Gaussian graphical models with applications to omics analyses

Katherine H. Shutta*, Roberta De Vito, Denise M. Scholtens, Raji Balasubramanian

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Gaussian graphical models (GGMs) provide a framework for modeling conditional dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory and a demonstration of various GGM tools in R. The mathematical foundations of GGMs are introduced with the goal of enabling the researcher to draw practical conclusions by interpreting model results. Background literature is presented, emphasizing methods recently developed for high-dimensional applications such as genomics, proteomics, or metabolomics. The application of these methods is illustrated using a publicly available dataset of gene expression profiles from 578 participants with ovarian cancer in The Cancer Genome Atlas. Stand-alone code for the demonstration is available as an RMarkdown file at

Original languageEnglish (US)
Pages (from-to)5150-5187
Number of pages38
JournalStatistics in Medicine
Issue number25
StatePublished - Nov 10 2022


  • Gaussian graphical models
  • graphical lasso
  • network medicine
  • omics
  • partial correlation networks

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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