Clustering biological annotations and gene expression data to identify putatively co-regulated biological processes

Corneliu Henegar, Raffaella Cancello, Sophie Rome, Hubert Vidal, Karine Clément, Jean Daniel Zucker

Research output: Contribution to journalArticle

Abstract

Motivation: Functional profiling is a key step of microarray gene expression data analysis. Identifying co-regulated biological processes could help for better understanding of underlying biological interactions within the studied biological frame. Results: We present herein an original approach designed to search for putatively co-regulated biological processes sharing a significant number of co-expressed genes. An R language implementation named "FunCluster" was built and tested on two gene expression data sets. A discriminatory functional analysis of the first data set, related to experiments performed on separated adipocytes and stroma vascular fraction cells of human white adipose tissue, highlighted the prevalent role of nonadipose cells in the synthesis of inflammatory and immunity molecules in human adiposity. On the second data set, resulting from a model investigating insulin coordinated regulation of gene expression in human skeletal muscle, FunCluster analysis spotlighted novel functional classes of putatively co-regulated biological processes related to protein metabolism and the regulation of muscular contraction. Availability: Supplementary information about the FunClust er tool is available on-line at http://corneliu.henegar.info/FunCluster.htm.

Original languageEnglish
Pages (from-to)833-852
Number of pages20
JournalJournal of Bioinformatics and Computational Biology
Volume4
Issue number4
DOIs
Publication statusPublished - Aug 2006

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Keywords

  • Computational biology
  • Functional profiling of gene expression
  • Gene expression pattern analysis

ASJC Scopus subject areas

  • Cell Biology
  • Medicine(all)

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