DGCA: A comprehensive R package for Differential Gene Correlation Analysis

Andrew T. McKenzie, Igor Katsyv, Won Min Song, Minghui Wang, Bin Zhang

Research output: Contribution to journalArticlepeer-review

136 Scopus citations

Abstract

Background: Dissecting the regulatory relationships between genes is a critical step towards building accurate predictive models of biological systems. A powerful approach towards this end is to systematically study the differences in correlation between gene pairs in more than one distinct condition. Results: In this study we develop an R package, DGCA (for Differential Gene Correlation Analysis), which offers a suite of tools for computing and analyzing differential correlations between gene pairs across multiple conditions. To minimize parametric assumptions, DGCA computes empirical p-values via permutation testing. To understand differential correlations at a systems level, DGCA performs higher-order analyses such as measuring the average difference in correlation and multiscale clustering analysis of differential correlation networks. Through a simulation study, we show that the straightforward z-score based method that DGCA employs significantly outperforms the existing alternative methods for calculating differential correlation. Application of DGCA to the TCGA RNA-seq data in breast cancer not only identifies key changes in the regulatory relationships between TP53 and PTEN and their target genes in the presence of inactivating mutations, but also reveals an immune-related differential correlation module that is specific to triple negative breast cancer (TNBC). Conclusions: DGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions. This user-friendly, effective, and comprehensive software tool will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases.

Original languageEnglish
Article number106
JournalBMC Systems Biology
Volume10
Issue number1
DOIs
StatePublished - 15 Nov 2016

Keywords

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

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