netbenchmark (version 1.4.2)

clr.wrap: CLR wrapper function

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.

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.

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 $X_i$ and gene $X_j$ is defined as follows $z_{ij}=\sqrt{z^2_i+z^2_j}$, where: $$z_i=max \left( 0,\frac{I(X_i;X_j)-\mu_i}{\sigma_i} \right)$$ $\mu_i$ and $\sigma_i$ are respectively the mean and standard deviation of the empirical distribution of the mutual information between both genes.

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)