Application of Aligned-UMAP to longitudinal biomedical studies

Anant Dadu, Vipul K. Satone, Rachneet Kaur, Mathew J. Koretsky, Hirotaka Iwaki, Yue A. Qi, Daniel M. Ramos, Brian Avants, Jacob Hesterman, Roger Gunn, Mark R. Cookson, Michael E. Ward, Andrew B. Singleton, Roy H. Campbell, Mike A. Nalls, Faraz Faghri

Research output: Contribution to journalArticlepeer-review


High-dimensional data analysis starts with projecting the data to low dimensions to visualize and understand the underlying data structure. Several methods have been developed for dimensionality reduction, but they are limited to cross-sectional datasets. The recently proposed Aligned-UMAP, an extension of the uniform manifold approximation and projection (UMAP) algorithm, can visualize high-dimensional longitudinal datasets. We demonstrated its utility for researchers to identify exciting patterns and trajectories within enormous datasets in biological sciences. We found that the algorithm parameters also play a crucial role and must be tuned carefully to utilize the algorithm's potential fully. We also discussed key points to remember and directions for future extensions of Aligned-UMAP. Further, we made our code open source to enhance the reproducibility and applicability of our work. We believe our benchmarking study becomes more important as more and more high-dimensional longitudinal data in biomedical research become available.

Original languageEnglish (US)
Article number100741
Issue number6
StatePublished - Jun 9 2023
Externally publishedYes


  • Alzheimer's disease
  • DSML 3: Development/pre-production: Data science output has been rolled out/validated across multiple domains/problems
  • Parkinson's disease
  • clinical data
  • genomics
  • iPSC
  • longitudinal data
  • machine learning
  • proteomics
  • time-series
  • unsupervised learning

ASJC Scopus subject areas

  • General Decision Sciences


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