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  3. Two-stage Multiple Test Procedures Controlling False Discovery Rate With Auxiliary Variable And Their Application To Set4 Δ $\delta$ Mutant Data.
  1. Home
  3. Two-stage Multiple Test Procedures Controlling False Discovery Rate With Auxiliary Variable And Their Application To Set4 Δ $\delta$ Mutant Data.

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Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions
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Two-Stage Multiple Test Procedures Controlling False Discovery Rate With Auxiliary Variable and Their Application to

Seohwa Hwang1, Mark Louie Ramos2, DoHwan Park3

  • 1Department of Statistics, Seoul National University, Seoul, Republic of Korea.

Biometrical Journal. Biometrische Zeitschrift
|May 28, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces new methods for multiple hypotheses testing using auxiliary variables to control the false discovery rate (FDR). These approaches improve statistical power and gene selection in complex biological data analysis.

Keywords:
copulafalse discovery ratemultiple testingtwo‐stage procedure

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Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • Multiple hypotheses testing is crucial in analyzing large datasets.
  • Controlling the false discovery rate (FDR) is essential for reliable results.
  • Traditional methods may lack power when dealing with complex dependencies.

Purpose of the Study:

  • To develop novel methodologies for multiple hypotheses testing using auxiliary variables.
  • To enhance statistical power and control the FDR effectively.
  • To provide a framework for improved gene selection in genomic studies.

Main Methods:

  • Incorporation of auxiliary variables into multiple hypotheses testing frameworks.
  • Development of two distinct approaches based on the role of auxiliary variables.
  • Utilizing the copula method to model the dependence between primary and auxiliary variables.
  • Deriving joint distributions from marginal distributions to understand variable relationships.

    • Main Results:

    • The proposed methodologies effectively control the FDR.
    • The new methods demonstrate greater statistical power compared to traditional approaches.
    • Numerical studies confirm the efficacy of the proposed techniques.
    • Application to Set4 Δ mutant data set shows potential for improved gene selection.

    Conclusions:

    • Auxiliary variables offer a powerful tool for enhancing multiple hypotheses testing.
    • The proposed methods provide a statistically robust and more powerful alternative to existing techniques.
    • This approach has significant implications for gene selection and other complex data analyses.