Yuqi Zhang will present her General Exam "Linking Genomic Alterations to Cancer Pathway Expression Changes via Canonical Correlation Analysis" on Wednesday, May 21, 2025 at 2:30 PM in Icahn Lab 280.

Yuqi Zhang will present her General Exam "Linking Genomic Alterations to Cancer Pathway Expression Changes via Canonical Correlation Analysis" on Wednesday, May 21, 2025 at 2:30 PM in Icahn Lab 280. Committee Members: Mona Singh (advisor), Olga Troyanskaya, Yuri Pritykin Abstract: Cancer is a disease where numerous genomic alterations are acquired and cause widespread disruption to cellular function. Understanding how these alterations impact biological pathways is essential for uncovering the underlying mechanisms of complex diseases. We propose a framework based on Canonical Correlation Analysis (CCA) to uncover relationships between genes frequently altered in cancer and dysregulated pathway expression. Specifically, we jointly consider the multi-omic alterations affecting a known cancer-relevant gene—including its copy number alteration (CNA), mutation, and methylation status—and model their joint relationship with the expression of genes within specific biological pathways. A key contribution of our framework is a novel sampling-based hypothesis test for CCA that mitigates random signals often observed in high-dimensional datasets. Additionally, we adapt existing visualization methods to uncover relationships between specific types of genomic alterations and gene expression. Our model inherently accounts for correlation between molecular alterations and gene expression, improving sensitivity in detecting biologically meaningful associations. We apply our method across multiple driver genes and pathways, revealing significant associations that traditional gene-level approaches miss. Our framework also reveals the directionality of genomic alterations' regulatory effects on pathway expression while accounting for correlation and joint effects in high-dimensional data. By capturing the joint influence of genomic alterations on pathway activity while reducing statistical noise, our method offers a powerful approach for discovering key regulatory mechanisms in cancer. Reading List: https://docs.google.com/document/d/1edX-ARuzaajncovwykvHNW8GUXdYFqiw83vb_AYl... Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.
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CS Grad Department