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Related Experiment Videos

Cluster analysis and data visualization of large-scale gene expression data

G S Michaels1, D B Carr, M Askenazi

  • 1Institute for Computational Sciences and Informatics, George Mason University, Fairfax, VA 22030, USA. gmichael@osf1.gmu.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|August 11, 1998
PubMed
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Analyzing gene expression data from time course experiments is crucial for new gene discovery. This study introduces a strategy using cluster analysis and visualization to find correlated gene expression patterns, aiding in pathway and control process identification.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Gene discovery necessitates understanding expression context.
  • Rapid advancements in cDNA and genomic sequencing enable high-throughput gene expression screening.
  • Analyzing large-scale gene expression data is essential for biological insight.

Purpose of the Study:

  • To present a strategy for analyzing large-scale quantitative gene expression measurement data.
  • To leverage time course experiments for gene expression analysis.
  • To facilitate the discovery of new genes and their regulatory mechanisms.

Main Methods:

  • Utilizing cluster analysis for gene expression data.
  • Employing graphical visualization methods for time series data.

Related Experiment Videos

  • Developing a strategy for analyzing quantitative gene expression measurements.

    • Main Results:

    • Identified correlated patterns in gene expression from time series data.
    • Demonstrated the effectiveness of cluster analysis and visualization for large-scale data.
    • Revealed coherence in expression patterns suggesting shared biological processes.

    Conclusions:

    • The proposed strategy aids in understanding gene function and regulation.
    • Correlated gene expression patterns can indicate shared pathways and control mechanisms.
    • Experimental verification of identified patterns is feasible and valuable for gene discovery.