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Bioinformatics Advance Access originally published online on March 4, 2009
Bioinformatics 2009 25(9):1152-1157; doi:10.1093/bioinformatics/btp123
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets

Ashok Sharma 1, Robert Podolsky 1,2, Jieping Zhao 1 and Richard A. McIndoe 1,3,*

1Center for Biotechnology and Genomic Medicine, 2Department of Medicine and 3Department of Pathology, Medical College of Georgia, Augusta, GA, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: As the number of publically available microarray experiments increases, the ability to analyze extremely large datasets across multiple experiments becomes critical. There is a requirement to develop algorithms which are fast and can cluster extremely large datasets without affecting the cluster quality. Clustering is an unsupervised exploratory technique applied to microarray data to find similar data structures or expression patterns. Because of the high input/output costs involved and large distance matrices calculated, most of the algomerative clustering algorithms fail on large datasets (30 000 + genes/200 + arrays). In this article, we propose a new two-stage algorithm which partitions the high-dimensional space associated with microarray data using hyperplanes. The first stage is based on the Balanced Iterative Reducing and Clustering using Hierarchies algorithm with the second stage being a conventional k-means clustering technique. This algorithm has been implemented in a software tool (HPCluster) designed to cluster gene expression data. We compared the clustering results using the two-stage hyperplane algorithm with the conventional k-means algorithm from other available programs. Because, the first stage traverses the data in a single scan, the performance and speed increases substantially. The data reduction accomplished in the first stage of the algorithm reduces the memory requirements allowing us to cluster 44 460 genes without failure and significantly decreases the time to complete when compared with popular k-means programs. The software was written in C# (.NET 1.1).

Availability: The program is freely available and can be downloaded from http://www.amdcc.org/bioinformatics/bioinformatics.aspx.

Contact: rmcindoe{at}mail.mcg.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: David Rocke

Received on October 7, 2008; revised on January 27, 2009; accepted on February 28, 2009

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