Biostatistics Seminar: Fast diagonal single-cell multi-omics integration with self-mapping
Speaker: Leying Guan, PhD, Associate Professor of Biostatistics, Yale University
Presentation: Fast diagonal single-cell multi-omics integration with self-mapping
Abstract: The integration of “diagonal” single-cell multi-omics data—where distinct modalities (for example, RNA and protein) are measured in different cells and in disjoint feature spaces—remains a major challenge in computational biology. Unlike horizontal integration, diagonal datasets provide neither cell–cell correspondences nor reliable shared features, requiring cross-modal alignment under modality-specific measurement processes. Existing approaches therefore often rely on curated or inferred feature links together with mutual nearest-neighbor search to define anchor correspondences, but these anchors can be unreliable under weak linkage and the matching step can be computationally burdensome at atlas scale. Here we introduce SCALEMAP, a fast, modality-agnostic integration method based on self-mapping that avoids computationally expensive…
Event interval: Single day event. Campus room: Virtual Seminar. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Academics.
Thursday, March 5, 2026, 3:30 PM – 4:30 PM.
Final Exam - Gabriela Vasconcelos
Committee: Ali Shojaie (chair), Patrick Danaher, Li Hsu, Jon Wakefield, Sanjay Srivatsan (GSR)
Presentation: Statistical Methods for Spatially Resolved Transcriptomics Data
Abstract: Spatial transcriptomics technologies enable the measurement of gene expression within intact tissues, providing unprecedented opportunities to study spatially organized biological processes. However, the spatial structure of these data introduces statistical challenges that are not adequately addressed by existing methods. In particular, spatial correlation can induce spurious signals in downstream analyses, leading to inflated false positives, biased effect estimates, and misleading interpretations of gene regulation and differential expression. This dissertation develops statistical methodology to understand and account for spatial dependence, with a focus on differential expression and gene–gene association analyses in spatially resolved data.
The first project develops methods for within-sample differential expression that…
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Online Meeting Link: https://washington.zoom.us/j/93900910631?pwd=1xnS6Aa8IoWPfEdsPbEKOldinvUTIh.1. Campus room: HRC 370. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Academics.
Friday, March 6, 2026, 3:00 PM – 5:00 PM.
Final Exam - Yinxiang Wu
Committee: Ting Ye (co-chair), Andrea Rotnitzky (co-chair), Li Hsu, Marco Carone, Sara Lindstroem (GSR)
Presentation: Causal Inference in the Presence of Unmeasured Confounding: Advances in Mendelian Randomization and Proximal Causal Inference
Abstract: Observational data are indispensable for causal inference, particularly when randomized controlled trials are infeasible due to ethical, logistical, or economic constraints. However, observational data are subject to unmeasured confounding, where unobserved variables influence both the treatment and the outcome, potentially leading to biased estimates and spurious findings.
This dissertation aims to develop statistical methods for causal inference in the presence of unmeasured confounding, focusing on multivariable Mendelian randomization (MVMR) using summary-level data and proximal causal inference using individual-level data. MVMR uses genetic variants as instrumental variables (IVs) to infer the direct causal effects of multiple exposures on an outcome.…
Event interval: Single day event. Online Meeting Link: https://washington.zoom.us/j/6287851634. Campus room: HRC 342. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Academics.
Thursday, March 12, 2026, 10:00 AM – 12:00 PM.
Biostatistics Seminar: Advancing Cancer Risk Prediction: Multi-Ancestry PRS, Model Recalibration, and Benefit-Burden Evaluation
Speaker: Li Hsu, PhD, Professor, Biostatistics Program, Fred Hutch Cancer Center; Affiliate Professor of Biostatistics, University of Washington
Presentation: Advancing Cancer Risk Prediction: Multi-Ancestry PRS, Model Recalibration, and Benefit-Burden Evaluation
Abstract: Precision cancer prevention depends on accurate and transportable risk prediction tools with equitable performance across populations, as well as rigorous of prevention strategies in real-world cohorts. In this talk, I will present several lines of recent methodological and applied work aimed at advancing these goals in colorectal cancer. I will discuss improvements in the development of polygenic risk scores (PRS) across population groups. While PRS show promise for identifying individuals at elevated risk who may benefit from targeted screening, most existing scores are derived primarily from European ancestry data and have sub-optimal performance in underrepresented populations. To address this limitation, we developed a functionally…
Event interval: Single day event. Campus location: Architecture Hall (ARC). Campus room: ARC G070. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Academics.
Thursday, March 12, 2026, 3:30 PM – 4:30 PM.
Biostatistics Seminar: Five lessons for target gene identification in the post-GWAS era
Speaker: Boxiang Liu, PhD, Presidential Young Professor,Department of Pharmacy and Pharmaceutical Sciences & Department of Biomedical Informatics, National University of Singapore; Principal Research Scientist, Genome Institute of Singapore, A*STAR
Title: Five lessons for target gene identification in the post-GWAS era
Abstract: Despite over a million risk variants identified by genome-wide association studies (GWAS), translating these associations into therapeutic insights remains a bottleneck in genomic medicine. In this talk, I present five critical lessons from post-GWAS research that collectively advance our ability to pinpoint disease-relevant target genes. Using large-scale single-cell RNA sequencing across diverse Asian populations and gene-by-intervention studies, we highlight the significance of cell-type-specific QTLs, ancestry-driven genetic insights, and context-dependent gene regulation. Our systematic benchmarking of over gene implication methods shows that ensemble approaches and adaptive…
Event interval: Single day event. Campus room: Virtual Seminar. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Academics.
Thursday, March 19, 2026, 3:30 PM – 4:30 PM.