DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays
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- 18 January 2019
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 35 (17), 3055-3062
- https://doi.org/10.1093/bioinformatics/bty1054
Abstract
Motivation: 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. Results: 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.Keywords
Funding Information
- National Institute of Allergy and Infectious Diseases (U19AI118608)
- National Health and Medical Research Council
- NHMRC
- Career Development (GNT1087415)
This publication has 40 references indexed in Scilit:
- Passing Messages between Biological Networks to Refine Predicted InteractionsPLOS ONE, 2013
- Joint and individual variation explained (JIVE) for integrated analysis of multiple data typesThe Annals of Applied Statistics, 2013
- A novel approach for biomarker selection and the integration of repeated measures experiments from two assaysBMC Bioinformatics, 2012
- Bayesian correlated clustering to integrate multiple datasetsBioinformatics, 2012
- Comprehensive molecular portraits of human breast tumoursNature, 2012
- Discovery of multi-dimensional modules by integrative analysis of cancer genomic dataNucleic Acids Research, 2012
- Identifying multi-layer gene regulatory modules from multi-dimensional genomic dataBioinformatics, 2012
- Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problemsBMC Bioinformatics, 2011
- A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modulesBioinformatics, 2011
- Assessing the Role of Circulating, Genetic, and Imaging Biomarkers in Cardiovascular Risk PredictionCirculation, 2011