ArticleNature communications2023
Benchmarking strategies for cross-species integration of single-cell RNA sequencing data.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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Who cites it
72 citing papers in PubMed.
- Systematic benchmarking and optimal strategy selection of cross-species integration methods.Briefings in bioinformatics · 2026Article
- Unify learns cellular evolution with universal multimodal embeddings.Nature communications · 2026Article
- Cross-species integration of single-cell data reveals conserved pathology-associated cell populations across animal models and human samples.Cell reports methods · 2026Article
- Extreme conservation of cnidarian stinging cell identity despite 600 million years of evolution.bioRxiv : the preprint server for biology · 2026Article
- Comparative single-nucleus transcriptomics reveals asymmetric evolution of the Drosophila male and female germlines.PLoS biology · 2026Article
- Corneal Innervation Research at a Crossroads: A Tool-Driven Roadmap for the Future.Investigative ophthalmology & visual science · 2026Review
- CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein.Nature communications · 2026Article
- Modern Mining: The Role of Single-cell RNA Sequencing in Advancing Neuroscience Research.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026Review
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Greedy regression-based cross-species gene expression modeling: a translational framework for forensic wound age estimation.International journal of legal medicine · 2026Article
- Integration of human and mouse single-cell transcriptomes of the developing cerebellum nominates cells-of-origin for group 3 and 4 medulloblastoma.BMC cancer · 2026Article
- Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Decoding the cellular landscape of biological stress in the human brain.Neurobiology of stress · 2026Article
- TEDD 2.0: an advanced temporal gene expression database enabled by in-silico functional analyses for developmental mechanism investigation.Science China. Life sciences · 2026Article
- Multi-species integration, alignment and annotation of single-cell RNA-seq data with CAMEX.Nature communications · 2026Article
- Deep-Learning Tool ScVital Enables Species-Agnostic Integration of Cancer Cell States.Cancer research · 2026Article
- Dynamic regulation of spermatogenesis and hybrid sterility revealed by single-cell analysis in yak and cattle.Molecular biology and evolution · 2026Article
- Computational strategies for cross-species knowledge transfer.Nature methods · 2026Review
- scDenorm: a denormalization tool for integrating single-cell transcriptomics data.GigaScience · 2026Article
12 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
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. Here, we benchmark 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examine 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 develop a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieve a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs is beneficial. SAMap outperforms when integrating whole-body atlases between species with challenging gene homology annotation. We provide our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms.
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