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Area of Science:

  • Statistical methodology
  • Bioinformatics
  • Genomics

Background:

  • Simultaneous control of the true discovery proportion (TDP) is crucial in high-dimensional data analysis.
  • Existing methods for TDP control often lack the flexibility to prioritize specific feature sets based on user expertise.
  • Targeting statistical power towards biologically relevant or pre-specified feature sets remains a challenge.

Purpose of the Study:

  • To propose a novel statistical procedure that allows users to focus statistical power on prespecified 'focus sets'.
  • To enable inference for post hoc selected 'nonfocus sets' by providing a true discovery lower confidence bound.
  • To evaluate the performance of the proposed method through simulations and real-world data application.

Main Methods:

  • Development of a procedure combining partial true discovery guarantee procedures with Holm's procedure.
  • Implementation as a conservative shortcut to the established closed testing procedure.
  • Simulation studies to assess statistical power for focus and nonfocus sets, and for structured sets (trees, DAGs).
  • Comparison with existing methods like AdaFilter for replicability analysis.
  • Application to gene expression data using Gene Ontology enrichment analysis.

Main Results:

  • The proposed method demonstrates relatively high statistical power for user-defined 'focus sets'.
  • Power for 'nonfocus sets' is reduced, as intended, to enhance power for focus sets.
  • The procedure provides a lower confidence bound for true discoveries in 'nonfocus sets' via interpolation.
  • Performance is investigated for various set structures, including trees and directed acyclic graphs.
  • The method shows utility in replicability analysis and gene expression data analysis.

Conclusions:

  • The proposed procedure offers a flexible approach to true discovery proportion control, allowing targeted power allocation.
  • It provides a practical solution for researchers aiming to prioritize specific hypotheses or feature sets in their analyses.
  • The method is a valuable tool for enhancing the interpretability and focus of findings in complex biological data.