Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis.

ABSTRACT: Recent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance our understanding of treatment response and disease progression. Cellular neighborhood analysis is a popular computational approach for these complex image data. Despite its popularity, there are significant challenges, including high computational demands that limit feasibility for large-scale applications and the lack of a principled strategy for integrative analysis across images. This absence hampers the precise and consistent identification of spatial features and tracking of their dynamics over disease progression. To overcome these challenges, we introduce SpatialTopic, a spatial topic model designed to decode high-level spatial architecture across multiplexed tissue ima

SUBMITTER: Peng X 

PROVIDER: S-EPMC11482893 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature