Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets

By R. Argelaguet, B. Velten, Damien Arnol, S. Dietrich, T. Zenz, John C. Marioni, Florian Buettner, Wolfgang Huber, Oliver Stegle. Molecular Systems Biology, 2018

1,179 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d6cbab343a4e4ea6f8503fb17885c46ed0e20ff1

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Citations in the corpus

Integration strategies of multi-omics data for machine learning analysis (2021, background): “MOFA/MOFA+ [141,142] Bayesian Factor Analysis biomarker discovery, systemic knowledge MOFA found new biomarkers and pathways associated with Alzeihmer’s disease based on MO data including proteomics, metabolomics, lipidomics [143].”
Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources (2020, methodology): “Multi-Omics Factor Analysis (MOFA) [56] and Multiple Co-Inertia Analysis (MCIA) [57] are two techniques for doing so.”
Joint probabilistic modeling of single-cell multi-omic data with totalVI (2021, background): “Like other multi-omics analysis methods [31, 50, 51], totalVI assumes that RNA and protein measurements are generated from the same latent space of cells that captures their state.”
MOLI: multi-omics late integration with deep neural networks for drug response prediction (2019, methodology): “Multi-omics data provide a machine learning model with different views of the same sample and promise better characterization of biological processes (Argelaguet et al., 2018; Wang et al., 2014). Multi-omics data have been exploited for different problems such as driver gene identification (Dimitrak”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “Single-cell multi-omic integration can occur via consensus clustering approaches, multi-omic factor analysis (Argelaguet et al , 2018), or multi-omic gene regulatory network inference (Colome´-Tatche´ & Theis, 2018).”
Computational principles and challenges in single-cell data integration (2021, methodology): “Similarly, in a dataset of mouse postimplantation embryos where scNMT-seq was used to simultaneously profile RNA expression, DNA methylation and chromatin accessibility, an integrative analysis using MOFA revealed the existence of lineage-specific enhancers that are associated with germ layer commit”
Network analysis methods for studying microbial communities: A mini review (2021, methodology): “To achieve this, Argelaguet et al. [91] proposed a method called Multi-Omics Factor Analysis (MOFA) which uses group factor analysis [92] to provide an integrative analysis of a set of samples with measurements from different data modalities, making it an attractive tool for trans -kingdom analysis…”