DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays
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- Amrit Singh
- University of British Columbia Prevention of Organ Failure (PROOF) Centre of Excellence, , Vancouver, BC, Canada
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- Casey P Shannon
- University of British Columbia Prevention of Organ Failure (PROOF) Centre of Excellence, , Vancouver, BC, Canada
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- Benoît Gautier
- University of Queensland Diamantina Institute, Translational Research Institute The , Woolloongabba, Queensland, Australia
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- Florian Rohart
- Institute for Molecular Bioscience, The University of Queensland , St Lucia, Queensland, Australia
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- Michaël Vacher
- Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation , Brisbane, Queensland, Australia
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- Scott J Tebbutt
- University of British Columbia Prevention of Organ Failure (PROOF) Centre of Excellence, , Vancouver, BC, Canada
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- Kim-Anh Lê Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne , Melbourne, Australia
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- Inanc Birol
- editor
Description
<jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Using simulations and benchmark multi-omics studies, we show that DIABLO identifies features with superior biological relevance compared with existing unsupervised integrative methods, while achieving predictive performance comparable to state-of-the-art supervised approaches. DIABLO is versatile, allowing for modular-based analyses and cross-over study designs. In two case studies, DIABLO identified both known and novel multi-omics biomarkers consisting of mRNAs, miRNAs, CpGs, proteins and metabolites.</jats:p></jats:sec><jats:sec><jats:title>Availability and implementation</jats:title><jats:p>DIABLO is implemented in the mixOmics R Bioconductor package with functions for parameters’ choice and visualization to assist in the interpretation of the integrative analyses, along with tutorials on http://mixomics.org and in our Bioconductor vignette.</jats:p></jats:sec><jats:sec><jats:title>Supplementary information</jats:title><jats:p>Supplementary data are available at Bioinformatics online.</jats:p></jats:sec>
Journal
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- Bioinformatics
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Bioinformatics 35 (17), 3055-3062, 2019-01-18
Oxford University Press (OUP)
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Details 詳細情報について
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- CRID
- 1360292619976195968
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- ISSN
- 13674811
- 13674803
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- Data Source
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- Crossref