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Statistical Applications in Genetics and Molecular Biology

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Volume 12, Issue 5 (Oct 2013)

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Identifying clusters in genomics data by recursive partitioning

Gro Nilsen
  • Biomedical Informatics, Department of Informatics, University of Oslo, Norway; and Centre for Cancer Biomedicine, University of Oslo, Norway
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Ørnulf Borgan / Knut LiestØl
  • Biomedical Informatics, Department of Informatics, University of Oslo, Norway; and Centre for Cancer Biomedicine, University of Oslo, Norway
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Ole Christian Lingjærde
  • Corresponding author
  • Biomedical Informatics, Department of Informatics, University of Oslo, Norway; and Centre for Cancer Biomedicine, University of Oslo, Norway
  • K.G. Jebsen Centre for Breast Cancer Research, Oslo University Hospital, Oslo, Norway
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2013-08-13 | DOI: https://doi.org/10.1515/sagmb-2013-0016

Abstract

Genomics studies frequently involve clustering of molecular data to identify groups, but common clustering methods such as K-means clustering and hierarchical clustering do not determine the number of clusters. Methods for estimating the number of clusters typically focus on identifying the global structure in the data, however the discovery of substructures within clusters may also be of great biological interest. We propose a novel method, Partitioning Algorithm based on Recursive Thresholding (PART), that recursively uncovers distinct subgroups in the groups already identified. Outliers are common in high-dimensional genomics data and may mask the presence of substructure within a cluster. A crucial feature of the algorithm is the introduction of tentative splits of clusters to isolate outliers that might otherwise halt the recursion prematurely. The method is demonstrated on simulated as well as a wide range of real data sets from gene expression microarrays, where the correct clusters were known in advance. When subclusters are present and the variance is large or varies between the clusters, the proposed method performs better than two established global methods on simulated data. On the real data sets the overall performance of PART is superior to the global methods when used in combination with hierarchical clustering. The method is implemented in the R package clusterGenomics and is freely available from CRAN (The Comprehensive R Archive Network).

This article offers supplementary material which is provided at the end of the article.

Keyword:: cluster analysis; gene expression; genomics; recursion; subclusters

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About the article

Corresponding author: Ole Christian Lingjærde, Biomedical Informatics, Department of Informatics, University of Oslo, Postboks 1080 Blindern, 0316 Oslo, Norway; Centre for Cancer Biomedicine, University of Oslo, Norway; and K.G. Jebsen Centre for Breast Cancer Research, Oslo University Hospital, Oslo, Norway, e-mail:


Published Online: 2013-08-13

Published in Print: 2013-10-01


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2013-0016.

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