{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T05:46:23Z","timestamp":1772516783961,"version":"3.50.1"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"vor","delay-in-days":50,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"publisher","award":["2018-00520"],"award-info":[{"award-number":["2018-00520"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"publisher","award":["2017-01142"],"award-info":[{"award-number":["2017-01142"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Swedish Heart Lung Foundation","award":["20190017"],"award-info":[{"award-number":["20190017"]}]},{"name":"Swedish Heart Lung Foundation","award":["20190421"],"award-info":[{"award-number":["20190421"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372331"],"award-info":[{"award-number":["62372331"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Multi-omics data integration is a complex and challenging task in biomedical research. Consensus clustering, also known as meta-clustering or cluster ensembles, has become an increasingly popular downstream tool for phenotyping and endotyping using multiple omics and clinical data. However, current consensus clustering methods typically rely on ensembling clustering outputs with similar sample coverages (mathematical replicates), which may not reflect real-world data with varying sample coverages (biological replicates). To address this issue, we propose a new consensus clustering with missing labels (ccml) strategy termed ccml, an R protocol for two-step consensus clustering that can handle unequal missing labels (i.e. multiple predictive labels with different sample coverages). Initially, the regular consensus weights are adjusted (normalized) by sample coverage, then a regular consensus clustering is performed to predict the optimal final cluster. We applied the ccml method to predict molecularly distinct groups based on 9-omics integration in the Karolinska COSMIC cohort, which investigates chronic obstructive pulmonary disease, and 24-omics handprint integrative subgrouping of adult asthma patients of the U-BIOPRED cohort. We propose ccml as a downstream toolkit for multi-omics integration analysis algorithms such as Similarity Network Fusion and robust clustering of clinical data to overcome the limitations posed by missing data, which is inevitable in human cohorts consisting of multiple data modalities. The ccml tool is available in the R language (https:\/\/CRAN.R-project.org\/package=ccml, https:\/\/github.com\/pulmonomics-lab\/ccml, or https:\/\/github.com\/ZhoulabCPH\/ccml).<\/jats:p>","DOI":"10.1093\/bib\/bbad501","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T12:41:21Z","timestamp":1704976881000},"source":"Crossref","is-referenced-by-count":3,"title":["Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2189-5010","authenticated-orcid":false,"given":"Chuan-Xing","family":"Li","sequence":"first","affiliation":[{"name":"Respiratory Medicine Unit , Department of Medicine Solna & Centre for Molecular Medicine,"},{"name":"Karolinska Institutet , Department of Medicine Solna & Centre for Molecular Medicine,"}]},{"given":"Hongyan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Wenzhou Medical University , Wenzhou , China"}]},{"given":"Nazanin","family":"Zounemat-Kermani","sequence":"additional","affiliation":[{"name":"National Heart and Lung Institute , Faculty of Medicine, , London , United Kingdom"},{"name":"Imperial College London , Faculty of Medicine, , London , United Kingdom"},{"name":"Data Science Institute, Imperial College London , London , United Kingdom"}]},{"given":"Ian M","family":"Adcock","sequence":"additional","affiliation":[{"name":"National Heart and Lung Institute , Faculty of Medicine, , London , United Kingdom"},{"name":"Imperial College London , Faculty of Medicine, , London , United Kingdom"},{"name":"Data Science Institute, Imperial College London , London , United Kingdom"}]},{"given":"C Magnus","family":"Sk\u00f6ld","sequence":"additional","affiliation":[{"name":"Respiratory Medicine Unit , Department of Medicine Solna & Centre for Molecular Medicine,"},{"name":"Karolinska Institutet , Department of Medicine Solna & Centre for Molecular Medicine,"},{"name":"Department of Respiratory Medicine and Allergy, Karolinska University Hospital Solna , Stockholm , Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9987-9024","authenticated-orcid":false,"given":"Meng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Wenzhou Medical University , Wenzhou , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8013-2745","authenticated-orcid":false,"given":"\u00c5sa M","family":"Wheelock","sequence":"additional","affiliation":[{"name":"Respiratory Medicine Unit , Department of Medicine Solna & Centre for Molecular Medicine,"},{"name":"Karolinska Institutet , Department of Medicine Solna & Centre for Molecular Medicine,"},{"name":"Department of Respiratory Medicine and Allergy, Karolinska University Hospital Solna , Stockholm , 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