Multi-omics factor analysis

• Verfasst von: Argelaguet, Ricard [VerfasserIn]
• Verfasst von: Velten, Britta [VerfasserIn]
• Verfasst von: Arnol, Damien [VerfasserIn]
• Verfasst von: Dietrich, Sascha [VerfasserIn]
• Verfasst von: Zenz, Thorsten [VerfasserIn]
• Verfasst von: Marioni, John C [VerfasserIn]
• Verfasst von: Buettner, Florian [VerfasserIn]
• Verfasst von: Huber, Wolfgang [VerfasserIn]
• Verfasst von: Stegle, Oliver [VerfasserIn]
• Titel: Multi-omics factor analysis
• Titelzusatz: a framework for unsupervised integration of multi-omics data sets
• Verf.angabe: Ricard Argelaguet, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C Marioni, Florian Buettner, Wolfgang Huber & Oliver Stegle
• E-Jahr: 2018
• Jahr: 29 May 2018
• Fussnoten: Gesehen am 03.06.2020
• Titel Quelle: Enthalten in: Molecular systems biology
• Ort Quelle: [London] : Nature Publishing Group UK, 2005
• Jahr Quelle: 2018
• Band/Heft Quelle: 14 (2018,6) Artikel-Nummer e8124, Seite 1-13, 13 Seiten
• ISSN Quelle: 1744-4292
• DOI: doi:10.15252/msb.20178124
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: data integration
• Sach-SW: dimensionality reduction
• Sach-SW: multi-omics
• Sach-SW: personalized medicine
• Sach-SW: single-cell omics
• K10plus-PPN: 1699333777

Abstract

Abstract Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.