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Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments.

M K Kerr1, G A Churchill

  • 1The Jackson Laboratory, Bar Harbor, ME 04609, USA.

Proceedings of the National Academy of Sciences of the United States of America
|July 27, 2001
PubMed
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This study presents a novel statistical method using bootstrapping to validate gene expression clustering. This technique enhances the reliability of microarray data analysis and biological interpretation.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression microarray data analysis often involves clustering to identify patterns.
  • Assessing the statistical reliability of these clustering results is crucial for accurate biological interpretation.
  • Existing methods may not sufficiently address the stability of cluster analysis outcomes.

Purpose of the Study:

  • To introduce a general statistical inference technique for gene expression clustering.
  • To assess the stability and reliability of clustering results using bootstrapping.
  • To provide a framework for robust analysis of microarray data.

Main Methods:

  • Utilized an analysis of variance (ANOVA) model for normalization and differential gene expression estimation.

Related Experiment Videos

  • Applied bootstrapping, a randomization technique, to evaluate the stability of cluster analysis.
  • Demonstrated the technique using a publicly available gene expression dataset.

    • Main Results:

    • The bootstrapping approach provides a quantitative measure of clustering stability.
    • Results indicate that experimental replication is essential for reliable bootstrapping.
    • The method allows drawing conclusions about the trustworthiness of clustering outcomes.

    Conclusions:

    • The proposed bootstrapping technique enhances statistical inference from gene expression clustering.
    • Emphasizes the importance of experimental design and replication in microarray studies.
    • Offers a reliable method for validating patterns discovered in gene expression data.