Analysis of temporal-spatial co-variation within gene expression microarray data in an organogenesis model

Authors:
Martin Ehler;Vinodh Rajapakse;Barry Zeeberg;Brian Brooks;Jacob Brown;Wojciech Czaja;Robert F. Bonner
Affiliations:
Eunice Kennedy Shriver National Institute of Child Health and Human Development, Section on Medical Biophysics, National Institutes of Health, Bethesda, MD;Department of Mathematics, Norbert Wiener Center, University of Maryland, College Park, MD;National Cancer Institute, Laboratory of Molecular Pharmacology, Genomics & Bioinformatics Group, National Institutes of Health, Bethesda, MD;National Eye Institute, Ophthalmic Genetics and Visual Function Branch, National Institutes of Health, Bethesda, MD;National Eye Institute, Ophthalmic Genetics and Visual Function Branch, National Institutes of Health, Bethesda, MD;Department of Mathematics, Norbert Wiener Center, University of Maryland, College Park, MD;Eunice Kennedy Shriver National Institute of Child Health and Human Development, Section on Medical Biophysics, National Institutes of Health, Bethesda, MD
Venue:
ISBRA'10 Proceedings of the 6th international conference on Bioinformatics Research and Applications
Year:
2010

Citing 2
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Abstract

The gene networks underlying closure of the optic fissure during vertebrate eye development are poorly understood. We used a novel clustering method based on Laplacian Eigenmaps, a nonlinear dimension reduction method, 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. Our new method provided greater biological specificity than classical clustering algorithms in terms of identifying more biological processes and functions related to eye development as defined by Gene Ontology at lower false discovery rates. This new methodology builds on the advantages of LCM to isolate pure phenotypic populations within complex tissues and allows improved ability to identify critical gene products expressed at lower copy number. The combination of LCM of embryonic organs, gene expression microarrays, and extracting spatial and temporal co-variations appear to be a powerful approach to understanding the gene regulatory networks that specify mammalian organogenesis.