{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:43:38Z","timestamp":1753875818678,"version":"3.41.2"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T00:00:00Z","timestamp":1718668800000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1DP1DA048968-01"],"award-info":[{"award-number":["1DP1DA048968-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The advent of multimodal omics data has provided an unprecedented opportunity to systematically investigate underlying biological mechanisms from distinct yet complementary angles. However, the joint analysis of multi-omics data remains challenging because it requires modeling interactions between multiple sets of high-throughput variables. Furthermore, these interaction patterns may vary across different clinical groups, reflecting disease-related biological processes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a novel approach called Differential Canonical Correlation Analysis (dCCA) to capture differential covariation patterns between two multivariate vectors across clinical groups. Unlike classical Canonical Correlation Analysis, which maximizes the correlation between two multivariate vectors, dCCA aims to maximally recover differentially expressed multivariate-to-multivariate covariation patterns between groups. We have developed computational algorithms and a toolkit to sparsely select paired subsets of variables from two sets of multivariate variables while maximizing the differential covariation. Extensive simulation analyses demonstrate the superior performance of dCCA in selecting variables of interest and recovering differential correlations. We applied dCCA to the Pan-Kidney cohort from the Cancer Genome Atlas Program database and identified differentially expressed covariations between noncoding RNAs and gene expressions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and Implementation<\/jats:title>\n                  <jats:p>The R package that implements dCCA is available at https:\/\/github.com\/hwiyoungstat\/dCCA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbae288","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T13:18:52Z","timestamp":1718716732000},"source":"Crossref","is-referenced-by-count":0,"title":["dCCA: detecting differential covariation patterns between two types of high-throughput omics data"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3855-2316","authenticated-orcid":false,"given":"Hwiyoung","family":"Lee","sequence":"first","affiliation":[{"name":"Maryland Psychiatric Research Center, School of Medicine, University of Maryland , Baltimore, MD 21201 , United States"},{"name":"The University of Maryland Institute for Health Computing (UM-IHC) , North Bethesda, MD 20852 , United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3605-0811","authenticated-orcid":false,"given":"Tianzhou","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, University of Maryland , College Park, MD 20742 , United States"}]},{"given":"Hongjie","family":"Ke","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, University of Maryland , College Park, MD 20742 , United States"}]},{"given":"Zhenyao","family":"Ye","sequence":"additional","affiliation":[{"name":"The University of Maryland Institute for Health Computing (UM-IHC) , North Bethesda, MD 20852 , United States"},{"name":"Division of Biostatistics and Bioinformatics , Department of Epidemiology and Public Health, , Baltimore, MD 21201 , United States"},{"name":"School of Medicine, University of Maryland , Department of Epidemiology and Public Health, , Baltimore, MD 21201 , United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7990-4947","authenticated-orcid":false,"given":"Shuo","family":"Chen","sequence":"additional","affiliation":[{"name":"Maryland Psychiatric Research Center, School of Medicine, University of Maryland , Baltimore, MD 21201 , United States"},{"name":"The University of Maryland Institute for Health Computing (UM-IHC) , North Bethesda, MD 20852 , United States"},{"name":"Division of Biostatistics and Bioinformatics , Department of Epidemiology and Public Health, , Baltimore, MD 21201 , United States"},{"name":"School of Medicine, University of Maryland , Department of Epidemiology and Public Health, , Baltimore, MD 21201 , United States"}]}],"member":"286","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"key":"2024061813182465000_ref1","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1038\/cr.2008.24","article-title":"Microrna-21 targets tumor suppressor genes in invasion and metastasis","volume":"18","author":"Zhu","year":"2008","journal-title":"Cell Res"},{"key":"2024061813182465000_ref2","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1158\/0008-5472.CAN-16-2634","article-title":"Long noncoding rna and cancer: a new paradigm","volume":"77","author":"Bhan","year":"2017","journal-title":"Cancer Res"},{"key":"2024061813182465000_ref3","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1093\/biomet\/28.3-4.321","article-title":"Relations between two sets of variates","volume":"28","author":"Hotelling","year":"1936","journal-title":"Biometrika"},{"key":"2024061813182465000_ref4","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1109\/TKDE.2019.2958342","article-title":"A survey on canonical correlation analysis","volume":"33","author":"Yang","year":"2019","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2024061813182465000_ref5","doi-asserted-by":"crossref","first-page":"3807","DOI":"10.1002\/hbm.25090","article-title":"A technical review of canonical correlation analysis for neuroscience applications","volume":"41","author":"Zhuang","year":"2020","journal-title":"Hum Brain Mapp"},{"key":"2024061813182465000_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pgen.1010517","article-title":"Canonical correlation analysis for multi-omics: application to cross-cohort analysis","volume":"19","author":"Jiang","year":"2023","journal-title":"PLoS Genet"},{"key":"2024061813182465000_ref7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1003018","article-title":"Biomarker discovery by sparse canonical correlation analysis of complex clinical phenotypes of tuberculosis and malaria","volume":"9","author":"Rousu","year":"2013","journal-title":"PLoS Comput Biol"},{"key":"2024061813182465000_ref8","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1093\/bioinformatics\/btp515","article-title":"integrOmics: an R package to unravel relationships between two omics datasets","volume":"25","author":"Cao","year":"2009","journal-title":"Bioinformatics"},{"key":"2024061813182465000_ref9","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1093\/biostatistics\/kxp008","article-title":"A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis","volume":"10","author":"Witten","year":"2009","journal-title":"Biostatistics"},{"key":"2024061813182465000_ref10","doi-asserted-by":"crossref","first-page":"101656","DOI":"10.1016\/j.media.2020.101656","article-title":"Detecting genetic associations with brain imaging phenotypes in alzheimer\u2019s disease via a novel structured scca approach","volume":"61","author":"Lei","year":"2020","journal-title":"Med Image Anal"},{"key":"2024061813182465000_ref11","doi-asserted-by":"crossref","first-page":"i371","DOI":"10.1093\/bioinformatics\/btaa434","article-title":"Li Shen, and for the Alzheimer\u2019s Disease Neuroimaging Initiative. Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification","volume":"36","author":"Lei","year":"2020","journal-title":"Bioinformatics"},{"key":"2024061813182465000_ref12","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning","author":"Hastie","year":"2009"},{"key":"2024061813182465000_ref13","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1198\/016214502753479248","article-title":"Comparison of discrimination methods for the classification of tumors using gene expression data","volume":"97","author":"Dudoit","year":"2002","journal-title":"J Am Stat Assoc"},{"key":"2024061813182465000_ref14","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/j.1467-9868.2008.00674.x","article-title":"Sure independence screening for ultrahigh dimensional feature space","volume":"70","author":"Fan","year":"2008","journal-title":"J R Stat Soc Series B Stat Methodology"},{"key":"2024061813182465000_ref15","doi-asserted-by":"crossref","first-page":"4078","DOI":"10.1093\/bioinformatics\/btac518","article-title":"High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression","volume":"38","author":"Ke","year":"2022","journal-title":"Bioinformatics"},{"key":"2024061813182465000_ref16","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/3-540-44436-X_10","article-title":"Greedy approximation algorithms for finding dense components in a graph","volume-title":"Approximation Algorithms for Combinatorial Optimization","author":"Charikar","year":"2000"},{"key":"2024061813182465000_ref17","first-page":"406","article-title":"Sparse discriminant analysis","volume":"53","author":"Clemmensen","year":"2011","journal-title":"Dent Tech"},{"key":"2024061813182465000_ref18","doi-asserted-by":"crossref","first-page":"D956","DOI":"10.1093\/nar\/gkx1090","article-title":"LinkedOmics: analyzing multi-omics data within and across 32 cancer types","volume":"46","author":"Vasaikar","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2024061813182465000_ref19","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1093\/bioinformatics\/btt014","article-title":"Hongjin Han, and Di Wu. miRCancer: a microRNA-cancer association database constructed by text mining on literature","volume":"29","author":"Xie","year":"2013","journal-title":"Bioinformatics"},{"key":"2024061813182465000_ref20","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.gpb.2022.04.006","article-title":"Dbdemc 3.0: functional exploration of differentially expressed mirnas in cancers of human and model organisms","volume":"20","author":"Feng","year":"2022","journal-title":"Genomics Proteomics Bioinformatics"},{"key":"2024061813182465000_ref21","doi-asserted-by":"crossref","first-page":"461","DOI":"10.3233\/CBM-2011-0176","article-title":"Renal cell carcinoma","volume":"9","author":"Cairns","year":"2011","journal-title":"Cancer Biomark"},{"key":"2024061813182465000_ref22","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1126\/science.aai8690","article-title":"Dna damage is a pervasive cause of sequencing errors, directly confounding variant identification","volume":"355","author":"Chen","year":"2017","journal-title":"Science"},{"key":"2024061813182465000_ref23","doi-asserted-by":"crossref","first-page":"1140813","DOI":"10.3389\/fonc.2023.1140813","article-title":"Multiple functions and regulatory network of mir-150 in b lymphocyte-related diseases","volume":"13","author":"Hu","year":"2023","journal-title":"Front Oncol"},{"key":"2024061813182465000_ref24","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1002\/ijc.30845","article-title":"Large-scale genome-wide screening of circulating micrornas in clear cell renal cell carcinoma reveals specific signatures in late-stage disease","volume":"141","author":"Chanudet","year":"2017","journal-title":"Int J Cancer"},{"key":"2024061813182465000_ref25","doi-asserted-by":"crossref","first-page":"103287","DOI":"10.1016\/j.critrevonc.2021.103287","article-title":"Comprehensive review of chromophobe renal cell carcinoma","volume":"160","author":"Garje","year":"2021","journal-title":"Crit Rev Oncol Hematol"},{"key":"2024061813182465000_ref26","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2020.596359","article-title":"Microrna signature in renal cell carcinoma","volume":"10","author":"Ghafouri-Fard","year":"2020","journal-title":"Front Oncol"},{"key":"2024061813182465000_ref27","article-title":"Identification of rcc subtype-specific micrornas-meta-analysis of high-throughput rcc tumor microrna expression data","volume":"13","author":"Kajdasz","year":"2021","journal-title":"Cancer"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/4\/bbae288\/58266520\/bbae288.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/4\/bbae288\/58266520\/bbae288.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T13:19:12Z","timestamp":1718716752000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae288\/7695421"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,23]]},"references-count":27,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,5,23]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae288","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"type":"print","value":"1467-5463"},{"type":"electronic","value":"1477-4054"}],"subject":[],"published-other":{"date-parts":[[2024,7]]},"published":{"date-parts":[[2024,5,23]]},"article-number":"bbae288"}}