Date: 2026-04-17

Time: 15:30-16:30 (Montreal time)

Location: In person, Burnside 1104

https://mcgill.zoom.us/j/86146204241

Meeting ID: 861 4620 4241

Passcode: None

Abstract:

This talk presents two complementary methodological frameworks for improving single-cell CRISPR screens, from both the analysis and study-design perspectives. The first, spaCRT, addresses a key inferential challenge in single-cell data: gene expression measurements are often sparse and noisy, so standard asymptotic tests can miscalibrate significance while resampling methods, though more reliable, are often too slow at scale. spaCRT overcomes this by using saddlepoint approximations to provide a closed-form approximation to the resampling p-value, yielding accurate error control, competitive power, and substantial computational savings. The second, PerturbPlan, addresses the design side of these experiments: because CRISPR screens are expensive, experimental choices with similar budgets can differ greatly in statistical power. PerturbPlan uses an analytic power formula, validated through simulations and real datasets, to provide near-instant power estimates and generate cost-aware, power-optimized designs across a broad range of common study settings. Together, these frameworks aim to make single-cell CRISPR studies both more statistically reliable and more efficiently designed.

Speaker

Ziang Niu is a Ph.D. student in Statistics at the Wharton School of the University of Pennsylvania, advised by Eugene Katsevich and Bhaswar Bhattacharya. His research lies at the intersection of probabilistic theory, statistical methodology, and single-cell genomics. He works on methodological and theoretical questions in hypothesis testing for complex data, with an emphasis on computationally efficient and statistically rigorous methods motivated by modern biological applications.