Machine learning methods for interpreting spatial multi-omics data

PROJECT SUMMARY The proposed research program aims to develop innovative computational tools for the analysis and integration of data from emerging spatially-resolved genomic technologies, which have the potential to uncover the role of interactions with the environment in normal development and disease. Existing analytical tools for analyzing spatial omics data are limited in their interpretability and are not capable of integrating multi-modal data. Leveraging our extensive experience in computational modeling of single-cell data as another high-dimensional genomic data type, we will design machine learning frameworks in the form of probabilistic and deep generative models to tackle the analytical challenges of spatially-resolved genomic data and importantly integrate multiple data modalities. This framework will enable the identification of neighborhood patterns defined as regions with a unique composition of cell states, from the integration of spatial profiling of mRNAs, proteins, and histological imaging (Aim 1). We will build on a foundation of modeling gene regulatory networks to develop the first computational tool for inferring spatially-varying regulation from the integration of spatial ATAC-seq and RNA-seq (Aim 2). Additionally, we will develop a computational tool for inferring the spatial distribution of cells with distinct copy number profiles, and their associated gene programs (Aim 3). We highlight the versatility and generalizability of our computational methods by applying our techniques in multiple biological systems with our collaborators. These applications will provide novel insights in understanding the basis of spatial patterns in human and mouse embryonic development, brain organoid models, as well as disease systems such as neuropsychiatric disorders, glioblastoma and breast cancer. Our goal is to disseminate our computational toolbox as open-source software to the broader genomics community to unlock novel insights about the spatial organization of cell types, their interactions, and mechanisms in various biological systems.