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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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A multiple testing method for hypotheses structured in a directed acyclic graph.

Rosa J Meijer1, Jelle J Goeman

  • 1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Postzone S5-P, P.O. Box 9604, 2300 RC Leiden, The Netherlands.

Biometrical Journal. Biometrische Zeitschrift
|November 14, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new top-down multiple testing method for hypotheses structured in a directed acyclic graph (DAG). It controls the familywise error rate and enhances variable importance testing, with applications in gene set analysis.

Keywords:
Directed acyclic graphsFWER controlGene OntologyMultiple testing

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Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • Multiple testing procedures are crucial for analyzing complex biological data.
  • Existing methods often struggle with hypotheses structured in directed acyclic graphs (DAGs).
  • Gene set analysis requires simultaneous testing of gene sets and individual genes.

Purpose of the Study:

  • To develop a novel multiple testing method for DAG-structured hypotheses.
  • To generalize existing procedures for enhanced flexibility in hypothesis testing.
  • To enable simultaneous testing of gene sets and individual genes in association studies.

Main Methods:

  • A top-down multiple testing procedure is proposed.
  • The method strongly controls the familywise error rate.
  • It generalizes Meinshausen's procedure for tree-structured hypotheses, allowing for multiple parents and overlapping hypotheses.

Main Results:

  • The method provides a flexible approach to testing variable importance.
  • It facilitates the simultaneous assessment of gene sets and individual genes within a DAG framework.
  • Application to Gene Ontology terms in a survival setting demonstrates its utility.

Conclusions:

  • The novel method offers a powerful tool for multiple testing in DAG-structured hypotheses.
  • It is particularly well-suited for gene set analysis, integrating gene-level and set-level testing.
  • The R package 'cherry' implements this new procedure.