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Cluster analysis and display of genome-wide expression patterns

M B Eisen1, P T Spellman, P O Brown

  • 1Department of Genetics, Stanford University School of Medicine, 300 Pasteur Avenue, Stanford, CA 94305, USA.

Proceedings of the National Academy of Sciences of the United States of America
|December 9, 1998
PubMed
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This study introduces a cluster analysis system for gene expression data, grouping genes by expression patterns. This method aids in identifying gene functions and understanding cellular processes from genome-wide experiments.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • DNA microarrays generate vast amounts of genome-wide gene expression data.
  • Interpreting these complex datasets to understand gene function and cellular processes is challenging.
  • Standard statistical methods are needed to analyze and visualize gene expression patterns.

Purpose of the Study:

  • To develop a cluster analysis system for genome-wide gene expression data.
  • To visualize gene clustering and expression patterns intuitively for biologists.
  • To aid in the functional annotation of genes, especially novel or poorly characterized ones.

Main Methods:

  • Utilized standard statistical algorithms for cluster analysis.
  • Applied the system to genome-wide expression data from DNA microarray hybridizations.

Related Experiment Videos

  • Developed a graphical output to display clustering and expression data simultaneously.

    • Main Results:

    • The cluster analysis system successfully grouped genes with similar expression patterns.
    • In budding yeast (Saccharomyces cerevisiae), genes of known similar function were efficiently clustered together.
    • A similar tendency was observed in human gene expression data.
    • Coexpression patterns provided insights into the functions of novel genes.

    Conclusions:

    • Genome-wide expression patterns can indicate the status of cellular processes.
    • This clustering approach offers a straightforward method for inferring the functions of uncharacterized genes.
    • The system provides a valuable tool for biologists analyzing complex genomic data.