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Alignment-based versus variation-based transformation methods for clustering microarray time-series data

Published: 02 August 2010 Publication History

Abstract

It has been recently shown that traditional clustering methods do not necessarily perform well on time-series data, because of the temporal relationships involved in such data. This makes it a particularly difficult problem. In this paper, we propose to use spectral clustering approaches for clustering microarray time-series data. The approaches are based on two transformations that have been recently introduced, especially for gene expression time-series data, namely, alignment-based and variation-based transformations. Both transformations have been devised in order to take into account temporal relationships in the data, and have been shown to increase the ability of a clustering method in detecting co-expressed genes. We investigate the performances of these transformations methods, when combined with spectral clustering on two microarray time-series datasets, and discuss their strengths and weaknesses. Our experiments on two well known real-life datasets show the superiority of the alignment-based over the variation-based transformation for finding meaningful groups of co-expressed genes.

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  1. Alignment-based versus variation-based transformation methods for clustering microarray time-series data

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      cover image ACM Conferences
      BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
      August 2010
      705 pages
      ISBN:9781450304382
      DOI:10.1145/1854776
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      Published: 02 August 2010

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      Author Tags

      1. microarray time-series
      2. multiple alignment
      3. spectral clustering
      4. variation vector

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