DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

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. Availability and implementation 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. Supplementary information Supplementary data are available at Bioinformatics online.

A powerful framework for an integrative study with heterogeneous omics data: from univariate statistics to multi-block analysis

“…Can we determine a multi-omics signature to classify ecotypes?). Method: multi-block PLS-DA (referred as DIABLO for Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies) was recently developed to address this issue (Singh et al, 2019). A schematic view of the data sets and the methods implemented is presented in Figure 4.…”

MiBiOmics: An interactive web application for multi-omics data exploration and integration

“…More widely applicable methods exist, such as the R package mixOmics [21] that provides several semi-supervised methodologies often based on ordination techniques. We compared methods integrated in MiBiOmics (see Supplementary information for details) to the mixOmics DIABLO methodology [22]. Only few multi-omics features associated to breast cancer subtype in the TGCA dataset were extracted by all three methods (n=32, Figure 4A and Supplementary table 1).…”

A generic multivariate framework for the integration of microbiome longitudinal studies with other data types

“…For example, we can consider Generalised Canonical Correlation Analysis (GCCA, Tenenhaus and Tenenhaus 2011;Tenenhaus et al 2014), which, contrary to what its name suggests, generalises PLS for the integration of more than two datasets. Recently, we have developed the DIABLO method to discriminate different phenotypic groups in a supervised framework (Singh et al, 2019). In the context of this study however, we present the sparse GCCA in an unsupervised framework, where input datasets are spline-fitted matrices.…”

“…Some sparse multivariate methods have been proposed to integrate omics and microbiome datasets at a single time point and identify sets of features (multi-omics signatures) across multiple data types that are correlated with one another. For example, Gavin et al (2018) used the DIABLO method (Singh et al, 2019) to integrate 16S, proteomics and metaproteomics in a type I diabetes study, Guidi et al (2016) used sparse PLS (Lê Cao et al, 2008) to integrate environmental and metagenomic data from the Tara Oceans expedition to understand carbon export in oligotrophic oceans, and Fukuyama et al (2017) used sparse Canonical Correlation Analysis (Witten et al, 2009) to integrate 16S and metagenomic data. However, methods or frameworks to integrate multiple longitudinal datasets including microbiome data remain incomplete.…”