Cross-species cell-type assignment of single-cell RNA-seq by a heterogeneous graph neural network

Abstract

Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assignment is a crucial step to achieve that. However, the poorly annotated genome and limited known biomarkers hinder us from assigning cell identities for nonmodel species. Here, we design a heterogeneous graph neural network model CAME to learn aligned and interpretable cell and gene embeddings for cross-species cell type assignment and gene module extraction from scRNA-seq data. CAME achieves significant improvements in cell-type characterization across distant species due to the utilization of non-one-to-one homologous gene mapping ignored by early methods. Large-scale benchmarking study showed that CAME significantly outperformed four classical methods in terms of cell-type assignment and model robustness to insufficiency and inconsistency of sequencing depths. CAME could transfer the major cell types and interneuron subtypes of human brains to mouse ones and discover shared cell type-specific functions in homologous gene modules. CAME could well align the trajectories of human and macaque spermatogenesis and reveal their conservative expression dynamics. In short, CAME can make accurate cross-species cell type assignments even for nonmodel species, and uncover shared and divergent characteristics between two species from scRNA-seq data.