Benchmarking strategies for cross-species integration of single-cell RNA sequencing data

Abstract

The growing number of available single cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. We benchmarked 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examined the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also developed a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieved a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs was beneficial. SAMap outperformed when integrating whole-body atlases between species with challenging gene homology annotation. We provided our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms.