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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Bayesian inference of networks across multiple sample groups and data types.

Elin Shaddox1, Christine B Peterson2, Francesco C Stingo3

  • 1Department of Statistics, Rice University, Houston, TX, USA.

Biostatistics (Oxford, England)
|December 28, 2018
PubMed
Summary

This study introduces a Bayesian hierarchical model for network inference across multiple data types and sample groups. The method integrates diverse biological data for enhanced medical research, particularly for complex diseases like chronic obstructive pulmonary disease (COPD).

Keywords:
Bayesian inferenceChronic obstructive pulmonary disease (COPD)Data integrationGaussian graphical modelMarkov random field priorSpike and slab prior

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Area of Science:

  • Computational Biology and Bioinformatics
  • Statistical Genetics
  • Systems Biology

Background:

  • Medical studies often involve heterogeneous subjects and multi-platform data (e.g., metabolomics, proteomics, transcriptomics).
  • Existing methods may struggle to integrate diverse data types and sample groups effectively for network inference.
  • Understanding complex biological networks requires methods that can handle heterogeneity and multiple data sources.

Purpose of the Study:

  • To develop a flexible graphical modeling framework for inferring biological networks across multiple sample groups and data types.
  • To enable joint estimation of network structures without assumptions on data type influence directionality or network similarity.
  • To apply the framework to analyze gene expression and metabolite data in chronic obstructive pulmonary disease (COPD) patients.

Main Methods:

  • A Bayesian hierarchical model incorporating a Markov random field prior to link network structures across sample groups.
  • Linking network similarity parameters across different data platforms (e.g., transcriptomics, metabolomics).
  • Flexible model formulation accommodating differing numbers of variables and subjects across data types, requiring only shared sample groups.

Main Results:

  • Demonstrated the framework's capability through simulation studies.
  • Successfully applied the model to integrate gene expression and metabolite abundance data from COPD subjects.
  • The approach allows for robust network inference in complex, multi-modal biological datasets.

Conclusions:

  • The proposed Bayesian hierarchical model provides a powerful tool for integrated network inference across diverse biological data.
  • This framework enhances understanding of complex diseases by leveraging multi-platform data and sample heterogeneity.
  • The method is applicable to various medical research settings requiring joint analysis of multiple data types and groups.