In-silico generation of high-dimensional immune response data in patients using a deep neural network.

Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology
Published Date: Dec 27, 2022

Abstract

Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness.

Authors

  • Ramin Fallahzadeh
  • Neda H Bidoki
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Ina A Stelzer
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Martin Becker
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Ivana Marić
    Department of Pediatrics, Stanford University, Stanford, California, USA.
  • Alan L Chang
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Anthony Culos
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Thanaphong Phongpreecha
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Maria Xenochristou
    Stanford University, Stanford, CA, USA.
  • Davide De Francesco
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Camilo Espinosa
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Eloise Berson
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Franck Verdonk
    Department of Anaesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
  • Martin S Angst
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Brice Gaudilliere
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Nima Aghaeepour
    Departments of Anesthesiology, Pain, and Peri-operative Medicine and Biomedical Data Sciences, Stanford University, Stanford, CA, USA.

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