recountmethylation enables flexible analysis of public blood DNA methylation array data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36874953", "kind": "science-record", "revision": 2, "content_hash": "sha256:c8c0fdbcccaa27e5df20a42f9dbac7b15eb3750524114ef711cd5ecbdb32f69c", "created_at": "2026-11-01T19:01:30Z", "updated_at": "2026-11-01T21:30:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "recountmethylation enables flexible analysis of public blood DNA methylation array data.", "doi": "10.1093/bioadv/vbad020", "pmid": "36874953", "authors": ["Sean K Maden", "Brian Walsh", "Kyle Ellrott", "Kasper D Hansen", "Reid F Thompson", "Abhinav Nellore"], "journal": "Bioinformatics advances", "year": 2023, "abstract": "Summary: Thousands of DNA methylation (DNAm) array samples from human blood are publicly available on the Gene Expression Omnibus (GEO), but they remain underutilized for experiment planning, replication and cross-study and cross-platform analyses. To facilitate these tasks, we augmented our recountmethylation R/Bioconductor package with 12 537 uniformly processed EPIC and HM450K blood samples on GEO as well as several new features. We subsequently used our updated package in several illustrative analyses, finding (i) study ID bias adjustment increased variation explained by biological and demographic variables, (ii) most variation in autosomal DNAm was explained by genetic ancestry and CD4+ T-cell fractions and (iii) the dependence of power to detect differential methylation on sample size was similar for each of peripheral blood mononuclear cells (PBMC), whole blood and umbilical cord blood. Finally, we used PBMC and whole blood to perform independent validations, and we recovered 38-46% of differentially methylated probes between sexes from two previously published epigenome-wide association studies.\nAvailability and implementation: Source code to reproduce the main results are available on GitHub (repo: recountmethylation_flexible-blood-analysis_manuscript; url: https://github.com/metamaden/recountmethylation_flexible-blood-analysis_manuscript). All data was publicly available and downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). Compilations of the analyzed public data can be accessed from the website recount.bio/data (preprocessed HM450K array data: https://recount.bio/data/remethdb_h5se-gm_epic_0-0-2_1589820348/; preprocessed EPIC array data: https://recount.bio/data/remethdb_h5se-gm_epic_0-0-2_1589820348/).\nSupplementary information: Supplementary data are available at Bioinformatics Advances online.", "claims": [{"id": "claim-method", "statement": "We conducted power analyses on the blood samples included in recountmethylation by applying the simulation-based pwrEWAS approach (Graw et al., 2019) (Section 4).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36874953", "locator": "sections[6] \"2.4 Dependence of statistical power on sample size was similar across blood sample types\" paragraph 2", "quote": "We conducted power analyses on the blood samples included in recountmethylation by applying the simulation-based pwrEWAS approach (Graw et al., 2019) (Section 4)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data used in this study were publicly available and downloaded from the Gene Expression Omnibus (GEO) repository at National Center for Biotechnology Information (NCBI) website (https://www.ncbi.nlm.nih.gov/geo). 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Compilations of the analyzed public data can be accessed from the website recount.bio/data (preprocessed HM450K array data: https://recount.bio/data/remethdb_h5se-gm_epic_0-0-2_1589820348/; preprocessed EPIC array data: https://recount.bio/data/remethdb_h5se-gm_epic_0-0-2_1589820348/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Finally, we used PBMC and whole blood to perform independent validations, and we recovered 38–46% of differentially methylated probes between sexes from two previously published epigenome-wide association studies.", "kind": "quantitative", "target": {"value": 46.0, "unit": "per cent", "tolerance": null, "source": "Finally, we used PBMC and whole blood to perform independent validations, and we recovered 38–46% of differentially methylated probes between sexes from two previously published epigenome-wide association studies."}, "evidence": [{"asset": "source:pmid:36874953", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Finally, we used PBMC and whole blood to perform independent validations, and we recovered 38–46% of differentially methylated probes between sexes from two previously published epigenome-wide association studies."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Finally, we used PBMC and whole blood to perform independent validations, and we recovered 38–46% of differentially methylated probes between sexes from two previously published epigenome-wide association studies.", "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:36874953"], "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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No tolerance is published because the paper states none."}], "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 (simulation-based pwrEWAS approach), 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. 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We narrowed focus to 12 537 normal human blood samples from 63 studies, each of which had ≥10 samples af"], "order_basis": "paper_text_order:2259", "id": "assay:2"}]}