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Analysis of Temporal-spatial Co-variation within Gene Expression Microarray Data in an Organogenesis Model

Identifieur interne : 000F75 ( Main/Exploration ); précédent : 000F74; suivant : 000F76

Analysis of Temporal-spatial Co-variation within Gene Expression Microarray Data in an Organogenesis Model

Auteurs : Martin Ehler [États-Unis] ; Vinodh Rajapakse [États-Unis] ; Barry Zeeberg [États-Unis] ; Brian Brooks [États-Unis] ; Jacob Brown [États-Unis] ; Wojciech Czaja [États-Unis] ; Robert F. Bonner [États-Unis]

Source :

Lecture Notes in Computer Science [ 0302-9743 ]

RBID : ISTEX:CBDD308F95C0CE93BB0231DB1F48541746A287F5

Abstract

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.

Url:

https://api.istex.fr/ark:/67375/HCB-JTP2BK3F-9/fulltext.pdf

DOI: 10.1007/978-3-642-13078-6_6

Affiliations:

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Le document en format XML

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Analysis of Temporal-spatial Co-variation within Gene Expression Microarray Data in an Organogenesis Model

Analysis of Temporal-spatial Co-variation within Gene Expression Microarray Data in an Organogenesis Model

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Abstract

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