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DGCA specifications


Unique identifier OMICS_13629
Alternative name Differential Gene Correlation Analysis
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data A matrix of gene expression values, a design matrix specifying conditions associated with samples, and a specification of the conditions for comparison
Output data Differentially correlated gene pairs for visualization, gene ontology (GO) enrichment, and/or network construction
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.0.1
Stability Stable
WGCNA, matrixStats, methods
Maintained Yes




No version available



  • person_outline Bin Zhang

Publication for Differential Gene Correlation Analysis

DGCA citations


Commensal microbiota modulate gene expression in the skin

PMCID: 5789709
PMID: 29378633
DOI: 10.1186/s40168-018-0404-9

[…] Gene correlation analysis was performed on all 2820 DEGs with the DGCA R package, using default parameters unless otherwise specified []. Initially, genes were filtered for low central tendency, retaining only genes with average expression levels in the 75th percent […]


Transcriptome wide analysis of natural antisense transcripts shows their potential role in breast cancer

Sci Rep
PMCID: 5727077
PMID: 29234122
DOI: 10.1038/s41598-017-17811-2

[…] DiffCor list: Differential correlation analysis between pairs of protein-coding and antisense transcripts was performed using DGCA software (v. 1.0.1). Pairs of protein coding/antisense genes were selected for which the correlation significantly differed between normal and tumor samples (adjusted p-value < 0.05) and for whic […]


Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer’s disease

Mol Neurodegener
PMCID: 5674813
PMID: 29110684
DOI: 10.1186/s13024-017-0219-3

[…] et, we selected the genes as having an increasing or decreasing trend in expression across AD severity stages and an ANOVA p-value <0.05 as the HIPP AD DEG signature. We used the moduleGO function in DGCA [] (version 1.0.1) to perform gene ontology (GO) enrichment analysis on these DEG sets, which leverages the GOstats (version 2.34) [] and org.Hs.eg.db GO annotation (version 3.1.2) R packages. We […]


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DGCA institution(s)
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
DGCA funding source(s)
This work was supported by the grants F30AG052261 and R01AG046170 from the NIH/National Institute on Aging (NIA), R01CA163772 from NIH/National Cancer Institute (NCI), and U01AI111598-01 from NIH/National Institute of Allergy and Infectious Diseases (NIAID).

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