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Related Experiment Videos

Combining p-values in large-scale genomics experiments.

Dmitri V Zaykin1, Lev A Zhivotovsky, Wendy Czika

  • 1National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA. zaykind@niehs.nih.gov

Pharmaceutical Statistics
|September 20, 2007
PubMed
Summary
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This study compares p-value combination methods for large-scale genomics. The truncated product method (TPM) allows stronger claims about specific subsets of true associations (TAs) compared to global tests.

Area of Science:

  • Genomics
  • Statistical genetics
  • Bioinformatics

Background:

  • Large-scale genomics studies involve numerous statistical tests.
  • Controlling false discoveries is crucial in these experiments.
  • Traditional methods like individual adjustments can be insufficient.

Purpose of the Study:

  • To evaluate p-value combination methods for identifying true associations (TAs) in large-scale genomics.
  • To compare the power and claim strength of different methods.
  • To investigate methods allowing claims about subsets of p-values.

Main Methods:

  • Exploration of the inverse gamma distribution's shape parameter in p-value combination (Gamma method - GM).
  • Investigation of the rank truncated product method (RTP).

Related Experiment Videos

  • Analysis of the truncated product method (TPM) with explicit truncation.

    • Main Results:

    • The Gamma method (GM) offers improved power over Fisher's method for large L and moderate TAs.
    • GM, RTP, and Fisher's methods provide global tests for the presence of TAs.
    • TPM allows for claims about specific subsets of p-values, unlike GM and RTP.

    Conclusions:

    • TPM enables stronger claims about subsets of p-values.
    • GM provides higher statistical power compared to TPM, RTP, Fisher, and Simes methods in simulations.
    • The choice of method depends on whether a global claim or a subset-specific claim is desired.