ArticleCell reports methods2023
Single-cell assignment using multiple-adversarial domain adaptation network with large-scale references.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Binary-SPA: a reference-free method for cell annotation in high-resolution spatial transcriptomics.Nucleic acids research · 2026Article
- AnnQ: reference-based quantification of cellular abnormality at single-cell resolution.Briefings in bioinformatics · 2026Article
- Binary-SPA: A Reference-Free Method for Cell Annotation in High-Resolution Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Clonal evolution and transcriptional plasticity shape metastatic dissemination routes in prostate cancer.Nature communications · 2025Article
- Spatiotemporal analyses of the pan-cancer single-cell landscape reveal widespread profibrotic ecotypes associated with tumor immunity.Nature cancer · 2025Article
- Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors.Nature communications · 2025Article
- Out of distribution learning in bioinformatics: advancements and challenges.Briefings in bioinformatics · 2025Review
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration.Briefings in bioinformatics · 2024Article
- HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses.Nucleic acids research · 2023Article
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14 authors.
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Abstract
The rapid accumulation of single-cell RNA-seq data has provided rich resources to characterize various human cell populations. However, achieving accurate cell-type annotation using public references presents challenges due to inconsistent annotations, batch effects, and rare cell types. Here, we introduce SELINA (single-cell identity navigator), an integrative and automatic cell-type annotation framework based on a pre-curated reference atlas spanning various tissues. SELINA employs a multiple-adversarial domain adaptation network to remove batch effects within the reference dataset. Additionally, it enhances the annotation of less frequent cell types by synthetic minority oversampling and fits query data with the reference data using an autoencoder. SELINA culminates in the creation of a comprehensive and uniform reference atlas, encompassing 1.7 million cells covering 230 distinct human cell types. We substantiate its robustness and superiority across a multitude of human tissues. Notably, SELINA could accurately annotate cells within diverse disease contexts. SELINA provides a complete solution for human single-cell RNA-seq data annotation with both python and R packages.
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