Leaving out control groups: an internal contrast analysis of gene expression profiles in atrial fibrillation patients--a systems biology approach to clinical categorization.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:19255648", "kind": "science-record", "revision": 7, "content_hash": "sha256:565207953f222a3ad12620928fb35586a17583b24c89a72b319cc1eaba1878f3", "created_at": "2026-11-01T15:14:33Z", "updated_at": "2026-11-01T21:19:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Leaving out control groups: an internal contrast analysis of gene expression profiles in atrial fibrillation patients--a systems biology approach to clinical categorization.", "doi": "10.6026/97320630003275", "pmid": "19255648", "authors": ["Kurt Vanhoutte", "Carlo de Asmundis", "Anna Francesconi", "Jurgen Figysl", "Griet Steurs", "Tim Boussy", "Markus Roos", "Andreas Mueller", "Lucio Massimo", "Gaetano Paparella", "Kristien Van Caelenberg", "Gian Battista Chierchia", "Andrea Sarkozy", "Pedro Brugada Y Terradellas", "Martin Zizi"], "journal": "Bioinformation", "year": 2009, "abstract": "Atrial fibrillation (AF) is a frequent chronic dysrythmia with an incidence that increases with age (>40). Because of its medical and socio-economic impacts it is expected to become an increasing burden on most health care systems. AF is a multi-factorial disease for which the identification of subtypes is warranted. Novel approaches based on the broad concepts of systems biology may overcome the blurred notion of normal and pathological phenotype, which is inherent to high throughput molecular arrays analysis. Here we apply an internal contrast algorithm on AF patient data with an analytical focus on potential entry pathways into the disease. We used a RMA (Robust Multichip Average) normalized Affymetrix micro-array data set from 10 AF patients (geo_accession #GSE2240). Four series of probes were selected based on physiopathogenic links with AF entryways: apoptosis (remodeling), MAP kinase (cell remodeling), OXPHOS (ability to sustain hemodynamic workload) and glycolysis (ischemia). Annotated probe lists were polled with Bioconductor packages in R (version 2.7.1). Genetic profile contrasts were analysed with hierarchical clustering and principal component analysis. The analysis revealed distinct patient groups for all probe sets. A substantial part (54% till 67%) of the variance is explained in the first 2 principal components. Genes in PC1/2 with high discriminatory value were selected and analyzed in detail. We aim for reliable molecular stratification of AF. We show that stratification is possible based on physiologically relevant gene sets. Genes with high contrast value are likely to give pathophysiological insight into permanent AF subtypes.", "claims": [{"id": "claim-method", "statement": "To select genes with high contrast value between all patients a principal component analysis (PCA) [12] was done.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:19255648", "locator": "sections[5] \"Contrast analysis\" paragraph 2", "quote": "To select genes with high contrast value between all patients a principal component analysis (PCA) [12] was done."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data were obtained from 10 patient samples in an Affymetrix -set (geo_accession #GSE2240).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:19255648", "locator": "sections[3] \"Datasets\" paragraph 2", "quote": "The data were obtained from 10 patient samples in an Affymetrix -set (geo_accession #GSE2240)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper’s headline result was that stratification is possible based on physiologically relevant gene sets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:19255648", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We used a RMA (Robust Multichip Average) normalized Affymetrix micro-array data set from 10 AF patients (geo_accession #GSE2240)."}, {"asset": "source:pmid:19255648", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Genetic profile contrasts were analysed with hierarchical clustering and principal component analysis."}, {"asset": "source:pmid:19255648", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The analysis revealed distinct patient groups for all probe sets."}, {"asset": "source:pmid:19255648", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We show that stratification is possible based on physiologically relevant gene sets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The paper’s headline result was that stratification is possible based on physiologically relevant gene sets.", "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:19255648"], "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:19255648", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.6026/97320630003275", "sha256": "sha256:32e57ff9a566ff5736351d427ce17f393f69c77bd3c145322703e6d9ed1ab269", "bytes": 15254, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.6026/97320630003275", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC2649423", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To select genes with high contrast value between all patients a principal component analysis (PCA) [12] was done."}}, {"id": "method-2", "family": "clustering", "specific": "hierarchical", "label": "Clustering", "dsl_operations": ["hierarchical_cluster"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "hierarchical clustering"}}], "targets": [], "gaps": []}, "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": "R1", "expressible": true, "required_operations": ["pca_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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