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

View ORCID ProfileRicard Argelaguet, View ORCID ProfileDamien Arnol, View ORCID ProfileDanila Bredikhin, Yonatan Deloro, View ORCID ProfileBritta Velten, View ORCID ProfileJohn C Marioni, View ORCID ProfileOliver Stegle
doi: https://doi.org/10.1101/837104
Ricard Argelaguet
1European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
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  • ORCID record for Ricard Argelaguet
  • For correspondence: ricard@ebi.ac.uk marioni@ebi.ac.uk o.stegle@dkfz-heidelberg.de
Damien Arnol
1European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
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Danila Bredikhin
2European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
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Yonatan Deloro
1European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
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Britta Velten
2European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
3Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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John C Marioni
1European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
4Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK
5Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
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  • For correspondence: ricard@ebi.ac.uk marioni@ebi.ac.uk o.stegle@dkfz-heidelberg.de
Oliver Stegle
2European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
1European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
3Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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  • For correspondence: ricard@ebi.ac.uk marioni@ebi.ac.uk o.stegle@dkfz-heidelberg.de
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Abstract

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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Posted November 09, 2019.
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MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data
Ricard Argelaguet, Damien Arnol, Danila Bredikhin, Yonatan Deloro, Britta Velten, John C Marioni, Oliver Stegle
bioRxiv 837104; doi: https://doi.org/10.1101/837104
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MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data
Ricard Argelaguet, Damien Arnol, Danila Bredikhin, Yonatan Deloro, Britta Velten, John C Marioni, Oliver Stegle
bioRxiv 837104; doi: https://doi.org/10.1101/837104

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