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Ph.D. Proposal: Pujan Joshi
September 30, 2021 @ 11:00 am - 12:30 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
Additional Reviewers: Ion Mandoiu, Derek Aguiar
Date/Time: Thursday, September 30, 2021, 11:00 AM
Location: WebEx Meeting
Meeting link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m5d7571c3ba4f9d0bb62b0bdeda037913
Meeting number: 2623 427 2834
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Abstract:
Pathway analysis is a popular method aiming to derive biological interpretation from high-throughput gene expression studies. Pathway topology (PT) based techniques are generally considered to be more informative than gene-set based approaches. However, these methods only detect the differential activity of an entire pathway, thereby ignoring the importance of routes and sections 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 routes within pathways. 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 rPAC framework’s ability to isolate specific routes as potential signature of cancer types and subtypes. While these intra-pathway routes are informative, the underlying biological phenomenon is much more complex and individual pathway components can interact with components of other pathways through crosstalk. We extend our framework to use route-based approach to identify crosstalk between signaling pathway routes in different cancer cohorts. Crosstalk routes originate from one pathway and cross pathway boundaries to potentially regulate transcription factor block in another pathway. Finally, we will discuss how these crosstalk routes can be used to further extend our framework to conduct a higher-level pathway analysis. A higher-level pathway analysis will include abstraction of pathways and crosstalk routes in order to summarize key cellular signaling cascades to present gene regulation patterns from high-throughput omics data in a user-friendly manner.
