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Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011814","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T13:45:51Z","timestamp":1711374351000},"page":"e1011814","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":28,"title":["PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data 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Mastej","year":"2020","journal-title":"Metabolites"},{"key":"pcbi.1011814.ref023","first-page":"13","article-title":"OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics","volume":"1","author":"G Zhou","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1011814.ref024","doi-asserted-by":"crossref","first-page":"e1002375","DOI":"10.1371\/journal.pcbi.1002375","article-title":"Ten years of pathway analysis: Current approaches and outstanding challenges","author":"P Khatri","year":"2012","journal-title":"PLoS Computational Biology."},{"key":"pcbi.1011814.ref025","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1038\/10343","article-title":"Systematic determination of genetic network architecture","volume":"22","author":"S Tavazoie","year":"1999","journal-title":"Nat Genet"},{"key":"pcbi.1011814.ref026","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: 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Fang","year":"2021","journal-title":"Gigascience"},{"key":"pcbi.1011814.ref033","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1186\/s12859-022-05005-1","article-title":"Single sample pathway analysis in metabolomics: performance evaluation and application","volume":"23","author":"C Wieder","year":"2022","journal-title":"BMC Bioinformatics"},{"key":"pcbi.1011814.ref034","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1186\/1471-2105-15-162","article-title":"A multivariate approach to the integration of multi-omics datasets","volume":"15","author":"C Meng","year":"2014","journal-title":"BMC Bioinformatics"},{"key":"pcbi.1011814.ref035","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1471-2105-14-7","article-title":"GSVA: Gene set variation analysis for microarray and RNA-Seq data","volume":"14","author":"S H\u00e4nzelmann","year":"2013","journal-title":"BMC 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Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"pcbi.1011814.ref040","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.21105\/joss.01190","article-title":"Multiblock PLS: Block dependent prediction modeling for Python.","volume":"4","author":"A Baum","year":"2019","journal-title":"J Open Source Softw"},{"key":"pcbi.1011814.ref041","article-title":"Analysis of multiblock and hierarchical PCA and PLS models.","author":"J Westerhuis","year":"1998","journal-title":"Wiley Online Library"},{"key":"pcbi.1011814.ref042","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/cem.1180030104","article-title":"A multiblock partial least squares algorithm for investigating complex chemical systems","volume":"3","author":"LE Wangen","year":"1989","journal-title":"J Chemom"},{"key":"pcbi.1011814.ref043","doi-asserted-by":"crossref","first-page":"e1005752","DOI":"10.1371\/journal.pcbi.1005752","article-title":"mixOmics: An R package for \u2018omics feature selection and multiple data integration.","volume":"13","author":"F Rohart","year":"2017","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1011814.ref044","doi-asserted-by":"crossref","first-page":"e1009105","DOI":"10.1371\/journal.pcbi.1009105","article-title":"Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.","volume":"17","author":"C Wieder","year":"2021","journal-title":"PLoS Comput Biol."},{"key":"pcbi.1011814.ref045","first-page":"32","article-title":"Genetic Epidemiology of COPD (COPDGene) Study Design.","volume":"7","author":"EA Regan","year":"2010"},{"key":"pcbi.1011814.ref046","doi-asserted-by":"crossref","first-page":"81s","DOI":"10.1183\/09031936.03.00004611","article-title":"Nutritional and metabolic modulation in chronic obstructive pulmonary disease management.","volume":"22","author":"AMWJ Schols","year":"2003","journal-title":"European Respiratory 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surfactant lipids correlate with lung function in chronic obstructive pulmonary disease (COPD).","volume":"15","author":"CW Agudelo","year":"2020","journal-title":"PLoS One."},{"key":"pcbi.1011814.ref054","first-page":"1","article-title":"AIM2 nuclear exit and inflammasome activation in chronic obstructive pulmonary disease and response to cigarette smoke","volume":"18","author":"HB Tran","year":"2021","journal-title":"Journal of Inflammation (United Kingdom)."},{"key":"pcbi.1011814.ref055","doi-asserted-by":"crossref","DOI":"10.3390\/ijms222312803","article-title":"Anti-Inflammatory Function of Fatty Acids and Involvement of Their Metabolites in the Resolution of Inflammation in Chronic Obstructive Pulmonary Disease.","volume":"22","author":"S Kotlyarov","year":"2021","journal-title":"Int J Mol Sci"},{"key":"pcbi.1011814.ref056","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1016\/j.cell.2020.10.037","article-title":"Multi-Omics Resolves a Sharp Disease-State Shift between 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