scCross: efficient search for rare subpopulations across multiple single-cell samples.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38889273", "kind": "science-record", "revision": 2, "content_hash": "sha256:daaa673e47f5b87ab2629fd0861354e95f77da443b0c14b4c7ec1161d6ca9f51", "created_at": "2026-11-01T20:28:57Z", "updated_at": "2026-11-01T21:33:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "scCross: efficient search for rare subpopulations across multiple single-cell samples.", "doi": "10.1093/bioinformatics/btae371", "pmid": "38889273", "authors": ["Alexander Gerniers", "Siegfried Nijssen", "Pierre Dupont"], "journal": "Bioinformatics (Oxford, England)", "year": 2024, "abstract": "MOTIVATION: Identifying rare cell types is an important task to capture the heterogeneity of single-cell data, such as scRNA-seq. The widespread availability of such data enables to aggregate multiple samples, corresponding for example to different donors, into the same study. Yet, such aggregated data is often subject to batch effects between samples. Clustering it therefore generally requires the use of data integration methods, which can lead to overcorrection, making the identification of rare cells difficult. We present scCross, a biclustering method identifying rare subpopulations of cells present across multiple single-cell samples. It jointly identifies a group of cells with specific marker genes by relying on a global sum criterion, computed over entire subpopulation of cells, rather than pairwise comparisons between individual cells. This proves robust with respect to the high variability of scRNA-seq data, in particular batch effects.\nRESULTS: We show through several case studies that scCross is able to identify rare subpopulations across multiple samples without performing prior data integration. Namely, it identifies a cilium subpopulation with potential new ciliary genes from lung cancer cells, which is not detected by typical alternatives. It also highlights rare subpopulations in human pancreas samples sequenced with different protocols, despite visible shifts in expression levels between batches. We further show that scCross outperforms typical alternatives at identifying a target rare cell type in a controlled experiment with artificially created batch effects. This shows the ability of scCross to efficiently identify rare cell subpopulations characterized by specific genes despite the presence of batch effects.\nAVAILABILITY AND IMPLEMENTATION: The R and Scala implementation of scCross is freely available on GitHub, at https://github.com/agerniers/scCross/. A snapshot of the code and the data underlying this article are available on Zenodo, at https://zenodo.org/doi/10.5281/zenodo.10471063.", "claims": [{"id": "claim-method", "statement": "We present scCross, a biclustering method identifying rare subpopulations of cells present across multiple single-cell samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38889273", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We present scCross, a biclustering method identifying rare subpopulations of cells present across multiple single-cell samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Supplementary data are available at Bioinformatics online.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38889273", "locator": "sections[13] \"Supplementary data\" paragraph 2", "quote": "Supplementary data are available at Bioinformatics online."}], "state": "complete"}, {"id": "claim-target-result", "statement": "scCross consistently achieves a high F1 score (≥90%), making it the best performing approach in six out of the nine cases.", "kind": "quantitative", "target": {"value": 90.0, "unit": "per cent", "tolerance": null, "source": "scCross consistently achieves a high F1 score (≥90%), making it the best performing approach in six out of the nine cases."}, "evidence": [{"asset": "source:pmid:38889273", "locator": "sections[5] \"3.2 scCross outperforms alternatives in a controlled Jurkat v. 293 T experiment\" paragraph 6", "quote": "scCross consistently achieves a high F1 score (≥90%), making it the best performing approach in six out of the nine cases."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "scCross consistently achieves a high F1 score (≥90%), making it the best performing approach in six out of the nine cases.", "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:38889273"], "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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