ArticleBioinformatics (Oxford, England)2021
CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- scHilda: Hierarchical Integration of LLM with KG database for single cell type annotation.PLoS computational biology · 2026Article
- scSemiPLC: a semi-supervised learning framework for annotating single-cell RNA-Seq data by generating pseudo-labels through clustering.mSystems · 2026Article
- Computational tracking of cell origins using CellSexID from single-cell transcriptomes.Cell reports methods · 2025Article
- Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease.Nature communications · 2025Article
- HiCat: a semi-supervised approach for cell type annotation.Briefings in bioinformatics · 2025Article
- Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease.bioRxiv : the preprint server for biology · 2025Article
- An overview of computational methods in single-cell transcriptomic cell type annotation.Briefings in bioinformatics · 2025Review
- Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data.Journal of chemical information and modeling · 2025Article
- The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.Nature communications · 2024Article
- scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision.Bioinformatics (Oxford, England) · 2024Article
- CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.Nucleic acids research · 2024Article
- A cell marker-based clustering strategy (cmCluster) for precise cell type identification of scRNA-seq data.Quantitative biology (Beijing, China) · 2023Article
- Automatic Cell Type Annotation Using Marker Genes for Single-Cell RNA Sequencing Data.Biomolecules · 2022Article
- scMAGIC: accurately annotating single cells using two rounds of reference-based classification.Nucleic acids research · 2022Article
- LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell transcriptomes.Frontiers in genetics · 2022Article
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2 authors.
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Abstract
motivationSingle-cell RNA sequencing (scRNA-seq) technology has been widely applied to capture the heterogeneity of different cell types within complex tissues. An essential step in scRNA-seq data analysis is the annotation of cell types. Traditional cell-type annotation is mainly clustering the cells first, and then using the aggregated cluster-level expression profiles and the marker genes to label each cluster. Such methods are greatly dependent on the clustering results, which are insufficient for accurate annotation.
resultsIn this article, we propose a semi-supervised learning method for cell-type annotation called CALLR. It combines unsupervised learning represented by the graph Laplacian matrix constructed from all the cells and supervised learning using sparse logistic regression. By alternately updating the cell clusters and annotation labels, high annotation accuracy can be achieved. The model is formulated as an optimization problem, and a computationally efficient algorithm is developed to solve it. Experiments on 10 real datasets show that CALLR outperforms the compared (semi-)supervised learning methods, and the popular clustering methods. AVAILABILITY AND IMPLEMENTATION: The implementation of CALLR is available at https://github.com/MathSZhang/CALLR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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