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A Bayesian model to identify multiple expression patterns with simultaneous FDR control for a multi-factor RNA-seq

Yuanyuan Bian1, Chong He2, Jing Qiu3

  • 1Eli Lily and Company, Indianapolis, IN, USA.

Statistical Applications in Genetics and Molecular Biology
|April 21, 2023
PubMed
Summary
This summary is machine-generated.

A new Bayesian model accurately identifies gene expression patterns, controlling false discovery rates (FDRs) for differential and equivalent expression. This method outperforms traditional approaches, especially for complex patterns, and offers better power.

Keywords:
Bayesian modelequivalent expressiongroup specific FDRmultiple gene expression patternsnonlocal prior

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Identifying genes with specific expression patterns across conditions (tissues, genotypes) is crucial in biological research.
  • Current methods using separate differential expression analyses can yield false positives, particularly for patterns involving equivalent expression.

Purpose of the Study:

  • To introduce and evaluate a Bayesian partition model for identifying genes with desired expression patterns.
  • To simultaneously control false discovery rates (FDRs) for all identified gene patterns.
  • To compare the performance of the Bayesian model against common practices for controlling FDRs.

Main Methods:

  • Application of a Bayesian partition model to analyze gene expression data.
  • Simulation studies to assess the model's performance in controlling group-specific FDRs.
  • Comparison of the Bayesian method with traditional intersection-based approaches for identifying differentially expressed (DE) and non-DE genes.

Main Results:

  • The Bayesian partition model effectively controls group-specific FDRs for patterns involving both differential and equivalent expression.
  • Traditional methods fail to control FDRs for patterns with equivalent expression.
  • The proposed Bayesian method demonstrates higher statistical power for identifying DE gene patterns compared to common practices when FDR is controlled.

Conclusions:

  • The Bayesian partition model provides a robust framework for identifying complex gene expression patterns while maintaining FDR control.
  • Identifying patterns involving equivalent expression is inherently more challenging and requires larger sample sizes than identifying patterns with only differential expression.
  • The developed Bayesian approach offers a superior alternative to conventional methods for gene expression pattern analysis.