Journal article
Phenotypical Enrichment Strategies for Microarray Data Analysis Applied in a Type II Diabetes Study
Omics (Larchmont, N.Y.), v 9(3)
Sep 2005
PMID: 16209639
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Combining results from gene microarrays, clinical chemistry, and quantitative tissue histomorphology in an integrated bioinformatics setting enables prioritization of gene families as well as individual genes in a type II diabetes animal study. This new methodology takes advantage of a time-controlled mouse study as the animals progress from a normal phenotype to that of type II diabetes. Profiles from different levels of the biological hierarchy of unpooled entities provide an encompassing, system-wide view of biological changes. Here, phenotypic changes on the tissue-structural and physiological level are used as statistical covariants to enrich the gene expression analysis, suggesting correlative processes between gene expression and phenotype unlocked by multi-sample comparisons. We apply correlative and gene set enrichment procedures and compare the results to differential analysis to identify molecular markers. Evaluation based on ontological classifications proves changes in prioritization of disease-related genes that would have been overlooked by conventional gene expression analyses strategies.
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Details
- Title
- Phenotypical Enrichment Strategies for Microarray Data Analysis Applied in a Type II Diabetes Study
- Creators
- Keith Boyce - Immune Tolerance NetworkAndres Kriete - Drexel UniversitySheila Nagatomi - Icoria Inc (United States, Pittsburgh)Bruce Kelder - Ohio UniversityKaren Coschigano - Ohio UniversityJohn J Kopchick - Ohio University
- Publication Details
- Omics (Larchmont, N.Y.), v 9(3)
- Publisher
- Mary Ann Liebert
- Number of pages
- 15
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000232649500005
- Scopus ID
- 2-s2.0-26844572598
- Other Identifier
- 991014878170504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biotechnology & Applied Microbiology
- Genetics & Heredity