used with continuous data) to estimate the entropy - see build.mim.

References

Patrick E. Meyer, Daniel Marbach, Sushmita Roy and Manolis Kellis. Information-Theoretic Inference of Gene Networks Using Backward Elimination. The 2010 International Conference on Bioinformatics and Computational Biology.

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.

See Also

netbenchmark, evaluate, mrnet

Examples

    # Data
    data <- runif(100)
    dim(data) <- c(10,10)
    # Inference
    net <- mrnetb.wrap(as.data.frame(data))

netbenchmark documentation built on May 2, 2019, 6:08 p.m.