PCIT {CeTF} | R Documentation

## Partial Correlation and Information Theory (PCIT) analysis

### Description

The PCIT algorithm is used for reconstruction of gene co-expression networks (GCN) that combines the concept partial correlation coefficient with information theory to identify significant gene to gene associations defining edges in the reconstruction of GCN.

### Usage

    PCIT(input, tolType = "mean")

### Arguments

`input` | A correlation matrix.
`tolType` | Type of tolerance (default: 'mean') given the 3 pairwise correlations (see `tolerance`).

### Value

Returns an list with the significant correlations, raw adjacency matrix and significant adjacency matrix.

### References

REVERTER, Antonio; CHAN, Eva KF. Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics, v. 24, n. 21, p. 2491-2497, 2008. https://academic.oup.com/bioinformatics/article/24/21/2491/192682

### Examples

    # loading a simulated normalized data
    data('simNorm')

    # getting the PCIT results for first 30 genes
    results <- PCIT(simNorm[1:30, ])

    # printing PCIT output first 15 rows
    head(results$tab, 15)

[Package CeTF version 1.6.0 Index]