clr.wrap: CLR wrapper function

In netbenchmark: Benchmarking of several gene network inference methods

Description

Default wrapper function for the CLR network inference algorithm

Usage

    clr.wrap(data)

Arguments

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

Details

The Context Likelihood or Relatedness network (CLR) method derives a score that is associated to the empirical distribution of the mutual information values, in practice the score between gene Xi and gene Xj is defined as follows z_{ij}=√{z^2_i+z^2_j}, where:

z_i=max( 0,(I(Xi;Xj)-μ_i)/σ_i )

μ_i and σ_i are respectively the mean and standard deviation of the empirical distribution of the mutual information between both genes.

Value

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

References

Faith, Jeremiah J., et al. "Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles." PLoS biology 5.1 (2007): e8.

See Also

netbenchmark, evaluate, clr

Examples

    # Data
    data <- grndata::getData(datasource.name = "toy",FALSE)
    # Inference
    net <- clr.wrap(data)

Example output

Loading required package: grndata

netbenchmark documentation built on April 28, 2020, 7 p.m.