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DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks.

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  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

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|December 19, 2022
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DRAGON analyzes paired methylomic and transcriptomic data using Gaussian Graphical Models. This network approach improves multi-omic data analysis and identifies biomarkers like TFAP2B in breast cancer.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Multi-omic data, including methylomic and transcriptomic profiles, offers insights into cellular phenotypes and responses.
  • Analyzing paired omics data presents challenges due to varying data characteristics.

Purpose of the Study:

  • To develop a network approach for joint analysis of paired omics data.
  • To introduce DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks) for enhanced multi-omic data integration.

Main Methods:

  • Utilized Gaussian Graphical Models (GGMs) for network construction.
  • Developed DRAGON to calibrate parameters for optimal complexity-accuracy trade-off, accounting for different omics layers.
  • Validated through simulation studies and analysis of TCGA breast cancer data.

Main Results:

  • DRAGON demonstrated improved model inference and edge recovery compared to existing methods in simulations.
  • The method effectively adapted to differences in edge density and feature size between omics layers.
  • Identified gene regulation via promoter methylation and highlighted Transcription Factor AP-2 Beta (TFAP2B) as a potential biomarker in basal-type breast cancer.

Conclusions:

  • DRAGON provides an effective framework for joint analysis of multi-omic data.
  • The method facilitates the discovery of key molecular mechanisms and potential biomarkers.
  • DRAGON is available as open-source Python code via the Network Zoo package.