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      DGCA: A comprehensive R package for Differential Gene Correlation Analysis

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          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.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12918-016-0349-1) contains supplementary material, which is available to authorized users.

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          Most cited references60

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Categorical Data Analysis

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              Mutant p53: one name, many proteins.

              There is now strong evidence that mutation not only abrogates p53 tumor-suppressive functions, but in some instances can also endow mutant proteins with novel activities. Such neomorphic p53 proteins are capable of dramatically altering tumor cell behavior, primarily through their interactions with other cellular proteins and regulation of cancer cell transcriptional programs. Different missense mutations in p53 may confer unique activities and thereby offer insight into the mutagenic events that drive tumor progression. Here we review mechanisms by which mutant p53 exerts its cellular effects, with a particular focus on the burgeoning mutant p53 transcriptome, and discuss the biological and clinical consequences of mutant p53 gain of function.
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                Author and article information

                Contributors
                amckenz@gmail.com
                igor.katsyv@icahn.mssm.edu
                won-min.song@mssm.edu
                minghui.wang@mssm.edu
                212-824-8947 , bin.zhang@mssm.edu
                Journal
                BMC Syst Biol
                BMC Syst Biol
                BMC Systems Biology
                BioMed Central (London )
                1752-0509
                15 November 2016
                15 November 2016
                2016
                : 10
                : 106
                Affiliations
                [1 ]Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
                [2 ]Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
                [3 ]Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
                [4 ]Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
                Article
                349
                10.1186/s12918-016-0349-1
                5111277
                27846853
                b02b52e5-34cb-4ec3-965b-bf653ba18b45
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 23 May 2016
                : 3 November 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: RF1AG054014-01
                Award ID: U01AG052411
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: U01AI111598-01
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: R01CA163772
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Quantitative & Systems biology
                differential correlation,differential coexpression,multiscale clustering analysis,r package,rna-seq,tp53,breast cancer,triple negative breast cancer

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