ArticleBMC systems biology2016
DGCA: A comprehensive R package for Differential Gene Correlation Analysis.
Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 157 papers, 2 of them syntheses that pooled it.
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Who cites it
157 citing papers in PubMed, 2 syntheses or guidelines pooled it, 262 citations in OpenAlex.
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- iProDNet: integrated probabilistic differential network inference under heterogeneous biological conditions.Briefings in bioinformatics · 2026Article
- Gene network analysis predicts the primary regulators of ABA-dependent transcriptional activation and repression in Populus roots.PLoS genetics · 2026Article
- Molecular Networks and Key Regulators Underlying Resilience of the Human Brain to Aging and Dementia.Biomolecules · 2026Article
- Integrative Analysis of Abiotic Stress-Responsive Genes in Soybean Using Differential Gene Expression and Validation With Machine Learning.Plant-environment interactions (Hoboken, N.J.) · 2026Article
- Sexual Dimorphism in c-Fos Networks Governing Aggression.bioRxiv : the preprint server for biology · 2026Article
- Continuous low-intensity ultrasound influences the transcriptomic profile in M1 macrophages by downregulating inflammation and promoting M2-like markers.Scientific reports · 2026Article
- Transcriptional remodeling of human skeletal muscle following sleep restriction in postmenopausal women.Physiological genomics · 2026Article
- Mosaic: Single-Cell Atlas of Stress.Cells · 2026Review
- Thalamocortical functional connectivity alterations and peripheral immune transcriptomic dysregulation in major depressive disorder patients with suicidal ideation.Translational psychiatry · 2026Article
- Exploring differential gene correlations inBiotechnology reports (Amsterdam, Netherlands) · 2026Article
- DiCoLo: Integration-free and cluster-free detection of localized differential gene co-expression in single-cell data.bioRxiv : the preprint server for biology · 2026Article
- NOVAGene: a nonlinear approach to improve biological resolution in weighted gene co-expression networks for low-variability datasets.Frontiers in bioinformatics · 2026Article
- DiDyNet: a robust framework for differential dynamic network inference from longitudinal multi-omics data.Bioinformatics advances · 2026Article
- A powerful framework for differential co-expression analysis of general risk factors.Bioinformatics (Oxford, England) · 2025Article
97 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundDissecting 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.
resultsIn 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).
conclusionsDGCA 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.