FunCluster Algorithm



"FunCluster" is a genomic data analysis algorithm which performs functional analysis of  gene expression data obtained from cDNA microarray experiments. Besides automated functional annotation of gene expression data, FunCluster functional analysis aims to detect co-regulated biological processes through a specially designed clustering procedure involving biological annotations and gene expression data. FunCluster's functional analysis relies on Gene Ontology and KEGG annotations and is currently available for three organisms: Homo Sapiens, Mus Musculus and Saccharomyces Cerevisiae.






Clustering biological annotations and  gene expression data to identify

putatively co-regulated biological processes


Henegar C, Cancello R, Rome S, Vidal H, Clement K, Zucker JD


JBCB. 2006 Aug;4(4):833-52.


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.


Supplementary results to those presented within our JBCB paper are available below:


JBCB Supplementary Data




Reduction of macrophage infiltration and chemo-attractant gene expression changes in white adipose tissue of morbidly obese subjects after surgery-induced weight loss


Cancello R, Henegar C, Viguerie N, Taleb S, Poitou C, Rouault C, Coupaye M, Pelloux V,

Hugol D, Bouillot JL, Bouloumié A, Barbatelli G, Cinti S, Svensson PA, Barsh GS,

Zucker JD, Basdevant A, Langin D, Clément K


Diabetes. 2005 Aug;54(8):2277-86.


In human obesity, the stroma vascular fraction (SVF) of white adipose tissue (WAT) is enriched in macrophages. These cells may contribute to low-grade inflammation and to its metabolic complications. Little is known about the effect of weight loss on macrophages and genes involved in macrophage attraction. We examined subcutaneous WAT (scWAT) of 7 lean and 17 morbidly obese subjects before and 3 months after bypass surgery. Immunomorphological changes of the number of scWAT-infiltrating macrophages were evaluated, along with concomitant changes in expression of SVF-overexpressed genes. The number of scWAT-infiltrating macrophages before surgery was higher in obese than in lean subjects (HAM56+/CD68+; 22.6 ± 4.3 vs. 1.4 ± 0.6%, P <0.001). Typical "crowns" of macrophages were observed around adipocytes. Drastic weight loss resulted in a significant decrease in macrophage number (–11.63 ± 2.3%, P <0.001), and remaining macrophages stained positive for the anti-inflammatory protein interleukin 10. Genes involved in macrophage attraction (monocyte chemotactic protein [MCP]-1, plasminogen activator urokinase receptor [PLAUR], and colony-stimulating factor [CSF]-3) and hypoxia (hypoxia-inducible factor-1{alpha} [HIF-1{alpha}]), expression of which increases in obesity and decreases after surgery, were predominantly expressed in the SVF. We show that improvement of the inflammatory profile after weight loss is related to a reduced number of macrophages in scWAT. MCP-1, PLAUR, CSF-3, and HIF-1{alpha} may play roles in the attraction of macrophages in scWAT.


Supplementary results to those presented within our Diabetes paper, obtained through a discriminatory functional analysis of the adipose tissue data set containing differentially expressed genes specific to mature human adipocytes or stroma vascular fraction (SVF) cells, together with a list of significant "inflammatory" genes predominantly expressed within SVF cells, are available below:


Diabetes Supplementary Data






An R package containing an implementation of FunCluster algorithm (current version 1.07), together with the latest (October 2007) Gene Ontology and KEGG annotations for Homo Sapiens, Mus Musculus and Saccharomyces Cerevisiae, are available as ZIP (Windows) or TGZ (Linux) archives, as well as directly from CRAN repositories. Please see R man pages provided with this package on how to use FunCluster.




Last updated: 23.10.2007

© 2003 - 2007 by Corneliu Henegar

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