| 论文作者 |
Ren, JX; Gao, Q; Zhou, XC; Chen, L; Guo, W; Feng, KY; Lu, L; Huang, T; Cai, YD |
| 摘要 |
Simple Summary It is known that COVID-19 causes dynamic changes in the immune system. At different stages of the course of COVID-19, the immune cells may exhibit different patterns, which have not been fully uncovered. In this study, a machine-learning-based method was designed to deeply analyze the scRNA-seq data of three types of immune cells from patients with COVID-19, including B cells, T cells, and myeloid cells. Four levels of COVID-19 severity/outcome were involved for each cell type. As a result, several essential genes were obtained, and some of them could be confirmed to be related to SARS-CoV-2 infection. 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. |
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