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Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis.

Ben Li1, Yunxiao Li1, Zhaohui S Qin1,2

  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.

Statistics in Biosciences
|September 19, 2017
PubMed
Summary
This summary is machine-generated.

This study addresses the "large p, small n" problem in high-throughput biology by enhancing Bayesian hierarchical models. The new adaptiveHM R package leverages historical data for improved feature analysis and cross-platform insights.

Keywords:
450K methylation arrayBayesian hierarchical modelbisulphite sequencinghistorical datainformative prior

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Area of Science:

  • Genomics
  • Biostatistics
  • Computational Biology

Background:

  • High-throughput biotechnologies (microarray, next-generation sequencing) generate vast data.
  • The 'large p, small n' problem limits analysis of individual features.
  • Bayesian hierarchical models offer solutions but can cause over-correction due to shrinkage.

Purpose of the Study:

  • To investigate causes of over-correction in Bayesian hierarchical models.
  • To propose enhanced Bayesian methods utilizing historical and cross-platform data.
  • To develop a robust framework for analyzing high-throughput biological data.

Main Methods:

  • Developed an enhanced Bayesian hierarchical modeling framework.
  • Incorporated strategies to mitigate over-correction issues.
  • Utilized historical and multi-platform data for improved statistical power.
  • Implemented the method in the R package 'adaptiveHM'.

Main Results:

  • Demonstrated superior performance of the proposed strategy through simulations.
  • Validated the approach with real-world high-throughput data analysis.
  • Showcased the ability to borrow information across different data platforms.
  • The 'adaptiveHM' R package provides a practical implementation.

Conclusions:

  • The enhanced Bayesian approach effectively addresses the 'large p, small n' problem.
  • Leveraging historical and cross-platform data improves biological data analysis.
  • The 'adaptiveHM' package offers a valuable tool for researchers in the Big Data era.