Cell class distributional learning for low-resolution spatially transcriptomic data

Thomas M. Menino Convention & Exhibition Center 

Recent advancements in technology have made it possible to profile spatial transcriptomics (ST), which captures genome-wide gene expression while maintaining the spatial arrangement of cells within tissues. This progress has led to a better understanding of cell communication and tissue organization. However, current techniques face a trade-off between experimental throughput and spatial resolution. Sequencing-based approaches tend to prioritize higher throughput, which results in lower resolution, producing multicellular pixel data. This type of data necessitates innovative computational methods to disentangle cell classes within a pixel and address potential confounding issues. In this study, we are interested in learning the cell class-specific information from low-resolution ST data, where each pixel contains a mixture of several different cell classes. We develop a framework leveraging topic modeling, gene network, and machine-learning to infer the distribution of cell classes across regions and genes.

Spatial transcriptomic data

Low-resolution ST data

Topic modeling

Gene network

Cell class distribution

Cell-cell communication 

Section on Statistics in Genomics and Genetics