Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes.

ABSTRACT: It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from the cell phenotype space to the TCN space using a graph neural network model without intermediate clustering of cell embeddings. By leveraging graph pooling, CytoCommunity enables de novo identification of condition-specific and predictive TCNs under the supervision of sample labels. Using several types of spatial omics data, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. By analyzing risk-stratified colorectal

SUBMITTER: Hu Y 

PROVIDER: S-EPMC10864185 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature