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Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes.

Susmita Datta1, Somnath Datta

  • 1Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA. susmita.datta@louisville.edu

BMC Bioinformatics
|September 2, 2006
PubMed
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This study introduces two new metrics, biological homogeneity index (BHI) and biological stability index (BSI), to evaluate gene clustering algorithms. These indices help identify the best clustering methods for gene expression data analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cluster analysis is a common first step in analyzing gene expression profiles.
  • Post hoc analysis often assesses functional correlation of clustered genes, but results can be misleading.
  • Systematic evaluation of unsupervised clustering is needed due to potential unrelated genes within clusters.

Purpose of the Study:

  • To quantify the performance of unsupervised clustering algorithms in producing biologically meaningful gene clusters.
  • To introduce novel performance measures for evaluating clustering algorithms.
  • To compare the effectiveness of different clustering algorithms on gene expression data.

Main Methods:

  • Introduced two performance measures: Biological Homogeneity Index (BHI) and Biological Stability Index (BSI).

Related Experiment Videos

  • Evaluated ten clustering algorithms on two gene expression datasets (breast cancer SAGE profiles and yeast sporulation).
  • Utilized functional classes from prior knowledge and gene ontology databases for evaluation.

    • Main Results:

    • BHI measures the biological homogeneity within clusters.
    • BSI measures the consistency of producing biologically meaningful clusters across similar datasets.
    • Identified optimal clustering algorithms for both evaluated datasets based on BHI and BSI.

    Conclusions:

    • Functional information from gene ontology databases can systematically evaluate unsupervised clustering results.
    • This evaluation aids in selecting the most appropriate clustering algorithm for specific gene expression datasets.
    • The developed indices provide a robust framework for assessing the biological relevance of gene clusters.