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Related Experiment Videos

Supervised clustering of genes.

Marcel Dettling1, Peter Bühlmann

  • 1Seminar für Statistik, Eidgenössische Technische Hochschule (ETH) Zürich, 8092 Zürich, Switzerland. dettling@stat.math.ethz.ch

Genome Biology
|January 23, 2003
PubMed
Summary
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This study introduces a new supervised clustering algorithm for gene expression data, effectively identifying gene groups that predict tissue types. The method offers superior predictive potential compared to existing approaches, aiding in diagnostics and functional genomics.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray experiments generate high-dimensional gene expression data.
  • Identifying subsets of genes that determine tissue types (e.g., cancer) is crucial.
  • Existing methods may not fully leverage response variables for gene grouping.

Purpose of the Study:

  • To develop a novel supervised clustering algorithm for gene expression data.
  • To directly incorporate response variables into the gene grouping process.
  • To identify biologically relevant gene clusters for tissue type classification.

Main Methods:

  • A supervised clustering algorithm for genes was developed.
  • The algorithm directly integrates response variables (e.g., cancer type) into gene grouping.

Related Experiment Videos

  • Empirical evaluation on eight public microarray datasets was performed.

    • Main Results:

    • The algorithm successfully identified gene clusters with excellent predictive potential.
    • Performance was often superior to state-of-the-art methods using single genes.
    • Permutation tests and bootstrapping confirmed the stability and reliability of the identified clusters.

    Conclusions:

    • The developed algorithm identifies gene clusters that clearly distinguish tissue types.
    • This approach contrasts with methods like hierarchical clustering.
    • Findings are potentially valuable for medical diagnostics and advancing functional genomics.