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

Nat Methods. 2022 Feb;19(2):179-186. doi: 10.1038/s41592-021-01343-9. Epub 2022 Jan 13.


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 aligning the underlying patterns of variation in a data-driven manner. 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Computational Biology / methods*
  • Databases, Factual*
  • Evolution, Molecular
  • Gastrointestinal Microbiome / physiology*
  • Gene Expression Regulation, Developmental*
  • Humans
  • Infant
  • Longitudinal Studies
  • Single-Cell Analysis
  • Software*
  • Spatio-Temporal Analysis

Associated data

  • figshare/10.6084/m9.figshare.13233860.v1