CytoCommunity - Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes
Introduction
We developed the CytoCommunity algorithm for identifying TCNs that can be applied in either an unsupervised or a supervised learning framework. The direct usage of cell phenotypes as initial features to learn TCNs makes it applicable to both single-cell transcriptomics and proteomics data, with the interpretation of TCN functions facilitated as well. Additionally, CytoCommunity can not only infer TCNs for individual images but also identify condition-specific TCNs for a set of images by leveraging graph pooling and image labels, which effectively addresses the challenge of TCN alignment across images.
CytoCommunity is the first computational tool for end-to-end unsupervised and supervised analyses of single-cell spatial maps and enables direct discovery of conditional-specific cell-cell communication patterns across variable spatial scales.
Citation
Hu Y, Rong J, Xie R, Xu Y, Peng J, Gao L, Tan K. Learning predictive models of tissue cellular neighborhoods from cell phenotypes with graph pooling. bioRxiv, 2022.