Single-cell colocalization analysis using a deep generative model.

Abstract

Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, and cellular responses to external stimuli and their biological functions in diseases and tissues. However, existing computational methodologies identified the colocalization patterns between predefined cell populations, which can obscure the molecular signatures arising from intercellular communication. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers intercellular colocalization networks with single-cell resolution by the integration of single-cell and spatial transcriptomes. Along with colocalized population detection accuracy that is superior to existing methods in simulated dataset, DeepCOLOR identified plausible cell-cell interaction candidates between colocalized single cells and segregated cell populations defined by the colocalization relationships in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2. DeepCOLOR is applicable to studying cell-cell interactions behind various spatial niches. A record of this paper's transparent peer review process is included in the supplemental information.