Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets
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Abstract
Abstract Multi-omic studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous datasets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omic datasets. 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 multiomics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.
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MS lesions in general, characterized by myelin loss, were captured by MOFA factors 1 and 2, whereas lesions containing foamy GPNMB+ microglia, represented by the Micro_D gene signature, were strongly associated with factor 3.
“We found that MS lesions in general, characterized by myelin loss, were captured by factors 1 and 2, whereas lesions containing foamy GPNMB+ microglia, represented by the Micro_D gene signature, were strongly associated with factor 3 (Fig. 5b–e).”
From Foamy microglia link oxylipins to disease progression in multiple sclerosis (2026) · cites this paper as [53]
The Results sections on chronic lymphocytic leukaemia describe MOFA factors associated with IGHV status, trisomy 12, and drug sensitivity, not MS lesions. Searches for myelin loss, GPNMB+ microglia, and the Micro_D signature found no passage addressing the claimed lesion–factor associations. The quoted sentence presents these as the citing authors’ own findings. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIn the MOFA comparison, missing percentages ranged between approximately 10% and 80%, and all methods showed increased mean squared error of prediction with increased missing percentage.
“The missing percentages considered ranged between ~10–~80%, and all methods demonstrated increases to mean squared error of prediction with increased missing percentage.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Argelaguet et al., 2018
In a simulated setting, MOFA imputation resulted in lower mean squared error of prediction than feature-wise mean, SoftImpute, and k-nearest neighbor across varying percentages of missingness.
“Comparing these approaches in a simulated setting, it was found that imputation by MOFA resulted in a lower mean squared error of prediction (i.e., imputation) relative to compared methods across varying percentages of missingness.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Argelaguet et al., 2018
MOFA disentangles heterogeneity shared across different omics to discover the principal source of variability.
“Also, Argelaguet (Argelaguet et al., 2018) have developed MOFA (Multi-Omics Factor Analysis) which disentangles the heterogeneity shared across different omics to discover the principal source of variability.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Argelaguet et al., 2018
MOFA can integrate partially overlapping datasets.
“It can integrate partially overlapping datasets.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Argelaguet et al., 2018
Single-cell multi-omic integration can occur via consensus clustering approaches, multi-omic factor analysis, or multi-omic gene regulatory network inference.
“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 (Colomé‐Tatché & Theis, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Argelaguet et al, 2018
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- Authors
- Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O.
- Affiliations
- European Bioinformatics Institute (GB)
- European Molecular Biology Laboratory (DE)
- University Hospital Heidelberg (DE)
- German Cancer Research Center (DE)
- Corresponding author
- Argelaguet, Ricard
- Velten, Britta
- ORCID
- Venue
- Molecular Systems Biology
- Published
- 2018-06-01
- Volume
- 14
- Issue
- 6
- Pages
- e8124-e8124
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC6010767
- OpenAlex
- W2808845633
- Cited by
- 1,510
- References
- 77
- Citations by year
- 2026: 376 · 2025: 303 · 2024: 203 · 2023: 178 · 2022: 124 · 2021: 155 · 2020: 103 · 2019: 58 · 2018: 9 · 2016: 1
- FWCI
- 28.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, other-oa)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Site cant be reached (submittedVersion, cc-by)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Apollo (cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- European Union (633974)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- MOFA, multi-omics integration, unsupervised dimensionality reduction, hidden factors, disease heterogeneity, chronic lymphocytic leukemia, somatic mutations, DNA methylation, RNA expression, drug response, immunoglobulin heavy chain variable region, trisomy 12, oxidative stress response, single-cell multi-omics, cell differentiation, missing data imputation, outlier detection
- MeSH terms
- Antineoplastic Agents, Computer Simulation, Humans, Software, Models, Statistical, Leukemia, Lymphocytic, Chronic, B-Cell, Oxidative Stress, Computational Biology, Transcriptome, Datasets as Topic
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