## A Two-Step Clustering for 3-D Gene Expression Data Reveals theMain Features of the Arabidopsis Stress Response

2007
##### Authors
Supper, Jochen
Spieth, Christian
Wanke, Dierk
Kilian, Joachim
Harter, Klaus
Zell, Andreas
Journal article
##### Published in
Journal of Integrative Bioinformatics ; 4 (2007), 1. - 54
##### Abstract
We developed an integrative approach for discovering gene modules, i.e. genes that are tightly correlated under several experimental conditions and applied it to a threedimensional Arabidopsis thaliana microarray dataset. The dataset consists of approximately 23000 genes responding to 9 abiotic stress conditions at 6-9 different points in time. Our approach aims at finding relatively small and dense modules lending themselves to a specific biological interpretation. In order to detect gene modules within this dataset, we employ a two-step clustering process. In the first step, a k-means clustering on one condition is performed, which is subsequently used in the second step as a seed for the clustering of the remaining conditions. To validate the significance of the obtained modules, we performed a permutation analysis and determined a null hypothesis to compare the module scores against, providing a p-value for each module. Significant modules were mapped to the Gene Ontology (GO) in order to determine the participating biological processes.
As a result, we isolated modules showing high significance with respect to the p-values obtained by permutation analysis and GO mapping. In these modules we identified a number of genes that are either part of a general stress response with similar characteristics under different conditions (coherent modules), or part of a more specific stress response to a single stress condition (single response modules). We also found genes clustering within several conditions, which are, however, not part of a coherent module. These genes have a distinct temporal response under each condition. We call the modules they are contained in individual response modules (IR).
##### Subject (DDC)
570 Biosciences, Biology
##### Cite This
ISO 690STRAUCH, Martin, Jochen SUPPER, Christian SPIETH, Dierk WANKE, Joachim KILIAN, Klaus HARTER, Andreas ZELL, 2007. A Two-Step Clustering for 3-D Gene Expression Data Reveals theMain Features of the Arabidopsis Stress Response. In: Journal of Integrative Bioinformatics. 4(1), 54. Available under: doi: 10.2390/biecoll-jib-2007-54
BibTex
@article{Strauch2007TwoSt-8734,
year={2007},
doi={10.2390/biecoll-jib-2007-54},
title={A Two-Step Clustering for 3-D Gene Expression Data Reveals theMain Features of the Arabidopsis Stress Response},
number={1},
volume={4},
journal={Journal of Integrative Bioinformatics},
author={Strauch, Martin and Supper, Jochen and Spieth, Christian and Wanke, Dierk and Kilian, Joachim and Harter, Klaus and Zell, Andreas},
note={Article Number: 54}
}

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<dcterms:abstract xml:lang="eng">We developed an integrative approach for discovering gene modules, i.e. genes that are tightly correlated under several experimental conditions and applied it to a threedimensional Arabidopsis thaliana microarray dataset. The dataset consists of approximately 23000 genes responding to 9 abiotic stress conditions at 6-9 different points in time. Our approach aims at finding relatively small and dense modules lending themselves to a specific biological interpretation. In order to detect gene modules within this dataset, we employ a two-step clustering process. In the first step, a k-means clustering on one condition is performed, which is subsequently used in the second step as a seed for the clustering of the remaining conditions. To validate the significance of the obtained modules, we performed a permutation analysis and determined a null hypothesis to compare the module scores against, providing a p-value for each module. Significant modules were mapped to the Gene Ontology (GO) in order to determine the participating biological processes.&lt;br /&gt;As a result, we isolated modules showing high significance with respect to the p-values obtained by permutation analysis and GO mapping. In these modules we identified a number of genes that are either part of a general stress response with similar characteristics under different conditions (coherent modules), or part of a more specific stress response to a single stress condition (single response modules). We also found genes clustering within several conditions, which are, however, not part of a coherent module. These genes have a distinct temporal response under each condition. We call the modules they are contained in individual response modules (IR).</dcterms:abstract>
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