DCGL: an R package for identifying differentially coexpressed genes and links from gene expression microarray data

被引：79

作者：

Liu, Bao-Hong ^{[1
,2
,3
]}

Yu, Hui ^{[2
,3
]}

Tu, Kang ^{[4
]}

Li, Chun ^{[5
]}

Li, Yi-Xue ^{[1
,2
,3
]}

Li, Yuan-Yuan ^{[2
,3
]}

机构：

[1] Tongji Univ, Sch Life Sci & Technol, Shanghai 200092, Peoples R China

[2] Chinese Acad Sci, Shanghai Inst Biol Sci, Bioinformat Ctr, Key Lab Syst Biol, Shanghai 200031, Peoples R China

[3] Shanghai Ctr Bioinformat Technol, Shanghai 200235, Peoples R China

[4] NHLBI, NIH, Bethesda, MD USA

[5] Vanderbilt Univ, Sch Med, Dept Biostat, Ctr Human Genet Res, Nashville, TN 37232 USA

来源：

BIOINFORMATICS | 2010年 / 26卷 / 20期

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1093/bioinformatics/btq471

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Gene coexpression analysis was developed to explore gene interconnection at the expression level from a systems perspective, and differential coexpression analysis (DCEA), which examines the change in gene expression correlation between two conditions, was accordingly designed as a complementary technique to traditional differential expression analysis (DEA). Since there is a shortage of DCEA tools, we implemented in an R package 'DCGL' five DCEA methods for identification of differentially coexpressed genes and differentially coexpressed links, including three currently popular methods and two novel algorithms described in a companion paper. DCGL can serve as an easy-to-use tool to facilitate differential coexpression analyses.

引用

页码：2637 / 2638

页数：2

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