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Unsupervised meta-analysis on diverse gene expression datasets allows insight into gene function and regulation

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Item Type:Article
Title:Unsupervised meta-analysis on diverse gene expression datasets allows insight into gene function and regulation
Creators Name:Engelmann, J.C. and Schwarz, R. and Blenk, S. and Friedrich, T. and Seibel, P.N. and Dandekar, T. and Mueller, T.
Abstract:Over the past years, microarray databases have increased rapidly in size. While they offer a wealth of data, it remains challenging to integrate data arising from different studies. Here we propose an unsupervised approach of a large-scale meta-analysis on Arabidopsis thaliana whole genome expression datasets to gain additional insights into the function and regulation of genes. Applying kernel principal component analysis and hierarchical clustering, we found three major groups of experimental contrasts sharing a common biological trait. Genes associated to two of these clusters are known to play an important role in indole-3-acetic acid (IAA) mediated plant growth and development or pathogen defense. Novel functions could be assigned to genes including a cluster of serine/threonine kinases that carry two uncharacterized domains (DUF26) in their receptor part implicated in host defense. With the approach shown here, hidden interrelations between genes regulated under different conditions can be unraveled.
Keywords:Arabidopsis Thaliana, Microarray, Unsupervised Meta-Analysis, Function Prediction, Database, Gene Expression
Source:Bioinformatics and Biology Insights
ISSN:1177-9322
Publisher:Libertas Academica (New Zealand)
Volume:2
Page Range:265-280
Date:26 May 2008
Official Publication:http://www.la-press.com/unsupervised-meta-analysis-on-diverse-gene-expression-datasets-allows--article-a822-abstract?article_id=822
PubMed:View item in PubMed

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