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Model-based clustering and data transformations for gene expression data.

K Y Yeung1, C Fraley, A Murua

  • 1Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA. kayee@cs.washington.edu

Bioinformatics (Oxford, England)
|October 24, 2001
PubMed
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Model-based clustering, using Gaussian mixture models, effectively analyzes gene expression data. This approach outperforms heuristic methods by accurately identifying the number of clusters and appropriate models, even with data transformations.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Clustering is vital for exploratory analysis of gene expression data.
  • Heuristic algorithms are common, but probability model-based clustering offers a principled alternative.
  • Gaussian mixture models are powerful for clustering applications.

Purpose of the Study:

  • To benchmark model-based clustering against heuristic methods for gene expression data.
  • To evaluate the performance of Gaussian mixture models in identifying clusters and their number.
  • To assess the validity of Gaussian mixture assumptions on transformed gene expression data.

Main Methods:

  • Benchmarking model-based clustering using Gaussian mixture models.
  • Utilizing synthetic and real gene expression datasets with external evaluation criteria.

Related Experiment Videos

  • Assessing data fit to multivariate Gaussian distributions before and after transformations.

    • Main Results:

    • Model-based clustering demonstrated superior performance on synthetic data, correctly identifying models and cluster numbers.
    • On real data, it yielded comparable cluster quality to heuristic methods, with added benefits of model and cluster number suggestion.
    • Data transformations can lead to reasonable fits for Gaussian mixture models on gene expression data.

    Conclusions:

    • Model-based clustering, particularly with Gaussian mixture models, is a robust and informative approach for gene expression analysis.
    • It provides a principled framework for model selection and determining the optimal number of clusters.
    • The method offers advantages over heuristic approaches, especially when dealing with complex data structures.