ArticleNucleic acids research2025
Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Systematic benchmarking and optimal strategy selection of cross-species integration methods.Briefings in bioinformatics · 2026Article
- scMustree: a multi-scale functional hierarchy for single-cell transcriptomic analysis.Bioinformatics (Oxford, England) · 2026Article
- Article
- Similarities betweeneNeuro · 2026Article
- Unify learns cellular evolution with universal multimodal embeddings.Nature communications · 2026Article
- Organism-scale annotation with Pan-human Azimuth.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking plant single cell RNA-sequencing sample processing strategies.The EMBO journal · 2026Article
- New biological insights into osteosarcoma-lessons from single cell sequencing studies.Cancer metastasis reviews · 2026Review
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Multi-species integration, alignment and annotation of single-cell RNA-seq data with CAMEX.Nature communications · 2026Article
- Deciphering the 3D genome organization across species from Hi-C data.Nucleic acids research · 2026Article
- A comparative review of single-cell atlases: mapping cellular diversity across species and tissues.Cellular and molecular life sciences : CMLS · 2025Review
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Integrating single-cell RNA-seq datasets with substantial batch effects.BMC genomics · 2025Article
- OmniCellX: A Versatile and Comprehensive Browser-Based Tool for Single-Cell RNA Sequencing Analysis.Biology · 2025Article
- Opioid-induced immunosuppression of brain myeloid cells in SIV-infected rhesus macaques.Research square · 2025Article
- Establishing single cell RNA transcriptomics: a brief guide.Frontiers in zoology · 2025Review
- The dorsal/ventral subdivision of the hindbrain predates the tunicate/vertebrate split.bioRxiv : the preprint server for biology · 2025Article
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7 authors.
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Abstract
Cross-species single-cell RNA-seq data hold immense potential for unraveling cell type evolution and transferring knowledge between well-explored and less-studied species. However, challenges arise from interspecific genetic variation, batch effects stemming from experimental discrepancies and inherent individual biological differences. 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. Methods that effectively leverage gene sequence information capture underlying biological variances, while generative model-based approaches excel in batch effect removal. SATURN demonstrates robust performance across diverse taxonomic levels, from cross-genus to cross-phylum, emphasizing its versatility. SAMap excels in integrating species beyond the cross-family level, especially for atlas-level cross-species integration, while scGen shines within or below the cross-class hierarchy. As a result, our analysis offers recommendations and guidelines for selecting suitable integration methods, enhancing cross-species single-cell RNA-seq analyses and advancing algorithm development.
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