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Related Experiment Videos

Gene ordering in partitive clustering using microarray expressions.

Shubhra Sankar Ray1, Sanghamitra Bandyopadhyay, Sankar K Pal

  • 1Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata 700 108, India. shubhra_r@isical.ac.in

Journal of Biosciences
|October 5, 2007
PubMed
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This study introduces a novel hybrid method for ordering genes within clusters, improving gene expression analysis. The approach enhances partitive clustering by identifying subclusters and grouping functionally related genes more effectively.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data analysis relies on identifying genes with similar patterns.
  • Gene ordering is crucial in hierarchical clustering but unexplored in partitive clustering.
  • Partitive clustering methods lack robust gene ordering strategies.

Purpose of the Study:

  • To develop and evaluate a novel hybrid method for gene ordering within partitive clustering.
  • To demonstrate the importance of gene ordering in partitive clustering frameworks.
  • To improve the quality of gene expression data analysis through enhanced clustering.

Main Methods:

  • A hybrid approach combining Traveling Salesman Problem (TSP) algorithms (FRAG_GALK, Concorde) with Self-Organizing Maps (SOM).

Related Experiment Videos

  • Application of the hybrid method to microarray gene expression data.
  • Validation using yeast and fibroblast datasets.

    • Main Results:

    • The hybrid method improved partitive clustering results by identifying subclusters and grouping functionally correlated genes.
    • Demonstrated minimization of gene expression distances and maximization of biological gene ordering via MIPS categorization.
    • Achieved comparable or superior biological gene order with reduced computation time compared to hierarchical clustering.

    Conclusions:

    • Gene ordering significantly enhances partitive clustering for gene expression analysis.
    • The proposed hybrid method offers an effective and efficient approach for gene ordering.
    • This work opens new avenues for exploring gene ordering in partitive clustering frameworks.