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Author ORCID Identifier
Open Access Dissertation
Doctor of Philosophy (PhD)
Year Degree Awarded
Month Degree Awarded
Laura B. Balzer
Roberta De Vito
Susan E. Hankinson
Laura D. Kubzansky
Gaussian graphical models (GGMs) are useful network estimation tools for modeling direct dependencies that characterize multivariate data. The GGM modeling framework is one way to elucidate complex systems-level properties that can be difficult to detect in univariate analyses. In this dissertation, we begin by presenting a tutorial and review of the current state of the field of GGM theory and application. Next, we present a motivating application of GGMs in a study of metabolomic networks associated with chronic distress in women in the Women's Health Initiative (WHI) and in the Nurses' Health Study cohorts. In the third chapter, we present a tool called SpiderLearner, a SuperLearner-based ensemble method for GGM estimation that utilizes a range of existing GGM estimation approaches together with K-fold cross-validation to optimize a likelihood-based loss function. We show via simulation that SpiderLearner performs as well as or better than each individual method and present an application to risk prediction in ovarian cancer genomic data. In the fourth chapter, we present a factor analysis-based method that we have developed to estimate direct dependencies (GGMs) that are shared across studies (or conditions) and those that are study-specific in settings of multi-study data. We apply this method to analyze maternal response to an oral glucose tolerance test as assessed by targeted metabolomic profiles collected in the Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) study. We investigate differences in glucose metabolism across ancestry groups, constructing a GGM that is shared across four ancestry groups and a GGM specific to each of these.
Shutta, Katherine H., "Gaussian Graphical Models for Omics Data: New Methodology and Applications" (2022). Doctoral Dissertations. 2474.
Available for download on Wednesday, February 01, 2023