ArticleBMC bioinformatics2019
Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool.bioRxiv : the preprint server for biology · 2026Article
- Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.Biochemistry and biophysics reports · 2026Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Community assessment of methods to deconvolve cellular composition from bulk gene expression.Nature communications · 2024Article
- Improving replicability in single-cell RNA-Seq cell type discovery with Dune.BMC bioinformatics · 2024Article
- TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.Briefings in bioinformatics · 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
- A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2022Article
- Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells.Scientific reports · 2022Article
- A neural network-based method for exhaustive cell label assignment using single cell RNA-seq data.Scientific reports · 2022Article
- scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data.BMC bioinformatics · 2022Article
- MarkerCount: A stable, count-based cell type identifier for single-cell RNA-seq experiments.Computational and structural biotechnology journal · 2022Article
- Automatic cell type identification methods for single-cell RNA sequencing.Computational and structural biotechnology journal · 2021Review
- Article
- Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data.BMC bioinformatics · 2020Article
- CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments.BMC bioinformatics · 2020Article
- Modeling disease progression in Multiple Myeloma with Hopfield networks and single-cell RNA-seq.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
- A comparison of automatic cell identification methods for single-cell RNA sequencing data.Genome biology · 2019Article
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6 authors.
Funding
Abstract
backgroundSingle cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq, and unsupervised clustering combined with Principal Component Analysis (PCA) can be used to overcome this limitation. After clustering, however, one has to interpret the average expression of markers on each cluster to identify the corresponding cell types, and this is normally done by hand by an expert curator.
resultsWe present a computational tool for processing single cell RNA-seq data that uses a voting algorithm to automatically identify cells based on approval votes received by known molecular markers. Using a stochastic procedure that accounts for imbalances in the number of known molecular signatures for different cell types, the method computes the statistical significance of the final approval score and automatically assigns a cell type to clusters without an expert curator. We demonstrate the utility of the tool in the analysis of eight samples of bone marrow from the Human Cell Atlas. The tool provides a systematic identification of cell types in bone marrow based on a list of markers of immune cell types, and incorporates a suite of visualization tools that can be overlaid on a t-SNE representation. The software is freely available as a Python package at https://github.com/sdomanskyi/DigitalCellSorter .
conclusionsThis methodology assures that extensive marker to cell type matching information is taken into account in a systematic way when assigning cell clusters to cell types. Moreover, the method allows for a high throughput processing of multiple scRNA-seq datasets, since it does not involve an expert curator, and it can be applied recursively to obtain cell sub-types. The software is designed to allow the user to substitute the marker to cell type matching information and apply the methodology to different cellular environments.
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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.