ArticleComputational and structural biotechnology journal2022
MarkerCount: A stable, count-based cell type identifier for single-cell RNA-seq experiments.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool.bioRxiv : the preprint server for biology · 2026Article
- scGSDR: Harnessing gene semantics for single-cell pharmacological profiling.Communications biology · 2025Article
- Consensus representation of multiple cell-cell graphs from gene signaling pathways for cell type annotation.BMC biology · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication.Computational and structural biotechnology journal · 2024Article
- A self-training interpretable cell type annotation framework using specific marker gene.Bioinformatics (Oxford, England) · 2024Article
- scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data.Genome biology · 2024Article
- Article
- Identification of ulcerative colitis-specific immune cell signatures from public single-cell RNA-seq data.Genes & genomics · 2023Article
- A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data.Communications biology · 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
- Role of lymphoid lineage cells aberrantly expressing alarmins S100A8/A9 in determining the severity of COVID-19.Genes & genomics · 2023Article
- Transformer for one stop interpretable cell type annotation.Nature communications · 2023Article
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Authors and funding
4 authors.
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Abstract
Cell type identification is a key step toward downstream analysis of single cell RNA-seq experiments. Although the primary objective is to identify known cell populations, good identifiers should also recognize unknown clusters which may represent a previously unidentified subpopulation of a known cell type or tumor cells of an unknown phenotype. Herein, we present MarkerCount, which utilizes the number of expressed markers, regardless of their expression level. MarkerCount works in both reference- and marker-based mode, where the latter utilizes existing lists of markers, while the former uses a pre-annotated dataset to find markers to be used for cell type identification. In both modes, MarkerCount first utilizes the "marker count" to identify cell populations and, after rejecting uncertain cells, reassigns cell type and/or makes corrections in cluster-basis. The performance of MarkerCount was evaluated and compared with existing identifiers, both marker- and reference-based, that can be customized using publicly available datasets and marker databases. The results show that MarkerCount performs better in the identification of known populations as well as of unknown ones, when compared to other reference- and marker-based cell type identifiers for most of the datasets analyzed.
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