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Benchmarking cross-species single-cell RNA-seq data integration methods . . . Here, we present a pioneering benchmark study of cross-species single-cell RNA-seq data integration methods across various settings, spanning the entire animal tree of life hierarchy and including genus, family, order, class and phylum species integrations (Figure 1)
lt;br gt;跨物种单细胞 RNA-seq 数据整合方法的基准测试：迈向 . . . Here, we benchmarked nine data-integration methods across 20 species, encompassing 4 7 million cells, spanning eight phyla and the entire animal taxonomic hierarchy
Multi-species integration, alignment and annotation of single-cell RNA . . . To tackle the above challenges, we introduce CAMEX, a heterogeneous Graph Neural Network (GNN) tool that leverages many-to-many homologous relationships for multi-species integration, alignment, and annotation of scRNA-seq data from multiple species
Benchmarking cross-species single-cell RNA-seq data integration methods . . . Here, we benchmarked nine data-integration methods across 20 species, encompassing 4 7 million cells, spanning eight phyla and the entire animal taxonomic hierarchy Our evaluation reveals notable differences between the methods in removing batch effects and preserving biological variance across taxonomic distances