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Related Experiment Videos

Nonparametric bayesian estimation of positive false discovery rates.

Yongqiang Tang1, Subhashis Ghosal, Anindya Roy

  • 1Department of Psychiatry, SUNY Health Science Center, Brooklyn, New York 11203, USA.

Biometrics
|May 16, 2007
PubMed
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We introduce a Dirichlet process mixture model (DPMM) for analyzing P-value distributions in multiple hypothesis testing. This approach estimates key parameters and improves the positive false discovery rate estimation, as shown in leukemia data analysis.

Area of Science:

  • Statistical genetics
  • Bioinformatics
  • Computational biology

Background:

  • Multiple hypothesis testing is common in genomics.
  • Accurate P-value distribution modeling is crucial for reliable inference.
  • Existing methods may not fully capture complex P-value distributions.

Purpose of the Study:

  • To develop a flexible Dirichlet process mixture model (DPMM) for P-value distributions.
  • To estimate the proportion of true null hypotheses and rejection probabilities.
  • To introduce a novel positive false discovery rate (pFDR) estimator.

Main Methods:

  • Developed a DPMM for P-value distribution.
  • Employed a Markov chain Monte Carlo (MCMC) algorithm for posterior computation.
  • Proposed and evaluated a new pFDR estimator through simulations.

Related Experiment Videos

Main Results:

  • The DPMM effectively models P-value distributions.
  • Posterior estimates for true null proportion and rejection probability were obtained.
  • The proposed pFDR estimator demonstrated good performance in simulations.
  • Methodology successfully applied to a leukemia dataset.

Conclusions:

  • DPMM offers a robust framework for multiple testing.
  • The new pFDR estimator enhances statistical inference accuracy.
  • The approach is applicable to real-world biological data analysis.