Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning.

ABSTRACT: Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB imag

SUBMITTER: Chang Y 

PROVIDER: S-EPMC9440291 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature