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

Summary: 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 compleme...

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Detalles Bibliográficos
Autores principales: Liu, Bao-Hong, Yu, Hui, Tu, Kang, Li, Chun, Li, Yi-Xue, Li, Yuan-Yuan
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2951087/
https://www.ncbi.nlm.nih.gov/pubmed/20801914
http://dx.doi.org/10.1093/bioinformatics/btq471
Descripción
Sumario:Summary: 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. Contact: yyli@scbit.org and yxli@scbit.org Supplementary information: Supplementary data are available at Bioinformatics online.