netbenchmark (version 1.4.2)

pcit.wrap: pcit wrapper function

Description

Default wrapper function for the pcit network inference algorithm

Usage

pcit.wrap(data)

Arguments

data

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

Value

• pcit.wrap returns a matrix which is the weighted adjacency matrix of the network inferred by pcit algorithm.

Details

The Partial Correlation coefficient with Information Theory (PCIT) algorithm, combines the concept of partial correlation coefficient with information theory to identify significant gene-to-gene associations.

For every trio of genes in $X_i$, $X_j$ and $X_l$, the three first-order partial correlation coefficients are computed. These coefficients indicate the strength of the linear relationship between $X_i$ and $X_j$ that is uncorrelated with $X_l$, being therefore a measure of conditional independence. Then, the average ratio of partial to direct correlation is computed in order to obtain the tolerance level to be used as the local threshold for eliminating non-significant associations.

References

Reverter, Antonio, and Eva KF Chan. "Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks." Bioinformatics 24.21 (2008): 2491-2497.

See Also

netbenchmark, evaluate, pcit

Examples

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