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Related Experiment Videos

Statistical significance for genomewide studies.

John D Storey1, Robert Tibshirani

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

Proceedings of the National Academy of Sciences of the United States of America
|July 29, 2003
PubMed
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This study introduces a new statistical method using false discovery rates to analyze large genomic datasets. This approach, called the q value, balances true and false positives for more reliable significance testing in genomewide experiments.

Area of Science:

  • Genomics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genomewide experiments generate vast datasets requiring robust statistical analysis.
  • Testing thousands of features against null hypotheses often yields numerous significant results, complicating interpretation.
  • Existing methods may be too stringent, missing true discoveries, or too liberal, leading to false positives.

Purpose of the Study:

  • To propose a novel statistical approach for measuring significance in genomewide studies.
  • To introduce the q value as a measure of statistical significance based on the false discovery rate.
  • To offer a more balanced and interpretable alternative to traditional p values for large-scale genomic data analysis.

Main Methods:

  • Development of a statistical framework based on the false discovery rate (FDR).

Related Experiment Videos

  • Association of a q value with each tested feature, analogous to the p value.
  • Calibration of the method to balance true and false positive rates.

    • Main Results:

    • The proposed q value provides a measure of significance directly related to the false discovery rate.
    • This approach offers a sensible balance between true positives and false positives.
    • The q value method is more liberal than traditional criteria used in genome scans for linkage, reducing false positives without sacrificing discoveries.

    Conclusions:

    • The q value offers a reliable and interpretable measure of statistical significance for genomewide studies.
    • This method effectively manages the trade-off between false positives and true positives in large datasets.
    • The q value approach enhances the analysis of large-scale genomic data, improving the identification of significant features.