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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.
Papers
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
[HIF-1]),
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
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
Implementation
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.
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