Undergraduate Research & Scholarships

Quanjin(Terry) Su

Statistical Methods for Single‑Cell Data on Patient Cohorts

Single‑cell RNA‑seq exposes the rich mix of cell states inside every patient, yet current statistics often blur true biology with technical noise. This project develops and benchmarks new methods that model both within‑patient and between‑patient variability, enabling fair cross‑cohort comparisons and more accurate links between cellular diversity and health outcomes. Over the summer we will extend our benchmark to dozens of public datasets, testing density‑based summaries such as Gaussian Mixture Models and k‑Nearest Neighbors. Our ultimate goal is a robust, open-source toolkit that clinicians and biologists can trust when studying disease at single-cell resolution.

Message To Sponsor

Thank you very much for supporting this invaluable research experience. Your generosity allows me to fully immerse myself in developing statistical tools that could enhance our understanding of diseases at the single-cell level. This opportunity not only helps advance crucial scientific methodologies but also supports my personal goal of pursuing graduate studies in statistics and data science.