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Shrunken p-values for assessing differential expression with applications to genomic data analysis.

Debashis Ghosh1

  • 1Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan 48109-2029, USA. ghoshd@umich.edu

Biometrics
|December 13, 2006
PubMed
Summary

This study introduces shrunken p-values for assessing differential expression (SPADE), an alternative to false discovery rate control. SPADE offers a novel decision-theoretic approach for multiple testing adjustments and controlling false positives in high-throughput studies.

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • High-throughput technologies generate vast amounts of data, necessitating statistical methods to handle numerous hypotheses.
  • The multiple testing problem is a critical challenge in analyzing such data, with the false discovery rate (FDR) being a common approach.
  • Existing methods may not fully leverage decision-theoretic principles for robust inference.

Purpose of the Study:

  • To introduce and evaluate shrunken p-values for assessing differential expression (SPADE) as an alternative to traditional multiple testing adjustments.
  • To develop a novel method for multiple testing correction based on decision theory.
  • To provide a framework for controlling false positive results using a decision-theoretic approach.

Main Methods:

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  • Development of shrunken p-value estimators motivated by decision theory.
  • Formulation of a new multiple testing adjustment procedure based on these estimators.
  • Derivation of a decision rule for controlling the number of false positives.
  • Illustration through simulation studies and analysis of real-world gene expression data.

Main Results:

  • SPADE provides a novel approach to multiple testing adjustment.
  • The decision-theoretic framework enables effective control of false positive results.
  • Simulations demonstrate the performance of the proposed methodology.
  • Application to prostate cancer data showcases practical utility.

Conclusions:

  • Shrunken p-values for assessing differential expression (SPADE) offer a promising alternative for multiple testing in high-throughput studies.
  • The decision-theoretic foundation provides a rigorous basis for statistical inference and error control.
  • SPADE is a valuable tool for analyzing gene expression data and other high-dimensional biological datasets.