PCIT: Partial Correlation and Information Theory (PCIT) analysis

In CeTF: Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory 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)

CeTF documentation built on Nov. 25, 2020, 2 a.m.