KiWi: A scalable subspace clustering algorithm for gene expression analysis

Obi L. Griffith, Byron J. Gao, Mikhail Bilenky, Yuliya Prychyna, Martin Ester, Steven J.M. Jones

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a patternbased subspace cluster, is a subset of rows and columns in a data matrix for which all the rows induce the same linear ordering of columns. Existing OPSM discovery methods do not scale well to increasingly large expression datasets. In particular, twig clusters having few genes and many experiments incur explosive computational costs and are completely pruned off by existing methods. However, it is of particular interest to determine small groups of genes that are tightly coregulated across many conditions. In this paper, we present KiWi, an OPSM subspace clustering algorithm that is scalable to massive datasets, capable of discovering twig clusters and identifying negative as well as positive correlations. We extensively validate KiWi using relevant biological datasets and show that KiWi correctly assigns redundant probes to the same cluster, groups experiments with common clinical annotations, differentiates real promoter sequences from negative control sequences, and shows good association with cis-regulatory motif predictions.

Original languageEnglish
Title of host publication3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
DOIs
StatePublished - Dec 1 2009
Externally publishedYes
Event3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 - Beijing, China
Duration: Jun 11 2009Jun 13 2009

Publication series

Name3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

Conference

Conference3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
CountryChina
CityBeijing
Period06/11/0906/13/09

Keywords

  • Biclustering
  • Gene expression analysis
  • KiWi
  • OPSM
  • Pattern-based cluster
  • Subspace clustering
  • Twig cluster

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  • Cite this

    Griffith, O. L., Gao, B. J., Bilenky, M., Prychyna, Y., Ester, M., & Jones, S. J. M. (2009). KiWi: A scalable subspace clustering algorithm for gene expression analysis. In 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 [5163005] (3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009). https://doi.org/10.1109/ICBBE.2009.5163005