c3net.wrap: c3net wrapper function

In netbenchmark: Benchmarking of several gene network inference methods

Description

Default wrapper function for the C3net network inference algorithm

Usage

    c3net.wrap(data)

Arguments

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

Details

The Conservative Causal Core NETwork (C3NET) consists of two main steps. The first step is the elimination of non-significant edges, and the second step selects for each gene the edge among the remaining ones with maximum mutual information value. C3NET does not aim at inferring the entire network underlying gene regulation but mainly tries to recover the core structure.

Value

c3net.wrap returns a matrix which is the weighted adjacency matrix of the network inferred by c3net algorithm. The Mutual Information threshold is set to 0 - see c3net.

References

Altay, G\"okmen, and Frank Emmert-Streib. "Inferring the conservative causal core of gene regulatory networks." BMC Systems Biology 4.1 (2010): 132.

See Also

netbenchmark, evaluate, c3net

Examples

    # Data
    data <- runif(100)
    dim(data) <- c(10,10)
    # Inference
    net <- c3net.wrap(as.data.frame(data))

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