ArticleNucleic acids research2022
scMAGIC: accurately annotating single cells using two rounds of reference-based classification.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Seesaw signatures capture trajectory-like transcriptomic shifts and enable compact tumour cell classification across cancers.Communications biology · 2026Article
- A multi-omic atlas in the African turquoise killifish reveals increased glucocorticoid signaling as a hallmark of brain aging.bioRxiv : the preprint server for biology · 2026Article
- A spatially resolved human glioblastoma atlas reveals distinct cellular and molecular patterns of anatomical niches.Nature communications · 2026Article
- Single-nuclei RNA-sequencing uncovers sexually divergent exercise signatures partially mimicked by TFEB overexpression in mouse skeletal muscle.bioRxiv : the preprint server for biology · 2026Article
- scSorterDL: a deep neural network-enhanced ensemble LDAs for single cell classifications.Briefings in bioinformatics · 2025Article
- HiCat: a semi-supervised approach for cell type annotation.Briefings in bioinformatics · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- scATAcat: cell-type annotation for scATAC-seq data.NAR genomics and bioinformatics · 2024Article
- A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data.Genome research · 2024Article
- Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census.Bioinformatics (Oxford, England) · 2023Article
- CellAnn: a comprehensive, super-fast, and user-friendly single-cell annotation web server.Bioinformatics (Oxford, England) · 2023Article
- Cell-type annotation with accurate unseen cell-type identification using multiple references.PLoS computational biology · 2023Article
- Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.Computational and structural biotechnology journal · 2023Review
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5 authors.
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Abstract
Here, we introduce scMAGIC (Single Cell annotation using MArker Genes Identification and two rounds of reference-based Classification [RBC]), a novel method that uses well-annotated single-cell RNA sequencing (scRNA-seq) data as the reference to assist in the classification of query scRNA-seq data. A key innovation in scMAGIC is the introduction of a second-round RBC in which those query cells whose cell identities are confidently validated in the first round are used as a new reference to again classify query cells, therefore eliminating the batch effects between the reference and the query data. scMAGIC significantly outperforms 13 competing RBC methods with their optimal parameter settings across 86 benchmark tests, especially when the cell types in the query dataset are not completely covered by the reference dataset and when there exist significant batch effects between the reference and the query datasets. Moreover, when no reference dataset is available, scMAGIC can annotate query cells with reasonably high accuracy by using an atlas dataset as the reference.
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