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To illustrate MEFISTO, we apply the model to different datasets with spatial or temporal resolution, including an evolutionary atlas of organ development, a longitudinal microbiome study, a single-cell multi-omics atlas of mouse gastrulation and spatially resolved transcriptomics.įactor analysis is a first-line approach for the analysis of high-throughput sequencing data 1, 2, 3, 4, and is increasingly applied in the context of multi-omics datasets 5, 6, 7, 8. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. MEFISTO maintains the established benefits of factor analysis for multimodal data, but enables the performance of spatio-temporally informed dimensionality reduction, interpolation, and separation of smooth from non-smooth patterns of variation. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. ![]() ![]() Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology.
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