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Evaluation of clustering algorithms for gene expression data.

Susmita Datta1, Somnath Datta

  • 1Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA. susmita.datta@louisville.edu

BMC Bioinformatics
|January 16, 2007
PubMed
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This study introduces novel validation measures to assess clustering algorithms for gene expression data. These measures help select the optimal algorithm for grouping genes by function based on their expression profiles.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Cluster analysis is crucial for high-dimensional data, particularly in grouping genes by expression profiles.
  • Selecting the optimal clustering algorithm from numerous available methods presents a significant challenge.

Purpose of the Study:

  • To propose and evaluate new validation measures for assessing clustering algorithm performance.
  • To aid in selecting the most suitable clustering algorithm for gene expression data analysis.

Main Methods:

  • Developed two validation measures, each with components for statistical consistency (stability) and biological functional congruence.
  • Applied these measures to evaluate six well-known clustering algorithms (UPGMA, K-Means, Diana, Fanny, Model-Based, SOM).

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Main Results:

  • Smaller values of the proposed validation indices indicate superior clustering algorithm performance.
  • Case studies using breast cancer SAGE data and yeast time-course microarray data demonstrated the utility of the validation measures.

Conclusions:

  • No single clustering algorithm is universally optimal for all gene expression datasets.
  • The proposed validation measures facilitate informed selection of clustering algorithms tailored to specific datasets for functional gene grouping.