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

By Amrit Singh, C. Shannon, Benoît Gautier, Florian Rohart, M. Vacher, S. Tebbutt, Kim-Anh Lê Cao. Bioinform., 2019

846 citations recorded by the source corpus.

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 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 to 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 DIABLO is implemented in the mixOmics R Bioconductor package with functions for parameters choise and visualisation to assist in the interpretation of the integrative analyses, along with tutorials on http://mixomics.org and in our Bioconductor vignette. Suppl. information Supplementary information is available at Bioinformatics online.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cf188432a73721cdea52510f9f40703c2364c59f

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In Molecular Biology

Citations in the corpus

Network analysis methods for studying microbial communities: A mini review (2021, methodology): “Similarly, Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) [94] is a multi-omics integration tool based on partial least squares (PLS) regression, a technique to reduce the number of predictors by ﬁnding a small set of uncorrelated variables which are then used to…”
MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data (2023, positive): “Two robust dimensionality reduction methods, Procrustes analysis (PA) ( 31 ) and data integration analysis for biomarker discovery using latent components (DIABLO) ( 32 ), have been implemented to re v eal ov erall patterns between paired microbiome and metabolomics datasets.”
Computational principles and challenges in single-cell data integration (2021, methodology): “These approaches include unsupervised dimensionality reduction methods that represent different flavors of matrix factorization, such as canonical correlation analysis (CCA, implemented in Seurat 18,22 ), MOFA 69 , JIVE 70 , PLS 71 , MCIA 72 and iNMF 62 , among others.”
MOLI: multi-omics late integration with deep neural networks for drug response prediction (2019, background): “…have been exploited for different problems such as driver gene identification (Dimitrakopoulos et al., 2018; Mo et al., 2013; Shrestha et al., 2017; Singh et al., 2019), patient stratification (Khakabimamaghani and Ester, 2016), survival prediction (Chaudhary et al., 2018), subgroup discovery…”