Gaussian graphical models with applications to omics analyses

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

doi:10.1002/sim.9546

Gaussian graphical models with applications to omics analyses

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

^*Corresponding author for this work

Preventive Medicine

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

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 https://github.com/katehoffshutta/ggmTutorial.

Original language	English (US)
Pages (from-to)	5150-5187
Number of pages	38
Journal	Statistics in Medicine
Volume	41
Issue number	25
DOIs	https://doi.org/10.1002/sim.9546
State	Published - Nov 10 2022

Funding

information U.S. National Library of Medicine, R01LM013444-01 Research reported in this publication was supported by the U.S. Natural Library of Medicine of the National Institutes of Health under award number R01LM013444‐01.

Keywords

Gaussian graphical models
graphical lasso
network medicine
omics
partial correlation networks

ASJC Scopus subject areas

Epidemiology
Statistics and Probability

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1002/sim.9546

Cite this

@article{88b4111107be490da7e24ebb9da964c4,

title = "Gaussian graphical models with applications to omics analyses",

abstract = "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 https://github.com/katehoffshutta/ggmTutorial.",

keywords = "Gaussian graphical models, graphical lasso, network medicine, omics, partial correlation networks",

author = "Shutta, {Katherine H.} and {De Vito}, Roberta and Scholtens, {Denise M.} and Raji Balasubramanian",

note = "Publisher Copyright: {\textcopyright} 2022 John Wiley & Sons Ltd.",

year = "2022",

month = nov,

day = "10",

doi = "10.1002/sim.9546",

language = "English (US)",

volume = "41",

pages = "5150--5187",

journal = "Statistics in Medicine",

issn = "0277-6715",

publisher = "John Wiley and Sons Ltd",

number = "25",

}

TY - JOUR

T1 - Gaussian graphical models with applications to omics analyses

AU - Shutta, Katherine H.

AU - De Vito, Roberta

AU - Scholtens, Denise M.

AU - Balasubramanian, Raji

PY - 2022/11/10

Y1 - 2022/11/10

N2 - 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 https://github.com/katehoffshutta/ggmTutorial.

AB - 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 https://github.com/katehoffshutta/ggmTutorial.

KW - Gaussian graphical models

KW - graphical lasso

KW - network medicine

KW - omics

KW - partial correlation networks

UR - http://www.scopus.com/inward/record.url?scp=85139006205&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85139006205&partnerID=8YFLogxK

U2 - 10.1002/sim.9546

DO - 10.1002/sim.9546

M3 - Article

C2 - 36161666

AN - SCOPUS:85139006205

SN - 0277-6715

VL - 41

SP - 5150

EP - 5187

JO - Statistics in Medicine

JF - Statistics in Medicine

IS - 25

ER -

Gaussian graphical models with applications to omics analyses

Abstract

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this