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bioRxiv · 10.1101/2020.11.03.366674

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

Velten, B.·Braunger, J. M.·Arnol, D.·Argelaguet, R.·Stegle, O.
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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.

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Velten, B., Braunger, J. M., Arnol, D., Argelaguet, R., Stegle, O.. 2020-11-05. Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO. https://doi.org/10.1101/2020.11.03.366674

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