Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning.

ABSTRACT: Spatially resolved transcriptomics (SRT) technology enables us to gain novel insights into tissue architecture and cell development, especially in tumors. However, lacking computational exploitation of biological contexts and multi-view features severely hinders the elucidation of tissue heterogeneity. Here, we propose stMVC, a multi-view graph collaborative-learning model that integrates histology, gene expression, spatial location, and biological contexts in analyzing SRT data by attention. Specifically, stMVC adopting semi-supervised graph attention autoencoder separately learns view-specific representations of histological-similarity-graph or spatial-location-graph, and then simultaneously integrates two-view graphs for robust representations through attention under semi-supervision of

SUBMITTER: Zuo C 

PROVIDER: S-EPMC9551038 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature