报告题目：Integrating computational framework and statistical modeling for analyzing genomics data
报告人：马文静 香港中文大学（深圳）人工智能学院 助理教授
报告摘要：
Improvements of resolutions and spatial context in sequencing technologies over the past several years have deepen the understanding of complex biological systems. However, these improvements also pose substantial computational and statistical challenges when analyzing the high-dimensional data, including accurate identification of key biological features, such as functional spatial domains and cell type identities, and in leveraging this information to dissect bulk genomics data.
In this talk, the presenter will present three research projects that address these challenges. The first focuses on uncovering spatial domain dynamics in spatiotemporal transcriptomics. This work combines variational autoencoders with Gaussian processes to learn spatially-aware embeddings, and integrates optimal transport modeling to characterize domain transitions over time. The second project introduces Cellcano, a supervised learning framework for accurate and robust cell type identification in single-cell ATAC-seq data. By using entropy to select high-confidence anchor cells, Cellcano enhances cross-dataset prediction performance. The final project, LRcell, is a regression-based method that incorporates single-cell marker gene information to infer the sub-cell-type sources of differential expression signals from bulk RNA-seq data.
报告人简介：
Wenjing Ma is a postdoctoral research fellow in the Department of Biostatistics at the University of Michigan, working with Dr. Xiang Zhou. She received her Ph.D. in Computer Science and Informatics from Emory University under the supervision of Dr. Hao Wu. Her research focuses on developing methods and tools for accurately and efficiently analyzing high-throughput sequencing data, including spatial, single-cell and bulk genomics, to address important biological questions. Methodologically, she is interested in integrating statistical modeling into computational frameworks to incorporate biological priors. Biologically, she is interested in understanding cellular dynamics during development and disease progression by leveraging temporal information across multiple omics modalities. Her work has been published in journals including Nature Communications, Genome Biology, and Briefings in Bioinformatics.
报告时间：2025年12月24日（星期三）上午 9:30
报告地点：吉林大学正新楼三楼智慧教室
