ArticleNature communications2022
Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 501 papers, 1 of them a synthesis that pooled it.
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Who cites it
501 citing papers in PubMed, 1 synthesis or guideline pooled it, 738 citations in OpenAlex.
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- CellMAGE: cell-type deconvolution for multi-parent population analysis of gene expression.bioRxiv : the preprint server for biology · 2026Article
- Cell-type-specific dysregulated gene expression in the frontal cortex of an Angelman syndrome pig model.Human molecular genetics · 2026Article
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- ENT1 inhibition links oligodendrocyte lipid metabolism to connectivity in tauopathy.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Data-driven identification of cell subtypes for single-cell transcriptomic data with Subtypist.Briefings in bioinformatics · 2026Article
- Hippocampal neural stem cell-derived extracellular vesicles modulate microglia to promote resilience against tau oligomers.Neural regeneration research · 2026Article
- Single-Cell Transcriptome of Anaplastic Thyroid Cancer Reveals Immunosuppressive Tumor Microenvironment Remodeling.Cancer medicine · 2026Article
- SANNO: A Graph-Transformer Enhanced Optimal Transport Tool for Spatial Transcriptomic Annotation.Interdisciplinary sciences, computational life sciences · 2026Article
441 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be informative of both the individual cell clusters and various cell types present in the sample. Here, we developed a computational platform, ScType, which enables a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information. Using six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides unbiased and accurate cell type annotations by guaranteeing the specificity of positive and negative marker genes across cell clusters and cell types. We also demonstrate how ScType distinguishes between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for anticancer applications. The widely applicable method is deployed both as an interactive web-tool ( https://sctype.app ), and as an open-source R-package.
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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.