scCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38243719", "kind": "science-record", "revision": 4, "content_hash": "sha256:5fdc6892d93c152e124682a42e8d99fbfcf9bcfe43e0cfbb0974308788f45c20", "created_at": "2026-11-01T19:20:09Z", "updated_at": "2026-11-01T22:53:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "scCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution.", "doi": "10.1093/bioinformatics/btae028", "pmid": "38243719", "authors": ["Zeyu Chen", "Yuxin Miao", "Zhiyuan Tan", "Qifan Hu", "Yanhong Wu", "Xinqi Li", "Wenbo Guo", "Jin Gu"], "journal": "Bioinformatics (Oxford, England)", "year": 2024, "abstract": "SUMMARY: Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in the tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes in several tumor types, there is a great need to computationally transfer these labels to new datasets. Also, different studies used different approaches or criteria to define the cell subtypes for the same major cell lineages. The relationships between the cell subtypes defined in different studies should be carefully evaluated. In this updated package scCancer2, designed for integrative tumor scRNA-seq data analysis, we developed a supervised machine learning framework to annotate TME cells with annotated cell subtypes from 15 scRNA-seq datasets with 594 samples in total. Based on the trained classifiers, we quantitatively constructed the similarity maps between the cell subtypes defined in different references by testing on all the 15 datasets. Secondly, to improve the identification of malignant cells, we designed a classifier by integrating large-scale pan-cancer TCGA bulk gene expression datasets and scRNA-seq datasets (10 cancer types, 175 samples, 663 857 cells). This classifier shows robust performances when no internal confidential reference cells are available. Thirdly, scCancer2 integrated a module to process the spatial transcriptomic data and analyze the spatial features of TME.\nAVAILABILITY AND IMPLEMENTATION: The package and user documentation are available at http://lifeome.net/software/sccancer2/ and https://doi.org/10.5281/zenodo.10477296.", "claims": [{"id": "claim-method", "statement": "We divided the training set and validation set by sample sources (Supplementary Table S4) and trained an XGBoost model with a binary logistic objective function.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38243719", "locator": "sections[10] \"2.2.3 Model training and performance evaluation\" paragraph 2", "quote": "We divided the training set and validation set by sample sources (Supplementary Table S4) and trained an XGBoost model with a binary logistic objective function."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Supplementary data are available at Bioinformatics online.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38243719", "locator": "sections[26] \"Supplementary data\" paragraph 2", "quote": "Supplementary data are available at Bioinformatics online."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The XGBoost classifier shows robust performances when no internal confidential reference cells are available.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38243719", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "This classifier shows robust performances when no internal confidential reference cells are available."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The XGBoost classifier shows robust performances when no internal confidential reference cells are available.", "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:38243719"], "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. 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