pcit.wrap: pcit wrapper function

In netbenchmark: Benchmarking of several gene network inference methods

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.

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.

Value

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

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

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