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A method for clustering gene expression data based on graph structure.

Shigeto Seno1, Reiji Teramoto, Yoichi Takenaka

  • 1Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan. s-senoo@ist.osaka-u.ac.jp

Genome Informatics. International Conference on Genome Informatics
|February 12, 2005
PubMed
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This study introduces a novel clustering method, p-quasi complete linkage clustering, to improve gene expression analysis. It effectively reduces noise sensitivity compared to traditional hierarchical clustering for gene function discovery.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data analysis is crucial for understanding gene function.
  • DNA microarrays and oligonucleotide arrays generate extensive gene expression profiles.
  • Hierarchical clustering, using Pearson correlation, is a common but noise-sensitive method for gene clustering.

Purpose of the Study:

  • To develop a more robust gene clustering method for gene expression profiles.
  • To address the limitations of existing methods, particularly their sensitivity to experimental noise.
  • To improve the accuracy of gene function analysis from expression data.

Main Methods:

  • Proposed a novel clustering algorithm: p-quasi complete linkage clustering.
  • Applied the new method to gene expression datasets from yeast cell-cycles and human lung cancer.

Related Experiment Videos

  • Compared the performance of the proposed method against traditional clustering techniques.

    • Main Results:

    • The p-quasi complete linkage clustering method demonstrated improved performance in handling noisy gene expression data.
    • The method effectively clustered genes based on their expression profiles, offering a more reliable analysis.
    • Comparative analysis showed the proposed method's superiority over existing techniques in specific applications.

    Conclusions:

    • The p-quasi complete linkage clustering is an effective alternative for analyzing gene expression data.
    • This method offers enhanced robustness against experimental noise, leading to more accurate gene function insights.
    • The findings support the utility of this novel approach in genomic research and disease studies.