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
收藏
页码：2637 / 2638
页数：2
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