SuperCellCyto: enabling efficient analysis of large scale cytometry datasets.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38589921", "kind": "science-record", "revision": 3, "content_hash": "sha256:9034e1fd8f1d1274bcf7bdb37c1630878c3a207acfaa250d956e712d37958921", "created_at": "2026-11-01T19:20:33Z", "updated_at": "2026-11-01T22:11:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "SuperCellCyto: enabling efficient analysis of large scale cytometry datasets.", "doi": "10.1186/s13059-024-03229-3", "pmid": "38589921", "authors": ["Givanna H Putri", "George Howitt", "Felix Marsh-Wakefield", "Thomas M Ashhurst", "Belinda Phipson"], "journal": "Genome biology", "year": 2024, "abstract": "Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).", "claims": [{"id": "claim-method", "statement": "We then performed a differential abundance test (taking the batch into account) using Propeller.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38589921", "locator": "sections[19] \"Differential expression and abundance analysis\" paragraph 3", "quote": "We then performed a differential abundance test (taking the batch into account) using Propeller."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Availability of data and materialsThe SuperCellCyto R package is publicly available on GitHub (https://github.com/phipsonlab/SuperCellCyto) [32] and Zenodo (https://zenodo.org/records/10521294) [33], along with extensive vignettes (https://phipsonlab.github.io/SuperCellCyto/) [34], providing a valuable resource for the research community to incorporate SuperCellCyto into their analysis pipelines more efficiently and effectively. Additionally, the complete analysis workflow associated with the results presented below can be accessed online on https://github.com/phipsonlab/SuperCellCyto-analysis [35].The SuperCellCyto implementation is available as an R package on GitHub (https://github.com/phipsonlab/SuperCellCyto) [32] and Zenodo (https://zenodo.org/records/10521294) [33] under GNU General Public License v3.0. Vignettes, function documentations, contributing guidelines, and installation instructions are available on https://phipsonlab.github.io/SuperCellCyto/ [34]. SuperCellCyto v0.1.0 (https://doi.org/10.5281/zenodo.10521294) [33] was used in this manuscript.All analysis performed in this paper is available as a workflowr [62] website at https://phipsonlab.github.io/SuperCellCyto-analysis/ [35], with the original source code available on GitHub (https://github.com/phipsonlab/SuperCellCyto-analysis) [35].All datasets used in the study have previously been published, with references available both within the article and in Additional file 1: Table S1. Data files required to reproduce the analyses are available on Zenodo (https://doi.org/10.5281/zenodo.8274907) [63]. Anti_PD1, BCR_XL, Levine_32dim, and Samusik_all data were downloaded using the HDCytoData R package [64] version 1.18.0. CITEseq data was downloaded from CellxGene [65].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38589921", "locator": "sections[34] \"notes\" paragraph 1", "quote": "Availability of data and materialsThe SuperCellCyto R package is publicly available on GitHub (https://github.com/phipsonlab/SuperCellCyto) [32] and Zenodo (https://zenodo.org/records/10521294) [33], along with extensive vignettes (https://phipsonlab.github.io/SuperCellCyto/) [34], providing a valuable resource for the research community to incorporate SuperCellCyto into their analysis pipelines more efficiently and effectively. Additionally, the complete analysis workflow associated with the results presented below can be accessed online on https://github.com/phipsonlab/SuperCellCyto-analysis [35].The SuperCellCyto implementation is available as an R package on GitHub (https://github.com/phipsonlab/SuperCellCyto) [32] and Zenodo (https://zenodo.org/records/10521294) [33] under GNU General Public License v3.0. Vignettes, function documentations, contributing guidelines, and installation instructions are available on https://phipsonlab.github.io/SuperCellCyto/ [34]. SuperCellCyto v0.1.0 (https://doi.org/10.5281/zenodo.10521294) [33] was used in this manuscript.All analysis performed in this paper is available as a workflowr [62] website at https://phipsonlab.github.io/SuperCellCyto-analysis/ [35], with the original source code available on GitHub (https://github.com/phipsonlab/SuperCellCyto-analysis) [35].All datasets used in the study have previously been published, with references available both within the article and in Additional file 1: Table S1. Data files required to reproduce the analyses are available on Zenodo (https://doi.org/10.5281/zenodo.8274907) [63]. Anti_PD1, BCR_XL, Levine_32dim, and Samusik_all data were downloaded using the HDCytoData R package [64] version 1.18.0. CITEseq data was downloaded from CellxGene [65]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "SuperCellCyto groups highly similar cells into supercells, reducing cytometry dataset size by 10- to 50-fold while preserving biological diversity and reducing downstream computational demands.", "kind": "quantitative", "target": {"value": 50.0, "unit": "fold change", "tolerance": null, "source": "This aggregation effectively reduces the dataset size by 10 to 50 times, while preserving biological diversity, thus alleviating the computational demands of downstream analysis."}, "evidence": [{"asset": "source:pmid:38589921", "locator": "sections[1] \"Background\" paragraph 6", "quote": "This aggregation effectively reduces the dataset size by 10 to 50 times, while preserving biological diversity, thus alleviating the computational demands of downstream analysis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "SuperCellCyto groups highly similar cells into supercells, reducing cytometry dataset size by 10- to 50-fold while preserving biological diversity and reducing downstream computational demands.", "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:38589921"], "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:38589921", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s13059-024-03229-3", "sha256": "sha256:396cbc4dae47b87a39960b486fa259d0925d3ec8e45140bebce2c1ababd145af", "bytes": 74506, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s13059-024-03229-3", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11003185", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "Principal Component Analysis"}}, {"id": "method-2", "family": "differential_expression", "specific": "edger", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "EdgeR"}}, {"id": "method-3", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "Limma"}}, {"id": "method-4", "family": "clustering", "specific": "hierarchical", "label": "Clustering", "dsl_operations": ["hierarchical_cluster"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "dendrogram"}}, {"id": "method-5", "family": null, "specific": null, "label": "Propeller", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We then performed a differential abundance test (taking the batch into account) using Propeller."}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "differential_expression_fit", "expected_value": 50.0, "unit": "fold change", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}], "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": "R1", "expressible": true, "required_operations": ["pca_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca."}, "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": ["Availability of data and materialsThe SuperCellCyto R package is publicly available on GitHub (https://github.com/phipsonlab/SuperCellCyto) [32] and Zenodo (https://zenodo.org/records/10521294) [33], along with extensive vignettes (https://phipsonlab.github.io/SuperCellCyto/) [34], providing a valuable resource for the research community to incorporate SuperCellCyto into their analysis pipelines more efficiently and effectively. Additionally, the complete analysis workflow associated with the results presented below can be accessed online on https://github.com/phipsonlab/SuperCellCyto-analysis [35].The SuperCellCyto implementation is available as an R package on GitHub (https://github.com/phipsonlab/SuperCellCyto) [32] and Zenodo (https://zenodo.org/records/10521294) [33] under GNU General Public License v3.0. Vignettes, function documentations, contributing guidelines, and installation instructions are available on https://phipsonlab.github.io/SuperCellCyto/ [34]. SuperCellCyto v0.1.0 (https://doi.org/10.5281/zenodo.10521294) [33] was used in this manuscript.All analysis performed in this paper is available as a workflowr [62] website at https://phipsonlab.github.io/SuperCellCyto-analysis/ [35], with the original source code available on GitHub (https://github.com/phipsonlab/SuperCellCyto-analysis) [35].All datasets used in the study have previously been published, with references available both within the article and in Additional file 1: Table S1. Data files required to reproduce the analyses are available on Zenodo (https://doi.org/10.5281/zenodo.8274907) [63]. Anti_PD1, BCR_XL, Levine_32dim, and Samusik_all data were downloaded using the HDCytoData R package [64] version 1.18.0. CITEseq data was downloaded from CellxGene [65]."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["pca_fit"], "assets": ["source:pmid:38589921"], "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": "2026-11-01T19:14:50Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 17670, "completion_tokens": 1997, "cached_tokens": 128}}, "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:38589921", "type": "paper", "label": "pmid:38589921"}, {"id": "source:pmid:38589921", "type": "manuscript", "label": "PMID 38589921 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38589921", "type": "paper", "label": "pmid:38589921"}, {"id": "source:pmid:38589921", "type": "manuscript", "label": "PMID 38589921 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38589921", "type": "extraction", "used": ["source:pmid:38589921"], "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:38589921", "parent_hash": "sha256:6d58db7ee7b51725ae8d5b3ce0d6dd69a56caa34a17a35da5859b909a82e4cf1", "fork_of": null, "generation": 2}, "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": 0.93, "evidence": ["xisting R packages, including those from the scRNAseq field such as Limma [40], EdgeR [41–43], or"], "order_basis": "paper_text_order:7435", "id": "assay:1"}, {"role": "analysis", "definition_id": "principal_component_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pca_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Principal Component Analysis"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}