Resolving Tissue Maps: Statistical and Deep Learning Methods for Integrative Spatial Omics Across Samples, Sections, and Modalities
Brown University
Abstract:
Spatial omics technologies have opened new frontiers for understanding the molecular organization of tissues, yet major challenges remain in scalability, cross-sample alignment, and integration across resolutions and modalities. In this talk, I will present a series of data integration methods developed in my lab to address these challenges across samples, platforms, and molecular signals. I will introduce statistical methods that learn biologically informed spatial domains, and deep learning methods that jointly learn spatial correspondence and shared representations across tissue sections, as well as uncertainty-aware contrastive models for integrating spatial multi-omics data across diverse modalities. These approaches enable robust alignment despite distortion and batch effects, preserve modality-specific biological signals, and scale to ultra–high-resolution datasets. Together, these methods uncover refined spatial domains, modality-specific regulatory programs, and regions of biological or technical heterogeneity. By combining scalability, generalizability, and interpretability, our goal is to build reliable computational foundations for spatial omics that accelerate biological discovery.
Biography:
Ying Ma is an Assistant Professor of Biostatistics and the Edens Family Assistant Professor of Healthcare Communications and Technology in the Department of Biostatistics at Brown University, and a core faculty member of the Center for Computational Molecular Biology. Her lab focuses on developing efficient statistical learning and AI methods to address a variety of biological questions and computational challenges in genomics and genetics, particularly in single-cell RNA sequencing and spatially resolved transcriptomics. Her research aims to characterize cellular heterogeneity, spatial tissue organization, and how cellular states and microenvironments relate to phenotypic variation and disease mechanisms. By integrating multi-omics, imaging, and clinical data, her work seeks to advance our understanding of complex biological systems and enable data-driven precision medicine.