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Discovering local structure in gene expression data: the order-preserving submatrix problem.

Amir Ben-Dor1, Benny Chor, Richard Karp

  • 1Agilent Laboratories, 12741 NE 39th Street, Bellevue, WA 98005, USA. amir_ben-dor@agilent.com

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|August 26, 2003
PubMed
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This study introduces a novel method for finding local patterns in gene expression data by identifying order-preserving submatrices (OPSMs). This approach effectively uncovers hidden biological relationships within specific gene and experiment subsets.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression matrices are crucial for understanding biological processes.
  • Existing pattern discovery methods often rely on global comparisons of genes or experiments.
  • These global approaches may overlook localized biological signals.

Purpose of the Study:

  • To develop a method for discovering local patterns in gene expression data.
  • To identify order-preserving submatrices (OPSMs) within gene expression matrices.
  • To address the limitations of global pattern discovery techniques.

Main Methods:

  • Focusing on subsets of genes (G) and experiments (T) simultaneously.
  • Defining and searching for order-preserving submatrices (OPSMs).

Related Experiment Videos

  • Developing a probabilistic model and an efficient algorithm for OPSM detection.

    • Main Results:

    • The OPSM search problem is NP-hard in the worst case.
    • The developed algorithm successfully recovers hidden OPSMs in simulated data.
    • Application to breast cancer data revealed significant local patterns.

    Conclusions:

    • The proposed method effectively identifies localized patterns in gene expression data.
    • OPSMs represent a valuable tool for uncovering specific biological relationships.
    • This approach holds promise for analyzing complex biological processes and disease progression.