Learning outcomes

• Understand the data structures of single-cell RNA-seq and spatial transcriptomics and the potential opportunity of integrating them
• Distinguish between classification and prediction tasks in the development of supervised learning algorithms for analyzing spatial transcriptomics data
• Discuss the strategies for addressing the challenges arising from spatial transcriptomics
• Interpret the findings of discovering spatial information in the study of Alzheimer’s disease

Speaker bio

I’m an assistant professor at McGill University, specializing in statistical methods for genetic studies, RNA sequencing, and spatial multi-omics. Previously, I was a post-doc at the University of Pennsylvania and earned my Ph.D. from the University of Waterloo. My current work involves deep learning for analyzing spatial transcriptomics and metabolomics, aiming to enhance medical research through advanced data integration.