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Updated: May 27, 2025

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A probabilistic modeling framework for genomic networks incorporating sample heterogeneity.

Liying Chen1, Satwik Acharyya2, Chunyu Luo3

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.

Cell Reports Methods
|February 15, 2025
PubMed
Summary

Graphical regression (GraphR) addresses limitations in biological network analysis by accounting for sample heterogeneity. This Bayesian approach enables sample-specific network estimation and reveals biological insights missed by other methods.

Keywords:
CP: systems biologygene regulatory networksgenomicsgraphical regressionheterogeneous graphical modelsnetwork modelingspatial graphical modelsspatial transcriptomicsvariable selectionvariational Bayes

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Probabilistic graphical models are essential for analyzing complex biological networks in high-throughput omics data.
  • Existing models often assume sample homogeneity, restricting their application to heterogeneous biological systems.

Purpose of the Study:

  • To introduce graphical regression (GraphR), a flexible Bayesian method for network analysis in heterogeneous biological data.
  • To enable sparse, sample-specific network estimation and quantify the impact of heterogeneity on network structures.

Main Methods:

  • Developed a regression-based Bayesian framework (GraphR) to incorporate sample heterogeneity at multiple scales.
  • Utilized variational Bayes algorithms for computational efficiency in network estimation.
  • Compared GraphR's performance against state-of-the-art methods for network structure recovery and computational cost.

Main Results:

  • GraphR demonstrates superior efficiency in network structure recovery and computational cost across various settings.
  • Analysis of multi-omic and spatial transcriptomic datasets revealed novel inter- and intra-sample molecular network insights.
  • Identified biological discoveries not attainable with existing network analysis approaches.

Conclusions:

  • GraphR provides a powerful and flexible approach to analyze complex biological networks with sample heterogeneity.
  • The developed GraphR R package and Shiny App facilitate comprehensive analysis and dynamic visualization of biological networks.