CISPA
Browse
cispa_all_3893.pdf (2.64 MB)

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

Download (2.64 MB)
journal contribution
posted on 2023-11-29, 18:05 authored by Katherine H . Shutta, Deborah Weighill, Rebekka BurkholzRebekka Burkholz, Marouen Ben Guebila, Dawn L. DeMeo, Helena U. Zacharias, John Quackenbush, Michael Altenbuchinger
The increasing quantity of multi-omic data, such as methylomic and transcriptomic profiles collected on the same specimen or even on the same cell, provides a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations. We propose a network approach based on Gaussian Graphical Models (GGMs) that facilitates the joint analysis of paired omics data. This method, called DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network’s complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics ‘layers.’ In simulation studies, we show that DRAGON adapts to edge density and feature size differences between omics layers, improving model inference and edge recovery compared to state-of-the-art methods. We further demonstrate in an analysis of joint transcriptome - methylome data from TCGA breast cancer specimens that DRAGON can identify key molecular mechanisms such as gene regulation via promoter methylation. In particular, we identify Transcription Factor AP-2 Beta (TFAP2B) as a potential multi-omic biomarker for basal-type breast cancer. DRAGON is available as open-source code in Python through the Network Zoo package (netZooPy v0.8; netzoo.github.io).

History

Preferred Citation

Katherine Shutta, Deborah Weighill, Rebekka Burkholz, Guebila Ben, Dawn DeMeo, Helena Zacharias, John Quackenbush and Michael Altenbuchinger. DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks. In: Nucleic Acids Research. 2022.

Primary Research Area

  • Reliable Security Guarantees

Legacy Posted Date

2023-01-13

Journal

Nucleic Acids Research

Open Access Type

  • Green

Sub Type

  • Article

BibTeX

@article{cispa_all_3893, title = "DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks", author = "Shutta, Katherine H . and Weighill, Deborah and Burkholz, Rebekka and Ben Guebila, Marouen and DeMeo, Dawn L. and Zacharias, Helena U. and Quackenbush, John and Altenbuchinger, Michael", journal="{Nucleic Acids Research}", year="2022", }

Usage metrics

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC