PCIT: Partial Correlation and Information Theory (PCIT) analysis

In cbiagii/pcitRif: Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis

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)

cbiagii/pcitRif documentation built on Feb. 5, 2023, 9:03 p.m.