ArticlePeerJ2021
Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics.
Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Consensus representation of multiple cell-cell graphs from gene signaling pathways for cell type annotation.BMC biology · 2025Article
- Advancing automated cell type annotation with large language models and single-cell isoform sequencing.Computational and structural biotechnology journal · 2025Review
- Community assessment of methods to deconvolve cellular composition from bulk gene expression.Nature communications · 2024Article
- Identification of ulcerative colitis-specific immune cell signatures from public single-cell RNA-seq data.Genes & genomics · 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
- Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells.Scientific reports · 2022Article
- Appyters: Turning Jupyter Notebooks into data-driven web apps.Patterns (New York, N.Y.) · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
motivationAnalysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.e., without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. These new approaches are integrated in a software platform that includes many other state-of-the-art methodologies and provides a self-contained toolkit for scRNA-seq analysis.
resultsWe present a suite of software elements for the analysis of scRNA-seq data. This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. We illustrate the capability of the software using data from large datasets of peripheral blood mononuclear cells (PBMC), as well as plasma cells of bone marrow samples from healthy donors and multiple myeloma patients. We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data. AVAILABILITY: The DCS toolkit is available for download and installation through the Python Package Index (PyPI). The software can be deployed using the Python import function following installation. Source code is also available for download on Zenodo: DOI 10.5281/zenodo.2533377. SUPPLEMENTARY INFORMATION: Supplemental Materials are available at PeerJ online.
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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.