Cross-species cell-type assignment from single-cell RNA-seq data 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 the 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 owing to the utilization of non-one-to-one homologous gene mapping ignored

SUBMITTER: Liu X 

PROVIDER: S-EPMC9977153 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature