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Ph.D. Defense: Pujan Joshi
April 21, 2022 @ 11:00 am - 12:00 pm EDT
Title: A framework for analyzing omics data using routes of biological pathways
Ph.D. Candidate: Pujan Joshi
Major Advisor: Dong-Guk Shin
Associate Advisors: Charles Giardina, Sheida Nabavi
Review Committee members: Ion Mandoiu, Derek Aguiar
Date/Time: Thursday, April 21, 2022, 11:00 AM
Location: WebEx Meeting
WebEx Link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=md5053bb0d3deb616e4e8da7b16ead5bf
Meeting number: 2622 090 5258
Password: r3SpkyAT68R
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Abstract:
Pathway analysis is a popular method aiming to derive biological interpretation from high-throughput gene expression studies. Among existing approaches, Pathway Topology (PT) based techniques are generally considered to be the most informative. However, the traditional pathway analysis methods only detect the differential activity of an entire pathway, thereby ignoring the importance of discrete routes within the pathway. In our work, we start by discussing a novel route-based pathway analysis framework, namely rPAC, which uses pathway topology to identify and score individual pathway routes. The framework decomposes signaling routes into two parts (upstream portion of a transcription factor (TF) block and the downstream portion from the TF block) and generates a pathway route perturbation analysis scheme examining activity scores assigned to both parts together. A case study involving three epithelial cancer cohorts from The Cancer Genome Atlas (TCGA) is presented to demonstrate the framework’s ability to isolate specific routes as potential signature of cancer types and subtypes. While these intra-pathway routes are informative, individual pathway components are known to interact with components of other pathways through crosstalk. Therefore, we extend our framework to identify crosstalk routes in different cancer cohorts. Crosstalk routes originate from one pathway and cross pathway boundaries to potentially regulate transcription factor block(s) in another pathway. This pathway analysis framework is further extended to conduct a higher-level pathway analysis to show abstraction of the identified crosstalk routes, a step necessary to visualize gene regulation patterns from high-throughput omics data in a user-friendly manner. Finally, we experimented with our pathway analysis framework using single-cell transcriptomics data in various contexts (Cancer, Osteoarthritis, and Alzheimer’s Disease). We will discuss how cellular heterogeneity can be highlighted in terms of pathway routes, and how these route scores of individual cells can be utilized to group cells into, namely, ‘functional” subgroups, as opposed to subgrouping cells based on known cell type markers.
