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

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 kinas

SUBMITTER: Engelmann JC 

PROVIDER: S-EPMC2735942 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature