title: DGCA: A comprehensive R package for Differential Gene Correlation Analysis.
abstract: 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.
  
  
  
  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).
  
  
  
  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.
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.|Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.|Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
authors.forename: Andrew T
authors.initials: AT
authors.lastname: McKenzie
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.|Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.|Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
authors.forename: Igor
authors.initials: I
authors.lastname: Katsyv
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.|Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
authors.forename: Won-Min
authors.initials: WM
authors.lastname: Song
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.|Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
authors.forename: Minghui
authors.initials: M
authors.lastname: Wang
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA. bin.zhang@mssm.edu.|Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA. bin.zhang@mssm.edu.|Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY, 10029, USA. bin.zhang@mssm.edu.
authors.forename: Bin
authors.initials: B
authors.lastname: Zhang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D009154:Mutation; D051059:PTEN Phosphohydrolase / Q000235:genetics / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism; D012984:Software; D064726:Triple Negative Breast Neoplasms / Q000235:genetics* / Q000378:metabolism; D016159:Tumor Suppressor Protein p53 / Q000235:genetics / Q000378:metabolism
chemical_list: D011960:Receptors, Estrogen; D016159:Tumor Suppressor Protein p53; D051059:PTEN Phosphohydrolase
keywords: Breast cancer; Differential coexpression; Differential correlation; Multiscale clustering analysis; R package; RNA-Seq; TP53; Triple negative breast cancer