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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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              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

                Journal
                BMC Systems Biology
                BMC Syst Biol
                Springer Science and Business Media LLC
                1752-0509
                December 2016
                November 15 2016
                December 2016
                : 10
                : 1
                Article
                10.1186/s12918-016-0349-1
                b02b52e5-34cb-4ec3-965b-bf653ba18b45
                © 2016

                http://creativecommons.org/licenses/by/4.0/

                http://creativecommons.org/licenses/by/4.0/

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