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


BACKGROUND
yyli@scbit.org and yxli@scbit.org


BACKGROUND
Supplementary data are available at Bioinformatics online.
Liu
Bao-Hong
BH
School of Life Science and Technology, Tongji University, Shanghai 200092, P R China.
Yu
Hui
H
Tu
Kang
K
Li
Chun
C
Li
Yi-Xue
YX
Li
Yuan-Yuan
YY
D000465:Algorithms; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D012984:Software
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