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This article is part of the supplement: Selected Proceedings of the 6th International Symposium on Bioinformatics Research and Applications (ISBRA'10)

Open Access Proceedings

Nonlinear gene cluster analysis with labeling for microarray gene expression data in organ development

Martin Ehler12*, Vinodh N Rajapakse2, Barry R Zeeberg3, Brian P Brooks4, Jacob Brown4, Wojciech Czaja2 and Robert F Bonner1

Author Affiliations

1 National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Section on Medical Biophysics, Bethesda MD 20892, USA

2 University of Maryland, Department of Mathematics, Norbert Wiener Center, College Park MD 20742, USA

3 National Institutes of Health, National Cancer Institute, Laboratory of Molecular Pharmacology, Genomics & Bioinformatics Group, Bethesda MD 20892, USA

4 National Institutes of Health, National Eye Institute, Ophthalmic Genetics and Visual Function Branch, Bethesda MD 20892, USA

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BMC Proceedings 2011, 5(Suppl 2):S3  doi:10.1186/1753-6561-5-S2-S3

Published: 28 April 2011

Abstract

Background

The gene networks underlying closure of the optic fissure during vertebrate eye development are not well-understood. We use a novel clustering method based on nonlinear dimension reduction with data labeling to analyze microarray data from laser capture microdissected (LCM) cells at the site and developmental stages (days 10.5 to 12.5) of optic fissure closure.

Results

Our nonlinear methods created clusters of genes that mapped onto more specific biological processes and functions related to eye development as defined by Gene Ontology at lower false discovery rates than conventional linear cluster algorithms. Our new methods build on the advantages of LCM to isolate pure phenotypic populations within complex tissues in order to identify systems biology relationships among critical gene products expressed at lower copy number.

Conclusions

The combination of LCM of embryonic organs, gene expression microarrays, and nonlinear dimension reduction with labeling is a potentially useful approach to extract subtle spatial and temporal co-variations within the gene regulatory networks that specify mammalian organogenesis and organ function. Our results motivate further analysis of nonlinear dimension reduction with labeling within other microarray data sets from LCM dissected tissues or other cell specific samples to determine the more general utility of our method for uncovering more specific biological functional relationships.