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Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO

View ORCID ProfileBritta Velten, View ORCID ProfileJana M. Braunger, View ORCID ProfileDamien Arnol, View ORCID ProfileRicard Argelaguet, View ORCID ProfileOliver Stegle
doi: https://doi.org/10.1101/2020.11.03.366674
Britta Velten
1Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Jana M. Braunger
1Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Damien Arnol
2European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
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Ricard Argelaguet
2European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
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Oliver Stegle
1Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
3European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
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Abstract

Factor analysis is among the most-widely used methods for dimensionality reduction in genome biology, with applications from personalized health to single-cell studies. Existing implementations of factor analysis assume independence of the observed samples, an assumption that fails in emerging spatio-temporal profiling studies. Here, we present MEFISTO, a flexible and versatile toolbox for modelling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multi-modal data, but enables performing spatio-temporally informed dimensionality reduction, interpolation and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. We demonstrate MEFISTO through applications to an evolutionary atlas of mammalian organ development, where the model reveals conserved and evolutionary diverged developmental programs. In applications to a longitudinal microbiome study in infants, birth mode and diet were highlighted as major causes for heterogeneity in the temporally-resolved microbiome over the first years of life. Finally, we demonstrate that the proposed framework can also be applied to spatially resolved transcriptomics.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 05, 2020.
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Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO
Britta Velten, Jana M. Braunger, Damien Arnol, Ricard Argelaguet, Oliver Stegle
bioRxiv 2020.11.03.366674; doi: https://doi.org/10.1101/2020.11.03.366674
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Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO
Britta Velten, Jana M. Braunger, Damien Arnol, Ricard Argelaguet, Oliver Stegle
bioRxiv 2020.11.03.366674; doi: https://doi.org/10.1101/2020.11.03.366674

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