Differential gene expression analysis for multi-subject single-cell RNA-sequencing studies with aggregateBioVar.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33970215", "kind": "science-record", "revision": 7, "content_hash": "sha256:ae902427be462e514c1267406da3a0471dec2aa066f29a622845b61ab37ff419", "created_at": "2026-11-01T15:41:35Z", "updated_at": "2026-11-01T22:03:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Differential gene expression analysis for multi-subject single-cell RNA-sequencing studies with aggregateBioVar.", "doi": "10.1093/bioinformatics/btab337", "pmid": "33970215", "authors": ["Andrew L Thurman", "Jason A Ratcliff", "Michael S Chimenti", "Alejandro A Pezzulo"], "journal": "Bioinformatics (Oxford, England)", "year": 2021, "abstract": "MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) provides more granular biological information than bulk RNA-sequencing; bulk RNA sequencing remains popular due to lower costs which allows processing more biological replicates and design more powerful studies. As scRNA-seq costs have decreased, collecting data from more than one biological replicate has become more feasible, but careful modeling of different layers of biological variation remains challenging for many users. Here, we propose a statistical model for scRNA-seq gene counts, describe a simple method for estimating model parameters and show that failing to account for additional biological variation in scRNA-seq studies can inflate false discovery rates (FDRs) of statistical tests.\nRESULTS: First, in a simulation study, we show that when the gene expression distribution of a population of cells varies between subjects, a naïve approach to differential expression analysis will inflate the FDR. We then compare multiple differential expression testing methods on scRNA-seq datasets from human samples and from animal models. These analyses suggest that a naïve approach to differential expression testing could lead to many false discoveries; in contrast, an approach based on pseudobulk counts has better FDR control.\nAVAILABILITY AND IMPLEMENTATION: A software package, aggregateBioVar, is freely available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/aggregateBioVar.html) to accommodate compatibility with upstream and downstream methods in scRNA-seq data analysis pipelines.\nSUPPLEMENTARY INFORMATION: Raw gene-by-cell count matrices for pig scRNA-seq data are available as GEO accession GSE150211. Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-data-access", "statement": "The data from pig airway epithelia underlying this article are available in GEO and can be accessed with GEO accession GSE150211. Data for the analysis of human skin biopsies were obtained from GEO accession GSE130973. Data for the analysis of human trachea were obtained from GEO accessions GSE143705 (bulk RNA-seq) and GSE143706 (scRNA-seq). The scRNA-seq data for the analysis of human lung tissue were obtained from GEO accession GSE122960, and the bulk RNA-seq of purified AT2 and AM fractions were shared by the authors immediately upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33970215", "locator": "sections[14] \"Data availability\" paragraph 2", "quote": "The data from pig airway epithelia underlying this article are available in GEO and can be accessed with GEO accession GSE150211. Data for the analysis of human skin biopsies were obtained from GEO accession GSE130973. Data for the analysis of human trachea were obtained from GEO accessions GSE143705 (bulk RNA-seq) and GSE143706 (scRNA-seq). The scRNA-seq data for the analysis of human lung tissue were obtained from GEO accession GSE122960, and the bulk RNA-seq of purified AT2 and AM fractions were shared by the authors immediately upon request."}], "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:33970215"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122960"], "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": ["data:geo:GSE122960"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33970215", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btab337", "sha256": "sha256:a5326dda3ecf7017ee815a77ad1a935fed3474b4c933250b86b34beee445db15", "bytes": 57926, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btab337", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8504643", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE122960", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122960", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE122960", "version": null, "note": "The data from pig airway epithelia underlying this article are available in GEO and can be accessed with GEO accession GSE150211. Data for the analysis of human skin biopsies were obtained from GEO accession GSE130973. Data for the analysis of human trachea were obtained from GEO accessions GSE143705 (bulk RNA-seq) and GSE143706 (scRNA-seq). The scRNA-seq data for the analysis of human lung tissue were obtained from GEO accession GSE122960, and the bulk RNA-seq of purified AT2 and AM fractions were shared by the authors immediately upon request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data from pig airway epithelia underlying this article are available in GEO and can be accessed with GEO accession GSE150211. Data for the analysis of human skin biopsies were obtained from GEO accession GSE130973. Data for the analysis of human trachea were obtained from GEO accessions GSE143705 (bulk RNA-seq) and GSE143706 (scRNA-seq). The scRNA-seq data for the analysis of human lung tissue were obtained from GEO accession GSE122960, and the bulk RNA-seq of purified AT2 and AM fractions were shared by the authors immediately upon request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3489182", "GSM3489183", "GSM3489184", "GSM3489185", "GSM3489186", "GSM3489187", "GSM3489188", "GSM3489189", "GSM3489190", "GSM3489191", "GSM3489192", "GSM3489193", "GSM3489194", "GSM3489195", "GSM3489196", "GSM3489197", "GSM3489198"], "sample_count": 17, "group_sizes": null, "group_sizes_basis": null, "platform": "20301", "taxon": "Homo sapiens", "title": "Single-Cell Transcriptomic Analysis of Human Lung Reveals Complex Multicellular Changes During Pulmonary Fibrosis II", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE130973", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE130973", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE130973", "version": null, "note": "The data from pig airway epithelia underlying this article are available in GEO and can be accessed with GEO accession GSE150211. 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The scRNA-seq data for the analysis of human lung tissue were obtained from GEO accession GSE122960, and the bulk RNA-seq of purified AT2 and AM fractions were shared by the authors immediately upon request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4272903", "GSM4272904", "GSM4272905", "GSM4272906", "GSM4272907", "GSM4272908", "GSM4272909", "GSM4272910", "GSM4272911", "GSM4272912", "GSM4272913", "GSM4272914"], "sample_count": 12, "group_sizes": null, "group_sizes_basis": null, "platform": "18573", "taxon": "Homo sapiens", "title": "Human basal cell diversity in normal and IPF lung (bulk RNAseq)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE143706", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143706", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE143706", "version": null, "note": "The data from pig airway epithelia underlying this article are available in GEO and can be accessed with GEO accession GSE150211. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE150211", "GSE130973", "GSE143705", "GSE143706", "GSE122960"], "resolved_accessions": ["GSE150211", "GSE130973", "GSE143705", "GSE143706", "GSE122960"], "statements": ["The data from pig airway epithelia underlying this article are available in GEO and can be accessed with GEO accession GSE150211. Data for the analysis of human skin biopsies were obtained from GEO accession GSE130973. Data for the analysis of human trachea were obtained from GEO accessions GSE143705 (bulk RNA-seq) and GSE143706 (scRNA-seq). 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As scRNA-seq costs have decreased, collecting data from m", "rs. Here, we propose a statistical model for scRNA-seq gene counts, describe a simple method for es", "count for additional biological variation in scRNA-seq studies can inflate false discovery rates (F"], "order_basis": "paper_text_order:113", "id": "assay:2"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ous bulk RNA-seq (Bartlett et al., 2016) and microarray (Stoltz et al., 2010) studies have suggested"], "order_basis": "paper_text_order:3383", "id": "assay:3"}, {"role": "data_generation", "definition_id": "flow_cytometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["F (Reyfman et al., 2019). 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