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The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray

John D Storey1, James Y Dai, Jeffrey T Leek

  • 1Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. jstorey@u.washington.edu

Biostatistics (Oxford, England)
|August 25, 2006
PubMed
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This study introduces an optimal discovery procedure (ODP) for analyzing high-dimensional biological data, improving gene expression analysis in microarrays. The new method significantly increases the number of significant genes identified compared to existing approaches.

Area of Science:

  • Genetics and Systems Biology
  • Bioinformatics and Computational Biology

Background:

  • High-dimensional biological studies require accurate signal extraction from complex datasets.
  • Identifying differentially expressed genes in comparative microarray experiments is a key challenge.

Purpose of the Study:

  • To propose and evaluate a new approach for optimal hypothesis testing in high-dimensional studies.
  • To develop a generally applicable estimate of the Optimal Discovery Procedure (ODP) for microarray data analysis.

Main Methods:

  • Estimating the Optimal Discovery Procedure (ODP) using information from the entire dataset for each feature tested.
  • Comparing the ODP approach against five existing methods for identifying differentially expressed genes.

Main Results:

Related Experiment Videos

  • The proposed ODP method demonstrates superior performance over existing methods in microarray experiments.
  • Significant increases (72%–185%) in identified genes were observed at a 3% false discovery rate in breast cancer tumor type comparisons.

Conclusions:

  • The ODP offers an optimal strategy for multiple significance testing in high-dimensional biological data.
  • The developed microarray method provides a more powerful tool for gene expression analysis and is available via the EDGE software package.