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Published on November 4, 2009; 10.1104/pp.109.145318

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Received July 24, 2009
Accepted November 2, 2009

Assembly of an Interactive Correlation Network for the Arabidopsis Genome Using a Novel Heuristic Clustering Algorithm

Marek Mutwil , Bjorn Usadel , Moritz Schutte , Ann Loraine , Oliver Ebenhoh , and Staffan Persson *

Max-Planck-Institute for Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam, Germany; Department of Bioinformatics and Genomics, North Carolina Research Campus, University of North Carolina at Charlotte, 203 Oak Avenue, Kannapolis, NC 28081, USA

* Corresponding author; email: Persson{at}mpimp-golm.mpg.de.

A vital quest in biology is comprehensible visualization and interpretation of correlation relationships on a genome scale. Such relationships may be represented in the form of networks, which usually require disassembly into smaller manageable units, or clusters, to facilitate interpretation. Several graph clustering algorithms that may be used to visualize biological networks are available. However, only some of these support weighted edges, and none provide good control of cluster sizes, which is crucial for comprehensible visualization of large networks. We constructed an interactive co-expression network for the Arabidopsis genome using a novel Heuristic Cluster Chiseling Algorithm (HCCA) that supports weighted edges, and that may control average cluster sizes. Comparative clustering analyses demonstrated that the HCCA performed as well as, or better than, both the commonly used Markov, MCODE, and k-means clustering algorithms. We mapped MapMan ontology terms onto co-expressed node vicinities of the network, which revealed transcriptional organization of previously unrelated cellular processes. We further explored the predictive power of this network through mutant analyses, and identified six new genes that are essential to plant growth. We show that the HCCA partitioned network constitutes an ideal "cartographic" platform for visualization of correlation networks. This approach rapidly provides network partitions with relative uniform cluster sizes on a genome-scale level, and may thus be used for correlation network lay-outs also for other species.






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