Protocol to identify biomarkers in patients with post-COVID condition using multi-omics and machine learning analysis of human plasma.

Journal: STAR protocols
PMID: 38678567

Abstract

Here, we present a workflow for analyzing multi-omics data of plasma samples in patients with post-COVID condition (PCC). Applicable to various diseases, we outline steps for data preprocessing and integrating diverse assay datasets. Then, we detail statistical analysis to unveil plasma profile changes and identify biomarker-clinical variable associations. The last two steps discuss machine learning techniques for unsupervised clustering of patients based on their inherent molecular similarities and feature selection to identify predictive biomarkers. For complete details on the use and execution of this protocol, please refer to Wang et al..

Authors

  • Mobin Khoramjoo
    Department of Physiology, University of Alberta, Edmonton, AB T6G 2H7, Canada; Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB T6G 2S2, Canada.
  • Karthik Srinivasan
    McGovern Institute for Brain Research, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, Massachusetts 02139, USA.
  • Kaiming Wang
    Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB T6G 2S2, Canada; Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, AB T6G 2G3, Canada.
  • David Wishart
  • Vinay Prasad
    University of California San Francisco, 550 16th St, 2nd Fl, San Francisco, CA, 94158, USA. Electronic address: vinayak.prasad@ucsf.edu.
  • Gavin Y Oudit
    Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB T6G 2S2, Canada; Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, AB T6G 2G3, Canada. Electronic address: gavin.oudit@ualberta.ca.

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