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BMCBI
2004

Graph-based iterative Group Analysis enhances microarray interpretation

9 years 3 months ago
Graph-based iterative Group Analysis enhances microarray interpretation
Background: One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically important associations between the differentially expressed genes. A large part of the relevant functional evidence can be represented in the form of graphs, e.g. metabolic and signaling pathways, protein interaction maps, shared GeneOntology annotations, or literature co-citation relations. Such graphs are easily constructed from available genome annotation data. The problem of biological interpretation can then be described as identifying the subgraphs showing the most significant patterns of gene expression. We applied a graph-based extension of our iterative Group Analysis (iGA) approach to obtain a statistically rigorous identification of the subgraphs of interest in any evidence graph. Results: We validated the Graph-based iterative Group Analysis (GiGA) by applying it to the classic yeast diauxic shift expe...
Rainer Breitling, Anna Amtmann, Pawel Herzyk
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2004
Where BMCBI
Authors Rainer Breitling, Anna Amtmann, Pawel Herzyk
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