ArticleNature communications2023
Cellcano: supervised cell type identification for single cell ATAC-seq 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 35 papers.
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Who cites it
35 citing papers in PubMed.
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- ScanNet: Single-cell annotation informed by transcriptional regulation Network via iterative heterogeneous graph learning.PLoS computational biology · 2026Article
- ATACompass enables cross-species cell identity mapping for scATAC-seq without gene annotations.Cell reports methods · 2026Article
- scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.Genomics, proteomics & bioinformatics · 2026Article
- Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation.PLoS computational biology · 2026Article
- Genome- and peak-informed two-stage framework for scATAC-seq cell type identification.Bioinformatics (Oxford, England) · 2026Article
- Integrative transcriptome-wide association analyses reveal PRKCG-linked GABAergic dysfunction in Fragile X-associated tremor/ataxia syndrome.Nature communications · 2026Article
- Human-scATAC-Corpus: a comprehensive database of scATAC-seq data.Nucleic acids research · 2026Article
- CellPredX, a computational framework for cross-data type, cross-sample, and cross-protocol cell type annotation through domain adaptation and deep metric learning.PLoS computational biology · 2026Article
- Article
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- scDIFF: automatic cell type annotation using scATAC-seq data by incorporating bulk-level genomic and epigenomic information in a deep diffusive transformer.Briefings in bioinformatics · 2025Article
- EpiAgent: foundation model for single-cell epigenomics.Nature methods · 2025Article
- scKAN: interpretable single-cell analysis for cell-type-specific gene discovery and drug repurposing via Kolmogorov-Arnold networks.Genome biology · 2025Article
- scMultiNODE: Integrative and Scalable Framework for Multi-Modal Temporal Single-Cell Data.bioRxiv : the preprint server for biology · 2025Article
- Cell-Type Annotation for scATAC-Seq Data by Integrating Chromatin Accessibility and Genome Sequence.Biomolecules · 2025Article
- TORC: Target-Oriented Reference Construction for supervised cell-type identification in scRNA-seq.Genome biology · 2025Article
- annATAC: automatic cell type annotation for scATAC-seq data based on language model.BMC biology · 2025Article
- ScInfeR: an efficient method for annotating cell types and sub-types in single-cell RNA-seq, ATAC-seq, and spatial omics.Briefings in bioinformatics · 2025Article
- New horizons at the interface of artificial intelligence and translational cancer research.Cancer cell · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Computational cell type identification is a fundamental step in single-cell omics data analysis. Supervised celltyping methods have gained increasing popularity in single-cell RNA-seq data because of the superior performance and the availability of high-quality reference datasets. Recent technological advances in profiling chromatin accessibility at single-cell resolution (scATAC-seq) have brought new insights to the understanding of epigenetic heterogeneity. With continuous accumulation of scATAC-seq datasets, supervised celltyping method specifically designed for scATAC-seq is in urgent need. Here we develop Cellcano, a computational method based on a two-round supervised learning algorithm to identify cell types from scATAC-seq data. The method alleviates the distributional shift between reference and target data and improves the prediction performance. After systematically benchmarking Cellcano on 50 well-designed celltyping tasks from various datasets, we show that Cellcano is accurate, robust, and computationally efficient. Cellcano is well-documented and freely available at https://marvinquiet.github.io/Cellcano/ .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.