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The Beta-Binomial SGoF method for multiple dependent tests.

Jacobo de Uña-Alvarez¹

¹University of Vigo.

Statistical Applications in Genetics and Molecular Biology

|May 23, 2012

Summary

A new Beta-Binomial SGoF (BB-SGoF) method corrects the SGoF procedure for dependent tests. This powerful approach maintains statistical power even with complex data dependencies.

Area of Science:

Biostatistics
Bioinformatics
Statistical Genetics

Background:

The Significance of GoF (SGoF) method is a powerful tool for multiple hypothesis testing.
Dependency between tests can reduce the effectiveness of standard multiple testing procedures.
A need exists for robust methods that account for test dependencies in high-throughput biological data.

Purpose of the Study:

Introduce a corrected SGoF method, Beta-Binomial SGoF (BB-SGoF), to handle dependent tests.
Establish the theoretical properties and practical implementation of BB-SGoF.
Evaluate the performance and power of BB-SGoF in analyzing biological data.

Main Methods:

Development of the BB-SGoF method based on the beta-binomial model.
Theoretical analysis of the properties of the BB-SGoF procedure.

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Application of BB-SGoF to real gene/protein expression datasets.

Simulation studies to assess the performance of BB-SGoF.

Main Results:

The BB-SGoF method effectively corrects for dependencies among multiple tests.
BB-SGoF demonstrates significant statistical power, comparable to or exceeding other methods in dependent scenarios.
Analysis of real datasets highlights the practical utility of BB-SGoF in gene expression studies.
Simulations confirm the robustness and power of BB-SGoF under various dependency structures.

Conclusions:

The BB-SGoF method provides a statistically sound approach for multiple testing with dependent data.
The SGoF strategy retains substantial power even when tests are not independent.
BB-SGoF is a valuable tool for analyzing complex biological data, particularly in genomics and proteomics.