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

• DOI: 10.1038/s41467-022-33619-9
• Publication Date: 2022-10-10T13:06:46Z
• Authors: Chunman Zuo; Yijian Zhang; Chen Cao; Jinwang Feng; Mingqi Jiao; Luonan Chen
• Keywords: 0301 basic medicine; 0303 health sciences; Science; Q; Breast Neoplasms; Article; 3. Good health; Interdisciplinary Placement; 03 medical and health sciences; Humans; Female; Transcriptome

AbstractSpatially 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 biological contexts. stMVC outperforms other tools in detecting tissue structure, inferring trajectory relationships, and denoising on benchmark slices of human cortex. Particularly, stMVC identifies disease-related cell-states and their transition cell-states in breast cancer study, which are further validated by the functional and survival analysis of independent clinical data. Those results demonstrate clinical and prognostic applications from SRT data.