ArticleNature communications2021
Hierarchical progressive learning of cell identities in single-cell data.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
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- Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.Biochemistry and biophysics reports · 2026Article
- Cross-dataset annotation harmonization for cell-type hierarchy construction.Bioinformatics (Oxford, England) · 2026Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- Federated single-cell QTL meta-analysis reveals novel disease mechanisms.bioRxiv : the preprint server for biology · 2026Article
- ecPICK: A deep learning-enabled spatial diagnostic platform for direct ecDNA identification and clinical prognosis across pan-cancer histopathology.Theranostics · 2026Article
- Article
- Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.bioRxiv : the preprint server for biology · 2025Article
- GIPR agonism and antagonism decrease body weight and food intake via different mechanisms in male mice.Nature metabolism · 2025Article
- M-band wavelet-based multi-view clustering of cells.PLoS computational biology · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Considerations for building and using integrated single-cell atlases.Nature methods · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Detecting global and local hierarchical structures in cell-cell communication using CrossChat.Nature communications · 2024Article
- Immune landscape of oncohistone-mutant gliomas reveals diverse myeloid populations and tumor-promoting function.Nature communications · 2024Article
- TCfinder: Robust tumor cell discrimination in scRNA-seq based on gene pathway activity.iMetaOmics · 2024Article
- A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data.Genome research · 2024Article
- scPLAN: a hierarchical computational framework for single transcriptomics data annotation, integration and cell-type label refinement.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Supervised methods are increasingly used to identify cell populations in single-cell data. Yet, current methods are limited in their ability to learn from multiple datasets simultaneously, are hampered by the annotation of datasets at different resolutions, and do not preserve annotations when retrained on new datasets. The latter point is especially important as researchers cannot rely on downstream analysis performed using earlier versions of the dataset. Here, we present scHPL, a hierarchical progressive learning method which allows continuous learning from single-cell data by leveraging the different resolutions of annotations across multiple datasets to learn and continuously update a classification tree. We evaluate the classification and tree learning performance using simulated as well as real datasets and show that scHPL can successfully learn known cellular hierarchies from multiple datasets while preserving the original annotations. scHPL is available at https://github.com/lcmmichielsen/scHPL .
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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.