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Related Experiment Videos

Statistical estimation of cluster boundaries in gene expression profile data.

K Horimoto1, H Toh

  • 1Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga, Saga 849-8501, Japan. horimoto@post.saga-med.ac.jp

Bioinformatics (Oxford, England)
|December 26, 2001
PubMed
Summary

This study introduces a new statistical method for automatically determining gene clusters from gene expression profiles. The approach uses the variance inflation factor to objectively define cluster boundaries, improving upon traditional methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression profile data are rapidly accumulating due to advances in microarray techniques.
  • Clustering procedures are used to analyze this data and extract gene information.
  • Systematic determination of gene cluster boundaries remains challenging, often relying on visual inspection or biological knowledge.

Purpose of the Study:

  • To propose a statistical procedure for estimating the number of clusters in hierarchical clustering of gene expression profiles.
  • To enable automatic determination of cluster boundaries without additional analyses or prior biological knowledge.

Main Methods:

  • Hierarchical clustering of gene expression profiles.
  • Evaluation of statistical properties at dendrogram nodes using the variance inflation factor from multiple regression analysis.

Related Experiment Videos

  • Automatic determination of cluster boundaries based on statistical evaluation.

    • Main Results:

    • A novel statistical procedure for estimating the number of gene clusters was developed.
    • The method allows for automatic determination of cluster boundaries.
    • The procedure demonstrated promising results on the expression profiles of 2467 yeast genes.

    Conclusions:

    • The proposed statistical procedure offers an objective and automated method for gene cluster boundary determination.
    • This approach can enhance the analysis of large-scale gene expression datasets.
    • The method reduces reliance on subjective visual inspection or domain-specific biological knowledge.