Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning
| dc.contributor.author | Flores, Mario A. | |
| dc.contributor.author | Paniagua, Karla | |
| dc.contributor.author | Huang, Wenjian | |
| dc.contributor.author | Ramirez, Ricardo | |
| dc.contributor.author | Falcon, Leonardo | |
| dc.contributor.author | Liu, Andy | |
| dc.contributor.author | Chen, Yidong | |
| dc.contributor.author | Huang, Yufei | |
| dc.contributor.author | Jin, Yufang | |
| dc.date.accessioned | 2022-12-22T14:35:44Z | |
| dc.date.available | 2022-12-22T14:35:44Z | |
| dc.date.issued | 2022-12-01 | |
| dc.date.updated | 2022-12-22T14:35:46Z | |
| dc.description.abstract | The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with some of them presenting a mild infection, while others present severe symptoms or even death. The identification of the molecular states related to the severity of a COVID-19 infection has become of the utmost importance to understanding the differences in critical immune response. In this study, we computationally processed a set of publicly available single-cell RNA-Seq (scRNA-Seq) data of 12 Bronchoalveolar Lavage Fluid (BALF) samples diagnosed as having a mild, severe, or no infection, and generated a high-quality dataset that consists of 63,734 cells, each with 23,916 genes. We extended the cell-type and sub-type composition identification and our analysis showed significant differences in cell-type composition in mild and severe groups compared to the normal. Importantly, inflammatory responses were dramatically elevated in the severe group, which was evidenced by the significant increase in macrophages, from 10.56% in the normal group to 20.97% in the mild group and 34.15% in the severe group. As an indicator of immune defense, populations of T cells accounted for 24.76% in the mild group and decreased to 7.35% in the severe group. To verify these findings, we developed several artificial neural networks (ANNs) and graph convolutional neural network (GCNN) models. We showed that the GCNN models reach a prediction accuracy of the infection of 91.16% using data from subtypes of macrophages. Overall, our study indicates significant differences in the gene expression profiles of inflammatory response and immune cells of severely infected patients. | |
| dc.description.department | Electrical and Computer Engineering | |
| dc.identifier | doi: 10.3390/genes13122264 | |
| dc.identifier.citation | Genes 13 (12): 2264 (2022) | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12588/1474 | |
| dc.rights | Attribution 4.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | deep learning | |
| dc.subject | single-cell RNA-Seq | |
| dc.subject | SARS-CoV-2 | |
| dc.subject | cell type identification | |
| dc.subject | infection severity | |
| dc.title | Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning | |
| dc.type | Article |