PuSH - Publication Server of Helmholtz Zentrum München: Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.

Navigation

Home

Deutsch

Research

Advanced Search

Browse by ...

... Journal

... Publication Type

... Research Data

... Publication Year

Publication overview

Support & Contact

Contact persons

Help

Data protection

Argelaguet, R.* ; Velten, B.* ; Arnol, D.* ; Dietrich, S.* ; Zenz, T.* ; Marioni, J.C.* ; Buettner, F. ; Huber, W.* ; Stegle, O.*

Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.

Mol. Syst. Biol. 14:e8124 (2018)

Publ. Version/Full Text

DOI

PMC

	Open Access Gold

Abstract
Metrics
Extra information

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.

Altmetric

Additional Metrics?

[➜Log in]

Edit extra informations Login

Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Data Integration ; Dimensionality Reduction ; Multi-omics ; Personalized Medicine ; Single-cell Omics; Polycyclic Aromatic-hydrocarbons; Lung Epithelial-cells; Yangtze-river Delta; 6 European Cities; Ambient Air; Oxidative Stress; Mouse Lung; A549 Cells; Cytotoxic Responses; Seasonal-variation

ISSN (print) / ISBN 1744-4292

e-ISSN 1744-4292

Journal Molecular Systems Biology

Quellenangaben Volume: 14, Issue: 6, Article Number: e8124

Publisher EMBO Press

Publishing Place Po Box 211, 1000 Ae Amsterdam, Netherlands

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)