Date of Award

January 2023

Document Type

Open Access Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Zuoheng Wang

Second Advisor

Xiting Yan

Abstract

Diverse types of cells interact and communicate with each other to maintain tissue homeostasis and perform biological functions. Perturbations to these interactions can break the homeostasis of the tissue microenvironment, leading to disease. Understanding intercellular communication changes in disease is critical for therapeutic development. Cell-cell communication networks (CCCNs) inferred from single-cell RNA sequencing data are highly variable and only capture a snapshot of the dynamic intercellular communication system. We develop a graphical generative model to compare CCCNs between disease and control samples to identify disease associated perturbations to intercellular communications. The distribution of CCCNs is learned using variational graph autoencoder (VGAE) in disease and control groups separately. Then a large number of graphs is generated to assess the significance of the difference between the two distributions using different graph distance measures. We demonstrate the advantage of this approach in improving the power of identifying disease associated perturbations to intercellular communications through both simulation studies and real scRNA-seq datasets.

Comments

This is an Open Access Thesis.

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