Data standards for single-cell RNA-sequencing of paediatric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40416408", "kind": "science-record", "revision": 2, "content_hash": "sha256:640031a0d54d34ca602d6b4c142e9a31acf651f28319ba167e5759cc2fdd7e0b", "created_at": "2026-11-01T20:30:33Z", "updated_at": "2026-11-01T21:35:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Data standards for single-cell RNA-sequencing of paediatric cancer.", "doi": "10.1002/cti2.70033", "pmid": "40416408", "authors": ["Xiaohan Xu", "John Saxon", "Megan Sioe Fei Soon", "Colin Yc Lee", "Zewen Kelvin Tuong"], "journal": "Clinical & translational immunology", "year": 2025, "abstract": "Single-cell RNA sequencing (scRNA-seq) is a powerful tool for investigating paediatric cancers, but individual studies often profile a small number of individuals. It is now the standard practice to upload the scRNA-seq data to data repositories to support scientific reproducibility. Public data deposition is a cost-effective and sustainability-conscious solution that allows any researcher to download and analyse existing scRNA-seq data to develop new ideas. This is incredibly valuable, especially in the context of paediatric cancer research, where access to funding and to patient cohorts may be prohibitive. However, standards for data deposition are absent, leading to significant issues that may slow progress. As a consequence, it is difficult, even impossible, for other researchers to validate findings or utilise these data for tailored analyses. Here, we systematically accessed and reviewed publicly available scRNA-seq data sets from various paediatric cancer studies, covering over 1.3 million cells across 488 clinical samples. We highlight striking inconsistencies with study design and data availability across several levels, which hinder downstream analyses and data reproducibility. To address these challenges, we propose a recommendations framework to improve data deposition practices that promote more effective use of scRNA-seq data sets deposited on public repositories and accelerate discoveries in paediatric cancer research and beyond. We urge data standards institutes and repositories, such as NCBI Gene Expression Omnibus (GEO) and European Genome-Phenome Archive (EGA), to strictly enforce these standardised data practices.", "claims": [{"id": "claim-method", "statement": "We implemented a Gaussian Mixture Model (GMM) from the scikit‐learn library (v1.4.1) with two components to classify the cells into two discrete clusters based on the mitochondrial content of the cells and total number of read counts, allowing up to 1000 iterations for convergence.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40416408", "locator": "sections[3] \"Data processing and analysis\" paragraph 2", "quote": "We implemented a Gaussian Mixture Model (GMM) from the scikit‐learn library (v1.4.1) with two components to classify the cells into two discrete clusters based on the mitochondrial content of the cells and total number of read counts, allowing up to 1000 iterations for convergence."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The Jupyter Notebooks for extracting gene expression matrices from accessible data sets and preprocessing and meta‐analysis of constructed pan‐cancer scRNA‐seq object are available on GitHub at https://github.com/tuonglab/pan‐paedcancer‐scrnaseq. These data were derived from the resources available in the public domain, as listed in Table 1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40416408", "locator": "sections[15] \"Data availability statement\" paragraph 2", "quote": "The Jupyter Notebooks for extracting gene expression matrices from accessible data sets and preprocessing and meta‐analysis of constructed pan‐cancer scRNA‐seq object are available on GitHub at https://github.com/tuonglab/pan‐paedcancer‐scrnaseq. These data were derived from the resources available in the public domain, as listed in Table 1."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We highlight striking inconsistencies with study design and data availability across several levels, which hinder downstream analyses and data reproducibility.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40416408", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We highlight striking inconsistencies with study design and data availability across several levels, which hinder downstream analyses and data reproducibility."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We highlight striking inconsistencies with study design and data availability across several levels, which hinder downstream analyses and data reproducibility.", "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:40416408"], "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:40416408", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/cti2.70033", "sha256": "sha256:e9ac907ba86537b4b7c6d324034c1359147b1bb21dec38ff68a65a8d1278a57c", "bytes": 34163, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1002/cti2.70033", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12101384", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "Gaussian Mixture Model (GMM)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We implemented a Gaussian Mixture Model (GMM) from the scikit‐learn library (v1.4.1) with two components to classify the cells into two discrete clusters based on the mitochondrial content of the cells and total number of read counts, allowing up to 1000 iterations for convergence."}}], "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 (Gaussian Mixture Model (GMM)), 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 Jupyter Notebooks for extracting gene expression matrices from accessible data sets and preprocessing and meta‐analysis of constructed pan‐cancer scRNA‐seq object are available on GitHub at https://github.com/tuonglab/pan‐paedcancer‐scrnaseq. These data were derived from the resources available in the public domain, as listed in Table 1."], "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:40416408"], "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": "2026-11-01T19:53:03Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 8426, "completion_tokens": 1564, "cached_tokens": 264}}, "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:40416408", "type": "paper", "label": "pmid:40416408"}, {"id": "source:pmid:40416408", "type": "manuscript", "label": "PMID 40416408 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40416408", "type": "paper", "label": "pmid:40416408"}, {"id": "source:pmid:40416408", "type": "manuscript", "label": "PMID 40416408 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40416408", "type": "extraction", "used": ["source:pmid:40416408"], "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:40416408", "parent_hash": "sha256:ddba3b3ede7157589578a20b139d550101d85f4d570eacb5cc06563a4274d375", "fork_of": null, "generation": 1}, "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": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Data standards for single-cell RNA-sequencing of paediatric cancer.", "Single-cell RNA sequencing (scRNA-seq) is a powerful tool for investiga"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"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": ["Single-cell RNA sequencing (scRNA-seq) is a powerful tool for investiga", "Single-cell RNA sequencing (scRNA-seq) is a powerful tool for investigating paedia", "t is now the standard practice to upload the scRNA-seq data to data repositories to support scienti", "researcher to download and analyse existing scRNA-seq data to develop new ideas. This is incredibl"], "order_basis": "paper_text_order:68", "id": "assay:2"}]}