Journal
BIOINFORMATICS
Volume 26, Issue 20, Pages 2637-2638Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btq471
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Funding
- Shanghai Institutes for Biological Sciences
- Chinese Academy of Sciences [2008KIP207]
- National '973' Basic Research Program [2006CB0D1203, 2006CB0D1205]
- National Natural Science Foundation of China [30770497, 31000380]
- National Key Technologies RD Program [2007AA02Z331, 2009ZX10603]
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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.
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