ArticleScience advances2020
Learning for single-cell assignment.
Article in Science advances, 2020. 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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The trial behind it
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Who cites it
15 citing papers in PubMed.
- SpaPheno: linking spatial transcriptomics to clinical phenotypes with interpretable machine learning.Genome medicine · 2026Article
- scGSDR: Harnessing gene semantics for single-cell pharmacological profiling.Communications biology · 2025Article
- An overview of computational methods in single-cell transcriptomic cell type annotation.Briefings in bioinformatics · 2025Review
- Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization.Cancer biology & medicine · 2025Review
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Immune landscape of oncohistone-mutant gliomas reveals diverse myeloid populations and tumor-promoting function.Nature communications · 2024Article
- scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data.Genome biology · 2024Article
- scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics.Briefings in bioinformatics · 2024Article
- Multi-slice spatial transcriptome domain analysis with SpaDo.Genome biology · 2024Article
- Characterization of human pluripotent stem cell differentiation by single-cell dual-omics analyses.Stem cell reports · 2023Article
- Privacy-preserving integration of multiple institutional data for single-cell type identification with scPrivacy.Science China. Life sciences · 2023Article
- Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing.Briefings in bioinformatics · 2023Article
- Transformer for one stop interpretable cell type annotation.Nature communications · 2023Article
- Automatic cell type identification methods for single-cell RNA sequencing.Computational and structural biotechnology journal · 2021Review
- Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science.IEEE/ACM transactions on computational biology and bioinformaticsReview
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Efficient single-cell assignment without prior marker gene annotations is essential for single-cell sequencing data analysis. Current methods, however, have limited effectiveness for distinct single-cell assignment. They failed to achieve a well-generalized performance in different tasks because of the inherent heterogeneity of different single-cell sequencing datasets and different single-cell types. Furthermore, current methods are inefficient to identify novel cell types that are absent in the reference datasets. To this end, we present scLearn, a learning-based framework that automatically infers quantitative measurement/similarity and threshold that can be used for different single-cell assignment tasks, achieving a well-generalized assignment performance on different single-cell types. We evaluated scLearn on a comprehensive set of publicly available benchmark datasets. We proved that scLearn outperformed the comparable existing methods for single-cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single-cell type identification and categorizing ability.
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Registered trials
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.