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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Published on: December 10, 2012

Prior robust empirical Bayes inference for large-scale data by conditioning on rank with application to microarray

J G Liao1, Timothy McMurry, Arthur Berg

  • 1Division of Biostatistics and Bioinformatics, Penn State University, Hershey, PA 17033, USA.

Biostatistics (Oxford, England)
|August 13, 2013
PubMed
Summary

This study introduces a novel rank-conditioned inference method for microarray analysis, enhancing robustness when prior assumptions are inaccurate. This approach improves gene expression data analysis and reduces bias in statistical modeling.

Keywords:
Bayesian shrinkageConfidence intervalsRanking biasRobust multiple estimation

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Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Empirical Bayes methods are widely used in microarray data analysis for parameter estimation and multiple testing correction.
  • Standard empirical Bayes models can be sensitive to misspecified prior distributions, potentially leading to suboptimal performance.
  • Borrowing information across genes is a key advantage of empirical Bayes, but prior accuracy is crucial.

Purpose of the Study:

  • To develop a more robust inference method for microarray data analysis that is less sensitive to prior assumptions.
  • To improve the accuracy and reliability of statistical inference in gene expression studies.
  • To provide a flexible framework that can incorporate accurate non-parametric prior estimates.

Main Methods:

  • Proposed a novel rank-conditioned inference approach for statistical modeling.
  • Developed shrinkage and confidence interval estimation based on the distribution of error conditioned on data rank.
  • Contrasted the new method with standard Bayesian posterior inference, which conditions on the data itself.

Main Results:

  • The rank-conditioned method demonstrates robustness when the working prior deviates from the true prior.
  • Achieves high efficiency comparable to standard Bayesian methods when priors are well-specified.
  • Facilitates the incorporation of complex non-parametric prior estimates for improved inference.
  • Simulation studies confirmed the prior robustness of the new method.

Conclusions:

  • Rank-conditioned inference offers a robust alternative to standard empirical Bayes methods for microarray data analysis.
  • The proposed methodology enhances statistical inference accuracy, particularly when prior information is uncertain.
  • The R package rank.Shrinkage provides a practical implementation for researchers in gene expression analysis.