COVID-19 is a disease characterized by its seemingly unpredictable clinical outcomes. In order to better understand the molecular signature of the disease, a recent multi-omics study was done which looked at correlations between biomolecules and used a tree-based machine learning approach to predict clinical outcomes. This study specifically looked at patients admitted to the hospital experiencing COVID-19 or COVID-19 like symptoms. In this paper we examine the same multi-omics data, however we take a different approach, and we identify stable molecules of interest for further pathway analysis. We used stability selection, regularized regression models, enrichment analysis, and principal components analysis on proteomics, metabolomics, lipidomics, and RNA sequencing data, and we determined key molecules and biological pathways in disease severity, and disease status. In addition to the individual omics analyses, we perform the integrative method Sparse Multiple Canonical Correlation Analysis to analyse relationships of the different view of data. Our findings suggest that COVID-19 status is associated with the cell cycle and death, as well as the inflammatory response. This relationship is reflected in all four sets of molecules analyzed. We further observe that the metabolic processes, particularly processes to do with vitamin absorption and cholesterol are implicated in COVID-19 status and severity.
|Original language||English (US)|
|Issue number||4 April|
|State||Published - Apr 2022|
Bibliographical noteFunding Information:
This study was funded by the New Frontier in Research Fund [NFRFE-2018-00748], the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant [RGPIN-2019-04810], and the Vice-President (Research) Grant for Covid-19 at the University of Calgary in the form of funds to TC. This study was also funded by the National Institutes of Health (NIH) in the form of grants to SES [5KL2TR002492-04, 1R35GM142695-01].
Copyright: © 2022 Lipman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Machine Learning
PubMed: MeSH publication types
- Research Support, Non-U.S. Gov't
- Journal Article
- Research Support, N.I.H., Extramural