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Related Experiment Videos

Effects of dependence in high-dimensional multiple testing problems.

Kyung In Kim1, Mark A van de Wiel

  • 1Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands. k.i.kim@tue.nl

BMC Bioinformatics
|February 27, 2008
PubMed
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Dependence among high-dimensional variables impacts False Discovery Rate (FDR) control. The adaptive Benjamini-Hochberg procedure is most robust, while SAM and q-value methods inadequately control FDR under dependence.

Area of Science:

  • High-dimensional statistics
  • Statistical genetics
  • Bioinformatics

Background:

  • Dependence structures in high-dimensional data complicate multiple hypothesis testing.
  • Existing simulation studies often use oversimplified correlation structures.
  • Real-world data exhibits complex network features impacting statistical inference.

Purpose of the Study:

  • To systematically investigate the effects of network features (sparsity, correlation strength) on False Discovery Rate (FDR) control procedures.
  • To evaluate the robustness of popular FDR methods under various dependence structures.
  • To introduce a novel simulation method for dependent data with imposed network constraints.

Main Methods:

  • Simulation study using random correlation matrices to impose dependence structures.

Related Experiment Videos

  • Evaluation of FDR control procedures including Benjamini-Hochberg, Storey's q-value, SAM, and resampling methods.
  • Comparison of False Non-discovery Rates and estimates of null hypotheses.

    • Main Results:

    • SAM and q-value methods demonstrate inadequate FDR control under dependence.
    • The adaptive Benjamini-Hochberg procedure shows robustness and conservativeness.
    • Estimates of true null hypotheses vary significantly across dependence conditions.

    Conclusions:

    • A new guided simulation method for dependent data with conditional independence structures is presented.
    • The simulation framework facilitates structural analysis of dependency effects on multiple testing.
    • The approach is valuable for testing new methods for pi0 or FDR estimation in dependent contexts.