Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33520459", "kind": "science-record", "revision": 4, "content_hash": "sha256:2814adc35b4b758e97e35a7ccd1a7ea55407d89681b452fc99245a792a84ca66", "created_at": "2026-11-01T15:41:24Z", "updated_at": "2026-11-01T21:24:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics.", "doi": "10.7717/peerj.10670", "pmid": "33520459", "authors": ["Sergii Domanskyi", "Alex Hakansson", "Thomas J Bertus", "Giovanni Paternostro", "Carlo Piermarocchi"], "journal": "PeerJ", "year": 2021, "abstract": "MOTIVATION: Analysis 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.\nRESULTS: We 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.\nAVAILABILITY: 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.\nSUPPLEMENTARY INFORMATION: Supplemental Materials are available at PeerJ online.", "claims": [{"id": "claim-method", "statement": "To quantify each method’s performance we calculated a multi-class weighted F1 score, excluding unassigned cells, and took the median value over 100 independent random mixtures.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33520459", "locator": "sections[11] \"Cell identification in mixtures\" paragraph 2", "quote": "To quantify each method’s performance we calculated a multi-class weighted F1 score, excluding unassigned cells, and took the median value over 100 independent random mixtures."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:\nDigital Cell Sorter is available at GitHub: https://github.com/sdomanskyi/DigitalCellSorter.\nSource Code is available at Zenodo:\nSergii Domanskyi, & wangjiayin1010. (2020, November 26). sdomanskyi/DigitalCellSorter: DigitalCellSorter (Version 1.3.7). Zenodo. DOI 10.5281/zenodo.4292550.\nThe documentation is available at DigitalCellSorter:\nDomanskyi S., Szedlak A., Hawkins N.T., Wang J., Bertus T., Hakansson A., Paternostro G., Piermarocchi C. (2020). DigitalCellSorter’s documentation. Available at https://digital-cell-sorter.rtfd.io.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33520459", "locator": "sections[26] \"Data Availability Statement\" paragraphs 1-7", "quote": "The following information was supplied regarding data availability:\nDigital Cell Sorter is available at GitHub: https://github.com/sdomanskyi/DigitalCellSorter.\nSource Code is available at Zenodo:\nSergii Domanskyi, & wangjiayin1010. (2020, November 26). sdomanskyi/DigitalCellSorter: DigitalCellSorter (Version 1.3.7). Zenodo. DOI 10.5281/zenodo.4292550.\nThe documentation is available at DigitalCellSorter:\nDomanskyi S., Szedlak A., Hawkins N.T., Wang J., Bertus T., Hakansson A., Paternostro G., Piermarocchi C. (2020). DigitalCellSorter’s documentation. Available at https://digital-cell-sorter.rtfd.io."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33520459"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33520459", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.10670", "sha256": "sha256:ce5bf2818f8dfe4d8ea489177e219d4fdaa6d117905cd6c7901f387ff5b18183", "bytes": 56436, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.10670", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7811293", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:33520459:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:6a8146cb1a01a03d13747950c3899c15410eab93a8a662f0231c8abecf86156b", "bytes": 555, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#table-1", "version": null, "note": "Performance of annotation methods evaluated using mixtures of pure cell type populations.Median multi-class F1 and percentage of unassigned cells (U) using two lists of markers as knowledge-base.", "structure": {"pmcid": "PMC7811293", "section_index": 11, "section_title": "Cell identification in mixtures", "row_count": 4, "column_count": 7}, "table": {"label": "Table 1", "caption": "Performance of annotation methods evaluated using mixtures of pure cell type populations.Median multi-class F1 and percentage of unassigned cells (U) using two lists of markers as knowledge-base.", "rows": [["Method", "pDCS", "Hopfield", "Consensus"], ["Metric", "F1", "U (%)", "F1", "U (%)", "F1", "U (%)"], ["CD Marker", "0.836", "6.0", "0.867", "16.7", "0.947", "21.6"], ["CIBERSORT", "0.740", "6.6", "0.942", "22.3", "0.944", "22.6"]], "row_count": 4, "column_count": 7, "numeric_cells": 12, "header_rows": 2, "locator": "section[11]/table-wrap[1]#table-1"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "multi-class weighted F1 score", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To quantify each method’s performance we calculated a multi-class weighted F1 score, excluding unassigned cells, and took the median value over 100 independent random mixtures."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (multi-class weighted F1 score), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The following information was supplied regarding data availability:\nDigital Cell Sorter is available at GitHub: https://github.com/sdomanskyi/DigitalCellSorter.\nSource Code is available at Zenodo:\nSergii Domanskyi, & wangjiayin1010. (2020, November 26). sdomanskyi/DigitalCellSorter: DigitalCellSorter (Version 1.3.7). Zenodo. DOI 10.5281/zenodo.4292550.\nThe documentation is available at DigitalCellSorter:\nDomanskyi S., Szedlak A., Hawkins N.T., Wang J., Bertus T., Hakansson A., Paternostro G., Piermarocchi C. (2020). DigitalCellSorter’s documentation. Available at https://digital-cell-sorter.rtfd.io."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:33520459", "asset:table:pmid:33520459:1"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:26:46Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 3, "usage": {"prompt_tokens": 41304, "completion_tokens": 2467, "cached_tokens": 784}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:33520459", "type": "paper", "label": "pmid:33520459"}, {"id": "source:pmid:33520459", "type": "manuscript", "label": "PMID 33520459 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:33520459", "type": "paper", "label": "pmid:33520459"}, {"id": "source:pmid:33520459", "type": "manuscript", "label": "PMID 33520459 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-33520459", "type": "extraction", "used": ["source:pmid:33520459"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:33520459", "parent_hash": "sha256:fa4931ec79cc333e15ba2fe4ffa8d2a4a217b9c1524d0be3899614a752680001", "fork_of": null, "generation": 3}, "gaps": ["execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["tection, and Hopfield landscapes toolkit for single-cell transcriptomics.", "TION: Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps inclu", "terization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel al", "es and provides a self-contained toolkit for scRNA-seq analysis. RESULTS: We present a suite of sof"], "order_basis": "paper_text_order:65", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Hopfield landscapes toolkit for single-cell transcriptomics.", "MOTIVATION: Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different"], "order_basis": "paper_text_order:78", "id": "assay:2"}]}