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

Argelaguet, Ricard; Velten, Britta; Arnol, Damien; Dietrich, Sascha; Zenz, Thorsten; Marioni, John C.; Buettner, Florian; Huber, Wolfgang; Stegle, Oliver

doi:10.15252/msb.20178124

Cited by 837 publications

(869 citation statements)

References 68 publications

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“…In addition, besides cell clustering and trajectory inference, we note that DR methods are also used for many other analytic tasks in scRNAseq studies. For example, factor models for DR is an important modeling part for multiple scRNAseq data sets alignment [16], for integrative analysis of multiple omics data sets [55,56], as well as for deconvoluting bulk RNAseq data using cell type specific gene expression measurements from scRNAseq [57,58]. In addition, cell classification in scRNAseq also relies on a low-dimensional structure inferred from original scRNAseq through DR [59,60].…”

Section: Discussionmentioning

confidence: 99%

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Sun

Zhu

et al. 2019

Preprint

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Background: Dimensionality reduction (DR) is an indispensable analytic component for many areas of single cell RNA sequencing (scRNAseq) data analysis. Proper DR can allow for effective noise removal and facilitate many downstream analyses that include cell clustering and lineage reconstruction. Unfortunately, despite the critical importance of DR in scRNAseq analysis and the vast number of DR methods developed for scRNAseq studies, however, few comprehensive comparison studies have been performed to evaluate the effectiveness of different DR methods in scRNAseq.Results: Here, we aim to fill this critical knowledge gap by providing a comparative evaluation of a variety of commonly used DR methods for scRNAseq studies. Specifically, we compared 18 different DR methods on 30 publicly available scRNAseq data sets that cover a range of sequencing techniques and sample sizes. We evaluated the performance of different DR methods for neighborhood preserving in terms of their ability to recover features of the original expression matrix, and for cell clustering and lineage reconstruction in terms of their accuracy and robustness. We also evaluated the computational scalability of different DR methods by recording their computational cost. Conclusions:Based on the comprehensive evaluation results, we provide important guidelines for choosing DR methods for scRNAseq data analysis. We also provide all analysis scripts used in the present study at www.xzlab.org/reproduce.html. Together, we hope that our results will serve as an important practical reference for practitioners to choose DR methods in the field of scRNAseq analysis. our results can serve as an important guideline for practitioners to choose DR methods in the field of scRNAseq analysis.

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Section: Discussionmentioning

confidence: 99%

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Sun

Zhu

et al. 2019

Preprint

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“…Such an approach may help define disease trajectories earlier on in the natural history of osteoarthritis. To evaluate this, we applied multi-omics factor analysis (MOFA) 12 , an integrative method that can discover drivers of variability between samples or patients (latent factors) that is akin to a cross-data principal component analysis. The first two factors (axes of variation) were strongly associated with immune system processes and the extracellular matrix (see Methods, Supplementary Note), in keeping with the biological pathways identified to play an important role above.…”

Section: Patient Stratificationmentioning

confidence: 99%

“…To test for patient heterogeneity using a method that can detect both discrete clustering and a continuous spectrum of variation, we used multi-omics factor analysis (MOFA) 12 . MOFA can integrate data across omics levels and across tissues to discover drivers of variability between samples or patients.…”

Section: Multi-omics Factor Analysis (Mofa) and Correspondence To Sammentioning

confidence: 99%

Decoding the genomic basis of osteoarthritis

Steinberg

Southam

Butterfield

et al. 2019

Preprint

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Osteoarthritis is a serious joint disease that causes pain and functional disability for a quarter of a billion people worldwide 1 , with no disease-stratifying tools nor modifying therapy. Here, we use primary chondrocytes, synoviocytes and peripheral blood from patients with osteoarthritis to construct a molecular quantitative trait locus map of gene expression and protein abundance in disease. By integrating data across omics levels, we identify likely effector genes for osteoarthritis-associated genetic signals. We detect stark molecular differences between macroscopically intact (low-grade) and highly degenerated (high-grade) cartilage, reflecting activation of the extracellular matrix-receptor interaction pathway. Using unsupervised consensus clustering on transcriptome-wide sequencing, we identify molecularly-defined patient subgroups that correlate with clinical characteristics. Between-cluster differences are driven by inflammation, presenting the opportunity to stratify patients on the basis of their molecular profile for tailored intervention. We construct and validate a 7-gene classifier that reproducibly distinguishes between these disease subtypes. Finally, we identify potentially actionable compounds for disease modification and drug repositioning. Our findings contribute to both patient stratification and therapy development in this globally important area of unmet need.

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“…In a previous study, we developed Multi-Omics Factor Analysis (MOFA), a statistical framework for the integrative analysis of multiple data modalities 32 . Using a Bayesian Group Factor Analysis framework, MOFA infers a low-dimensional representation of the data in terms of a small number of (latent) factors that capture the global sources of variability ( Figure 1a ).…”

Section: Model Descriptionmentioning

confidence: 99%

MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data

Argelaguet

Arnol

Bredikhin

et al. 2019

Preprint

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Technological advances have enabled the joint analysis of multiple molecular layers at single cell resolution. At the same time, increased experimental throughput has facilitated the study of larger numbers of experimental conditions. While methods for analysing single-cell data that model the resulting structure of either of these dimensions are beginning to emerge, current methods do not account for complex experimental designs that include both multiple views (modalities or assays) and groups (conditions or experiments). Here we present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of structured single cell multi-modal data. MOFA+ builds upon a Bayesian Factor Analysis framework combined with fast GPU-accelerated stochastic variational inference. Similar to existing factor models, MOFA+ allows for interpreting variation in single-cell datasets by pooling information across cells and features to reconstruct a low-dimensional representation of the data. Uniquely, the model supports flexible group-level sparsity constraints that allow joint modelling of variation across multiple groups and views.To illustrate MOFA+, we applied it to single-cell data sets of different scales and designs, demonstrating practical advantages when analyzing datasets with complex group and/or view structure. In a multi-omics analysis of mouse gastrulation this joint modelling reveals coordinated changes between gene expression and epigenetic variation associated with cell fate commitment.

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

Cited by 837 publications

References 68 publications

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Decoding the genomic basis of osteoarthritis

MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data

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