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Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2

Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a sin...

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Autores principales: Sova, Milan, Kudelka, Milos, Raska, Milan, Mizera, Jan, Mikulkova, Zuzana, Trajerova, Marketa, Ochodkova, Eliska, Genzor, Samuel, Jakubec, Petr, Borikova, Alena, Stepanek, Ladislav, Kosztyu, Petr, Kriegova, Eva
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9697085/
https://www.ncbi.nlm.nih.gov/pubmed/36366522
http://dx.doi.org/10.3390/v14112422
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author Sova, Milan
Kudelka, Milos
Raska, Milan
Mizera, Jan
Mikulkova, Zuzana
Trajerova, Marketa
Ochodkova, Eliska
Genzor, Samuel
Jakubec, Petr
Borikova, Alena
Stepanek, Ladislav
Kosztyu, Petr
Kriegova, Eva
author_facet Sova, Milan
Kudelka, Milos
Raska, Milan
Mizera, Jan
Mikulkova, Zuzana
Trajerova, Marketa
Ochodkova, Eliska
Genzor, Samuel
Jakubec, Petr
Borikova, Alena
Stepanek, Ladislav
Kosztyu, Petr
Kriegova, Eva
author_sort Sova, Milan
collection PubMed
description Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a single factor but a combination of multiple factors that contribute to patient characterisation and host response. Therefore, multivariate approaches are needed to analyse these complex patient datasets, which are impossible to analyse with univariate comparisons (e.g., one immune cell subset versus one clinical factor). Using a SARS-CoV-2 infection as an example, we employed a patient similarity network (PSN) approach to assess the relationship between host immune factors and the clinical course of infection and performed visualisation and data interpretation. A PSN analysis of ~85 immunological (cellular and humoral) and ~70 clinical factors in 250 recruited patients with coronavirus disease (COVID-19) who were sampled four to eight weeks after a PCR-confirmed SARS-CoV-2 infection identified a minimal immune signature, as well as clinical and laboratory factors strongly associated with disease severity. Our study demonstrates the benefits of implementing multivariate network approaches to identify relevant factors and visualise their relationships in a SARS-CoV-2 infection, but the model is generally applicable to any complex dataset.
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spelling pubmed-96970852022-11-26 Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2 Sova, Milan Kudelka, Milos Raska, Milan Mizera, Jan Mikulkova, Zuzana Trajerova, Marketa Ochodkova, Eliska Genzor, Samuel Jakubec, Petr Borikova, Alena Stepanek, Ladislav Kosztyu, Petr Kriegova, Eva Viruses Article Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a single factor but a combination of multiple factors that contribute to patient characterisation and host response. Therefore, multivariate approaches are needed to analyse these complex patient datasets, which are impossible to analyse with univariate comparisons (e.g., one immune cell subset versus one clinical factor). Using a SARS-CoV-2 infection as an example, we employed a patient similarity network (PSN) approach to assess the relationship between host immune factors and the clinical course of infection and performed visualisation and data interpretation. A PSN analysis of ~85 immunological (cellular and humoral) and ~70 clinical factors in 250 recruited patients with coronavirus disease (COVID-19) who were sampled four to eight weeks after a PCR-confirmed SARS-CoV-2 infection identified a minimal immune signature, as well as clinical and laboratory factors strongly associated with disease severity. Our study demonstrates the benefits of implementing multivariate network approaches to identify relevant factors and visualise their relationships in a SARS-CoV-2 infection, but the model is generally applicable to any complex dataset. MDPI 2022-10-31 /pmc/articles/PMC9697085/ /pubmed/36366522 http://dx.doi.org/10.3390/v14112422 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sova, Milan
Kudelka, Milos
Raska, Milan
Mizera, Jan
Mikulkova, Zuzana
Trajerova, Marketa
Ochodkova, Eliska
Genzor, Samuel
Jakubec, Petr
Borikova, Alena
Stepanek, Ladislav
Kosztyu, Petr
Kriegova, Eva
Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2
title Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2
title_full Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2
title_fullStr Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2
title_full_unstemmed Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2
title_short Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2
title_sort network analysis for uncovering the relationship between host response and clinical factors to virus pathogen: lessons from sars-cov-2
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9697085/
https://www.ncbi.nlm.nih.gov/pubmed/36366522
http://dx.doi.org/10.3390/v14112422
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