Visualization of incrementally learned projection trajectories for longitudinal data.

Journal: Scientific reports
PMID: 38866809

Abstract

Longitudinal studies that continuously generate data enable the capture of temporal variations in experimentally observed parameters, facilitating the interpretation of results in a time-aware manner. We propose IL-VIS (incrementally learned visualizer), a new machine learning pipeline that incrementally learns and visualizes a progression trajectory representing the longitudinal changes in longitudinal studies. At each sampling time point in an experiment, IL-VIS generates a snapshot of the longitudinal process on the data observed thus far, a new feature that is beyond the reach of classical static models. We first verify the utility and correctness of IL-VIS using simulated data, for which the true progression trajectories are known. We find that it accurately captures and visualizes the trends and (dis)similarities between high-dimensional progression trajectories. We then apply IL-VIS to longitudinal multi-electrode array data from brain cortical organoids when exposed to different levels of quinolinic acid, a metabolite contributing to many neuroinflammatory diseases including Alzheimer's disease, and its blocking antibody. We uncover valuable insights into the organoids' electrophysiological maturation and response patterns over time under these conditions.

Authors

  • Tamasha Malepathirana
    Department of Mechanical Engineering, University of Melbourne, Melbourne, 3010, VIC, Australia.
  • Damith Senanayake
    Department of Mechanical Engineering, University of Melbourne, Melbourne, 3010, VIC, Australia.
  • Vini Gautam
    School of Chemical and Biomedical Engineering, University of Melbourne, Melbourne, 3010, VIC, Australia.
  • Martin Engel
    Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, 2522, NSW, Australia.
  • Rachelle Balez
    Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, 2522, NSW, Australia.
  • Michael D Lovelace
    Applied Neurosciences Program, Peter Duncan Neurosciences Research Unit, St. Vincent's Centre for Applied Medical Research, 405 Liverpool St., Darlinghurst, Sydney, 2010, NSW, Australia.
  • Gayathri Sundaram
    Bionyeri Pty Ltd., Hornsby, 2077, NSW, Australia.
  • Benjamin Heng
    Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, 2109, NSW, Australia.
  • Sharron Chow
    Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, 2109, NSW, Australia.
  • Christopher Marquis
    School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Sydney, 2052, NSW, Australia.
  • Gilles J Guillemin
    Applied Neurosciences Program, Peter Duncan Neurosciences Research Unit, St. Vincent's Centre for Applied Medical Research, 405 Liverpool St., Darlinghurst, Sydney, 2010, NSW, Australia.
  • Bruce Brew
    Applied Neurosciences Program, Peter Duncan Neurosciences Research Unit, St. Vincent's Centre for Applied Medical Research, 405 Liverpool St., Darlinghurst, Sydney, 2010, NSW, Australia.
  • Chennupati Jagadish
    Research School of Physics, Australian National University, Canberra, 2601, ACT, Australia.
  • Lezanne Ooi
    Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, 2522, NSW, Australia. lezanne@uow.edu.au.
  • Saman Halgamuge

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