RIF: Regulatory Impact Factors (RIF) analysis

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

Regulatory Impact Factors (RIF) analysis

Description

The RIF algorithm identify critical transcript factors (TF) from gene expression data.

Usage

RIF(input, nta = NULL, ntf = NULL, nSamples1 = NULL, nSamples2 = NULL)

Arguments

input	A matrix of expression with differentially expressed genes and transcript factors in rows, and the samples in columns.
nta	Number of Differentially Expressed (DE) genes.
ntf	Number of Transcription Factors (TFs).
nSamples1	Number of samples of condition 1.
nSamples2	Number of samples of condition 2.

Details

The input matrix must have the rows and columns ordered by the following request:

1. rows: DE genes followed by TFs;

2. columns: samples of condition1 followed by samples of condition2.

Value

Returns an dataframe with the regulatory impact factors metric for each transcript factor.

References

REVERTER, Antonio et al. Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data. Bioinformatics, v. 26, n. 7, p. 896-904, 2010. https://academic.oup.com/bioinformatics/article/26/7/896/212064

Examples

# load RIF input example
data('RIF_input')

# performing RIF analysis
RIF_out <- RIF(input = RIF_input,
               nta = 104,
               ntf = 50,
               nSamples1 = 10,
               nSamples2 = 10)

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