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dc.contributor.authorLe Cao, K-A
dc.contributor.authorGonzalez, I
dc.contributor.authorDejean, S
dc.date.accessioned2020-12-17T02:54:48Z
dc.date.available2020-12-17T02:54:48Z
dc.date.issued2009-11-01
dc.identifierpii: btp515
dc.identifier.citationLe Cao, K. -A., Gonzalez, I. & Dejean, S. (2009). integrOmics: an R package to unravel relationships between two omics datasets. BIOINFORMATICS, 25 (21), pp.2855-2856. https://doi.org/10.1093/bioinformatics/btp515.
dc.identifier.issn1367-4803
dc.identifier.urihttp://hdl.handle.net/11343/254670
dc.description.abstractMOTIVATION: With the availability of many 'omics' data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated. RESULTS: integrOmics efficiently performs integrative analyses of two types of 'omics' variables that are measured on the same samples. It includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets. The usefulness of both approaches has been demonstrated previously and successfully applied in various integrative studies. AVAILABILITY: integrOmics is freely available from http://CRAN.R-project.org/ or from the web site companion (http://math.univ-toulouse.fr/biostat) that provides full documentation and tutorials. CONTACT: k.lecao@uq.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
dc.languageEnglish
dc.publisherOXFORD UNIV PRESS
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0
dc.titleintegrOmics: an R package to unravel relationships between two omics datasets
dc.typeJournal Article
dc.identifier.doi10.1093/bioinformatics/btp515
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.source.titleBioinformatics
melbourne.source.volume25
melbourne.source.issue21
melbourne.source.pages2855-2856
dc.rights.licenseCC BY-NC
melbourne.elementsid1220050
melbourne.contributor.authorLe Cao, Kim-Anh
dc.identifier.eissn1460-2059
melbourne.accessrightsOpen Access


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