Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes
As COVID-19 develops, dynamic changes occur in the patient’s immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We d...
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MDPI AG
2023-07-01
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| Online Access: | https://www.mdpi.com/2079-7737/12/7/947 |
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| author | Jing-Xin Ren Qian Gao Xiao-Chao Zhou Lei Chen Wei Guo Kai-Yan Feng Lin Lu Tao Huang Yu-Dong Cai |
| author_facet | Jing-Xin Ren Qian Gao Xiao-Chao Zhou Lei Chen Wei Guo Kai-Yan Feng Lin Lu Tao Huang Yu-Dong Cai |
| author_sort | Jing-Xin Ren |
| collection | DOAJ |
| description | As COVID-19 develops, dynamic changes occur in the patient’s immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system. |
| first_indexed | 2024-03-11T01:17:44Z |
| format | Article |
| id | doaj.art-89fe4cf254b742fd99a321e755660630 |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-11T01:17:44Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-89fe4cf254b742fd99a321e7556606302023-11-18T18:23:26ZengMDPI AGBiology2079-77372023-07-0112794710.3390/biology12070947Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell SubtypesJing-Xin Ren0Qian Gao1Xiao-Chao Zhou2Lei Chen3Wei Guo4Kai-Yan Feng5Lin Lu6Tao Huang7Yu-Dong Cai8School of Life Sciences, Shanghai University, Shanghai 200444, ChinaDepartment of Pharmacy, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, ChinaCenter for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai 200025, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaKey Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200030, ChinaDepartment of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, ChinaDepartment of Radiology, Columbia University Medical Center, New York, NY 10032, USABio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, ChinaSchool of Life Sciences, Shanghai University, Shanghai 200444, ChinaAs COVID-19 develops, dynamic changes occur in the patient’s immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system.https://www.mdpi.com/2079-7737/12/7/947immune cellCOVID-19 severitymachine learning |
| spellingShingle | Jing-Xin Ren Qian Gao Xiao-Chao Zhou Lei Chen Wei Guo Kai-Yan Feng Lin Lu Tao Huang Yu-Dong Cai Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes Biology immune cell COVID-19 severity machine learning |
| title | Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes |
| title_full | Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes |
| title_fullStr | Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes |
| title_full_unstemmed | Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes |
| title_short | Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes |
| title_sort | identification of gene markers associated with covid 19 severity and recovery in different immune cell subtypes |
| topic | immune cell COVID-19 severity machine learning |
| url | https://www.mdpi.com/2079-7737/12/7/947 |
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