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

Ehler M, Rajapakse VN, Zeeberg BR, Brooks BP, Brown J, Czaja W, Bonner RF - BMC Proc (2011)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Section on Medical Biophysics, Bethesda MD 20892, USA. ehlermar@mail.nih.gov.

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.

No MeSH data available.


connectivity network for WGCNA weights Portion of the thresholded weighted correlation network derived from WGCNA. In the entire connectivity network, each of the genes to be labeled (Cdh4, Dll1, Tox, Onecut2, Dcc, Epha5, Cadps) would have more than 16 connections, see also Figure 11.
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Figure 12: connectivity network for WGCNA weights Portion of the thresholded weighted correlation network derived from WGCNA. In the entire connectivity network, each of the genes to be labeled (Cdh4, Dll1, Tox, Onecut2, Dcc, Epha5, Cadps) would have more than 16 connections, see also Figure 11.

Mentions: CIM Schroedinger Eigenmaps I Seven highly connected genes from Figures 11 and 12 were labeled in Schroedinger Eigenmaps. After clustering, all seven labeled genes are contained in the same cluster with 145 other genes. The cluster is enriched for categories (eye morphogenesis, eye development) that are more specific to eye development than the results without labeling suggesting that data-dependent gene labeling can increase the biological specificity.


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

Ehler M, Rajapakse VN, Zeeberg BR, Brooks BP, Brown J, Czaja W, Bonner RF - BMC Proc (2011)

connectivity network for WGCNA weights Portion of the thresholded weighted correlation network derived from WGCNA. In the entire connectivity network, each of the genes to be labeled (Cdh4, Dll1, Tox, Onecut2, Dcc, Epha5, Cadps) would have more than 16 connections, see also Figure 11.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3090761&req=5

Figure 12: connectivity network for WGCNA weights Portion of the thresholded weighted correlation network derived from WGCNA. In the entire connectivity network, each of the genes to be labeled (Cdh4, Dll1, Tox, Onecut2, Dcc, Epha5, Cadps) would have more than 16 connections, see also Figure 11.
Mentions: CIM Schroedinger Eigenmaps I Seven highly connected genes from Figures 11 and 12 were labeled in Schroedinger Eigenmaps. After clustering, all seven labeled genes are contained in the same cluster with 145 other genes. The cluster is enriched for categories (eye morphogenesis, eye development) that are more specific to eye development than the results without labeling suggesting that data-dependent gene labeling can increase the biological specificity.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Section on Medical Biophysics, Bethesda MD 20892, USA. ehlermar@mail.nih.gov.

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.

No MeSH data available.