DGCA: A comprehensive R package for Differential Gene Correlation Analysis.
DGCA: A comprehensive R package for Differential Gene Correlation Analysis.
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DOI：
10.1186/s12918-016-0349-1
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发表时间：
2016-11-15
影响因子：
--
通讯作者：
Zhang B
中科院分区：
文献类型：
--
作者：
McKenzie AT;Katsyv I;Song WM;Wang M;Zhang B
剖析基因之间的调控关系是建立准确的生物系统预测模型的关键一步。实现这一目标的一个强有力的方法是系统地研究在一个以上的不同条件下基因对之间相关性的差异。在这项研究中，我们开发了一个R软件包，DGCA（差分基因相关性分析），它提供了一套工具，用于计算和分析在多种条件下基因对之间的差分相关性。为了最小化参数假设，DGCA通过排列检验计算经验p值。为了在系统级别上理解差分相关性，DGCA执行高阶分析，例如测量相关性的平均差异和差分相关网络的多尺度聚类分析。通过模拟研究，我们表明，直接的z分数为基础的方法，DGCA采用显着优于现有的替代方法计算差分相关。DGCA在乳腺癌TCGA RNA-seq数据中的应用不仅确定了在存在失活突变的情况下TP 53和PTEN及其靶基因之间调控关系的关键变化，而且还揭示了特异于三阴性乳腺癌（TNBC）的免疫相关差异相关模块。DGCA是一个R软件包，用于系统评估不同条件下基因-基因调控关系的差异。这一用户友好、有效和全面的软件工具将极大地促进微分相关分析在许多生物学研究中的应用，从而有助于在复杂的生物系统和疾病中识别新的信号通路、生物标志物和靶标。本文的在线版本（doi：10.1186/s12918-016-0349-1）包含补充材料，可供授权用户使用。
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. 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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影响因子：
12.3
作者：
Auffray C;Chen Z;Hood L
通讯作者：
Hood L
影响因子：
8
作者：
Bandyopadhyay, S;Pai, SK;Watabe, K
通讯作者：
Watabe, K
影响因子：
2.7
作者：
FIELLER, EC;HARTLEY, HO;PEARSON, ES
通讯作者：
PEARSON, ES
影响因子：
11.2
作者：
Divekar SD;Storchan GB;Sperle K;Veselik DJ;Johnson E;Dakshanamurthy S;Lajiminmuhip YN;Nakles RE;Huang L;Martin MB
通讯作者：
Martin MB
DOI：
10.1093/bioinformatics/btp502
发表时间：
2009-11-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Choi Y;Kendziorski C
通讯作者：
Kendziorski C