SEMgsa: topology-based pathway enrichment analysis with structural equation models.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35978279", "kind": "science-record", "revision": 4, "content_hash": "sha256:fd9a2523ed46084e02c98b5e2c04df661f7fafe155f48d03ab135f024bbf983e", "created_at": "2026-11-01T18:15:06Z", "updated_at": "2026-11-01T22:07:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "SEMgsa: topology-based pathway enrichment analysis with structural equation models.", "doi": "10.1186/s12859-022-04884-8", "pmid": "35978279", "authors": ["Mario Grassi", "Barbara Tarantino"], "journal": "BMC bioinformatics", "year": 2022, "abstract": "BACKGROUND: Pathway enrichment analysis is extensively used in high-throughput experimental studies to gain insight into the functional roles of pre-defined subsets of genes, proteins and metabolites. Methods that leverages information on the topology of the underlying pathways outperform simpler methods that only consider pathway membership, leading to improved performance. Among all the proposed software tools, there's the need to combine high statistical power together with a user-friendly framework, making it difficult to choose the best method for a particular experimental environment.\nRESULTS: We propose SEMgsa, a topology-based algorithm developed into the framework of structural equation models. SEMgsa combine the SEM p values regarding node-specific group effect estimates in terms of activation or inhibition, after statistically controlling biological relations among genes within pathways. We used SEMgsa to identify biologically relevant results in a Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) together with a frontotemporal dementia (FTD) DNA methylation dataset (GEO accession: GSE53740) and compared its performance with some existing methods. SEMgsa is highly sensitive to the pathways designed for the specific disease, showing low p values ([Formula: see text]) and ranking in high positions, outperforming existing software tools. Three pathway dysregulation mechanisms were used to generate simulated expression data and evaluate the performance of methods in terms of type I error followed by their statistical power. Simulation results confirm best overall performance of SEMgsa.\nCONCLUSIONS: SEMgsa is a novel yet powerful method for identifying enrichment with regard to gene expression data. It takes into account topological information and exploits pathway perturbation statistics to reveal biological information. SEMgsa is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph .", "claims": [{"id": "claim-method", "statement": "We propose SEMgsa, a topology-based algorithm developed into the framework of structural equation models.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35978279", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We propose SEMgsa, a topology-based algorithm developed into the framework of structural equation models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed during the current study are available in that repository under the Data/ directory (https://github.com/fernandoPalluzzi/SEMgraph/tree/master/SEMgsa_replication/Data/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35978279", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The datasets analysed during the current study are available in that repository under the Data/ directory (https://github.com/fernandoPalluzzi/SEMgraph/tree/master/SEMgsa_replication/Data/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "SEMgsa had the best overall performance, achieving 90–100% statistical power across simulations while all methods controlled the type I error rate at the 0.05 significance level.", "kind": "quantitative", "target": {"value": 100.0, "unit": "per cent", "tolerance": null, "source": "SEMgsa stands out among all with 90–100% power across simulation."}, "evidence": [{"asset": "source:pmid:35978279", "locator": "sections[15] \"Simulation results\" paragraph 2", "quote": "SEMgsa stands out among all with 90–100% power across simulation."}, {"asset": "source:pmid:35978279", "locator": "sections[15] \"Simulation results\" paragraph 2", "quote": "At the 0.05 significance level, all methods control the type I error rate across the 10 pathways under different level of mean signal"}, {"asset": "source:pmid:35978279", "locator": "sections[15] \"Simulation results\" paragraph 5", "quote": "Among the methods compared, SEMgsa() has the best overall performance."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "SEMgsa had the best overall performance, achieving 90–100% statistical power across simulations while all methods controlled the type I error rate at the 0.05 significance level.", "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:35978279"], "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. 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