MicroCellClust: mining rare and highly specific subpopulations from single-cell expression data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33830183", "kind": "science-record", "revision": 6, "content_hash": "sha256:322934dc9bfec1f7b55f023f8ab9af94ca64bcebc8b74cedb2bdce4b2ba16af0", "created_at": "2026-11-01T15:41:31Z", "updated_at": "2026-11-01T21:25:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "MicroCellClust: mining rare and highly specific subpopulations from single-cell expression data.", "doi": "10.1093/bioinformatics/btab239", "pmid": "33830183", "authors": ["Alexander Gerniers", "Orian Bricard", "Pierre Dupont"], "journal": "Bioinformatics (Oxford, England)", "year": 2021, "abstract": "MOTIVATION: Identifying rare subpopulations of cells is a critical step in order to extract knowledge from single-cell expression data, especially when the available data is limited and rare subpopulations only contain a few cells. In this paper, we present a data mining method to identify small subpopulations of cells that present highly specific expression profiles. This objective is formalized as a constrained optimization problem that jointly identifies a small group of cells and a corresponding subset of specific genes. The proposed method extends the max-sum submatrix problem to yield genes that are, for instance, highly expressed inside a small number of cells, but have a low expression in the remaining ones.\nRESULTS: We show through controlled experiments on scRNA-seq data that the MicroCellClust method achieves a high F1 score to identify rare subpopulations of artificially planted human T cells. The effectiveness of MicroCellClust is confirmed as it reveals a subpopulation of CD4 T cells with a specific phenotype from breast cancer samples, and a subpopulation linked to a specific stage in the cell cycle from breast cancer samples as well. Finally, three rare subpopulations in mouse embryonic stem cells are also identified with MicroCellClust. These results illustrate the proposed method outperforms typical alternatives at identifying small subsets of cells with highly specific expression profiles.\nAVAILABILITYAND IMPLEMENTATION: The R and Scala implementation of MicroCellClust is freely available on GitHub, at https://github.com/agerniers/MicroCellClust/ The data underlying this article are available on Zenodo, at https://dx.doi.org/10.5281/zenodo.4580332.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "We show through controlled experiments on scRNA-seq data that the MicroCellClust method achieves a high F1 score to identify rare subpopulations of artificially planted human T cells.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33830183", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We show through controlled experiments on scRNA-seq data that the MicroCellClust method achieves a high F1 score to identify rare subpopulations of artificially planted human T cells."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this article are available on Zenodo, at https://dx.doi.org/10.5281/zenodo.4580332.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33830183", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The data underlying this article are available on Zenodo, at https://dx.doi.org/10.5281/zenodo.4580332."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "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:33830183"], "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": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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