Doctoral Defense - Yunesi Arash

About the Event

The Department of Computational Mathematics, Science and Engineering

Michigan State University

Ph.D. Dissertation Defense

August 8, 2025 at 10:00am Eastern Time

Zoom Meeting: Contact Department or Advisor for Zoom Information

Abstract

Statistical Models and Computational Tools for Cancer Transcriptomics

By: Arash Yunesi 
Advisor: Yuying Xie

Development and commercial introduction of high throughput RNA sequencing technologies has enabled a deeper look into the complex mechanisms present at sub-cellular level in diseases such as cancer and Alzheimer's disease. Bulk RNA sequencing measures the total or average content of the gene expressions per sample and allows for population level studies. These studies enable comparisons of genes and gene pathways that can predict prognosis outcome of patients or help biologists identify genes to target using immuno-therapies. More recently, spatial RNA sequencing technologies, measure tens of thousands of gene types over a grid of locations on the sample. Spatial RNA sequencing preserves location information of the measurements, hence it can be used to study the highly complex tumor micro-environment. This spatial information is crucial in understanding how immune system shuts down in and around the cancer cells, the interactions between malignant and healthy cells, and tumor metastasis. Spatial RNA sequencing, similar to other high dimensional data, requires a careful selection of important variables, in this case genes, for downstream analysis. Selecting a subset of the genes measured increases the biological signal and reduces the noise present in the data, both of which are crucial steps for any successful downstream analysis.

In this dissertation, I present a new statistical model and computational framework to select biologically informative genes for further analysis. I demonstrate the strength of this model and the computational framework through extensive simulations and applications on real datasets. In the rest of this dissertation, I present statistical analyses of bulk RNA sequencing data from Head and Neck Squamous Cancers from three different experiments.