Kailun Bai
- BSc (State University of New York at Stony Brook, 2015)
Notice of the Final Oral Examination for the Degree of Doctor of Philosophy
Topic
Deep Learning Methods for Cell Type Annotation in Single-Cell RNA Sequencing
Department of Mathematics and Statistics
Date & location
- Monday, September 15, 2025
- 9:00 A.M.
- Virtual Defence
Examining Committee
Supervisory Committee
- Dr. Xuekui Zhang, Department of Mathematics and Statistics, University of Victoria (Supervisor)
- Dr. Xiaojian Shao, Department of Mathematics and Statistics, UVic (Co-Supervisor)
- Dr. Belaid Moa, Department of Electrical and Computer Engineering, UVic (Outside Member)
External Examiner
- Dr. Pingzhao Hu, Department of Biochemistry, Western University
Chair of Oral Examination
- Dr. Michael McGuire, Department of Electrical and Computer Engineering, UVic
Abstract
This dissertation presents a systematic exploration of scalable, interpretable, and high-accuracy computational frameworks for automated cell type classification in scRNA-seq data. The research spans three major contributions, each addressing different trade-offs between simplicity, interpretability, and predictive power:
- PCLDA (Penalized Component-wise Linear Discriminant Analysis) introduces a highly interpretable and statistically grounded annotation tool.
- scSorterDL expands on this foundation by combining penalized LDA with ensemble learning and deep neural networks.
- CellAnnotatorNet represents the culmination of this research by integrating a categorical autoencoder with the Swarm-pLDA framework into a unified, fully differentiable architecture.
Together, these three contributions provide a progressive development from classical interpretable models to fully integrated deep learning pipelines for large-scale single-cell analysis.