Application of Aligned-UMAP to longitudinal biomedical studies.

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.

Déclaration de conflit d'intérêts

A.D., H.I., M.A.N., and F.F. declare the following competing financial interests, as their participation in this project was part of a competitive contract awarded to Data Tecnica International, LLC, by the NIH to support open science research. M.A.N. also currently serves on the scientific advisory board for Character Bio and is an advisor to Neuron23, Inc. The study’s funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. All authors and the public can access all data and statistical programming code used in this project for the analyses and results generation. F.F. takes final responsibility for the decision to submit the paper for publication.