DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence.

ABSTRACT: Recent development of spatial transcriptomics (ST) is capable of associating spatial information at different spots in the tissue section with RNA abundance of cells within each spot, which is particularly important to understand tissue cytoarchitectures and functions. However, for such ST data, since a spot is usually larger than an individual cell, gene expressions measured at each spot are from a mixture of cells with heterogenous cell types. Therefore, ST data at each spot needs to be disentangled so as to reveal the cell compositions at that spatial spot. In this study, we propose a novel method, named deconvoluting spatial transcriptomics data through graph-based convolutional networks (DSTG), to accurately deconvolute the observed gene expressions at each spot and recover its cell c

SUBMITTER: Song Q 

PROVIDER: S-EPMC8425268 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature