Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO.

ABSTRACT: Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. 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. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and align

SUBMITTER: Velten B 

PROVIDER: S-EPMC8828471 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature