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Related Experiment Videos

Handling multiple testing while interpreting microarrays with the Gene Ontology Database.

Michael V Osier1, Hongyu Zhao, Kei-Hoi Cheung

  • 1Department of Biological Sciences, Rochester Institute of Technology, 85 Lomb Memorial Drive, Rochester, NY 14623, USA. michael@bioinformatics.rit.edu

BMC Bioinformatics
|September 8, 2004
PubMed
Summary
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Correcting for multiple statistical tests in microarray analysis is challenging. This study found gene set distributions are unpredictable, suggesting permutation-based simulations are needed for reliable results.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Software tools for analyzing microarray data alongside genetic knowledgebases are under development.
  • A key challenge is correcting for multiple statistical testing, as simple methods are too conservative and complex ones are impractical.

Purpose of the Study:

  • To investigate the distribution of gene sets from various biological databases to inform the development of better statistical testing corrections.
  • To explore computationally efficient methods for multiple testing correction in microarray data analysis.

Main Methods:

  • A preliminary study was conducted on gene sets from Drosophila, S. cerevisiae, Wormbase, and Gramene databases.
  • Analysis utilized the Gene Ontology Database to examine the distribution of gene sets.

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Main Results:

  • The study found that the estimated distribution of gene sets is irregular and unpredictable.
  • The distribution's unpredictability extends beyond specific gene sets analyzed.

Conclusions:

  • The findings indicate that standard statistical corrections may not be universally applicable for microarray data analysis.
  • Permutation-based simulations are likely necessary to accurately determine the confidence and reliability of results from such analyses.