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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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DGCA: A comprehensive R package for Differential Gene Correlation Analysis.

Andrew T McKenzie^1,2,3, Igor Katsyv^1,2,3, Won-Min Song^1,2

¹Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.

BMC Systems Biology

|November 17, 2016

Summary

This summary is machine-generated.

Differential Gene Correlation Analysis (DGCA) is a new R package to analyze gene-pair correlations across conditions. It identifies regulatory changes in biological systems and aids in discovering novel pathways and biomarkers.

Keywords:

Breast cancer Differential coexpression Differential correlation Multiscale clustering analysis R package RNA-Seq TP53 Triple negative breast cancer

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Area of Science:

Genomics and Bioinformatics
Systems Biology
Cancer Research

Background:

Understanding gene regulatory relationships is crucial for predictive biological modeling.
Analyzing differential gene correlations across conditions offers insights into biological systems.

Purpose of the Study:

To develop an R package, DGCA (Differential Gene Correlation Analysis), for computing and analyzing differential gene correlations.
To provide tools for systems-level analysis of differential correlations, including average difference and multiscale clustering.

Main Methods:

Developed the DGCA R package implementing permutation testing for empirical p-values.
Utilized a z-score based method for calculating differential correlation.
Applied DGCA to TCGA RNA-seq data for breast cancer analysis.

Main Results:

DGCA's z-score method significantly outperforms existing differential correlation calculation methods.
Identified changes in regulatory relationships of TP53 and PTEN in breast cancer with inactivating mutations.
Discovered an immune-related differential correlation module specific to triple-negative breast cancer (TNBC).

Conclusions:

DGCA is a user-friendly R package for assessing gene-gene regulatory differences across conditions.
Facilitates identification of novel signaling pathways, biomarkers, and therapeutic targets in complex diseases.
Enhances the application of differential correlation analysis in biological studies.