Identifying SARS-CoV-2 infected cells with scVDN

Huan Hu; Zhen Feng; Xinghao Steven Shuai; Jie Lyu; Xiang Li; Hai Lin; Jianwei Shuai

doi:10.3389/fmicb.2023.1236653

Identifying SARS-CoV-2 infected cells with scVDN

Front Microbiol. 2023 Jul 10:14:1236653. doi: 10.3389/fmicb.2023.1236653. eCollection 2023.

Authors

Huan Hu^{1

2

3}, Zhen Feng⁴, Xinghao Steven Shuai⁵, Jie Lyu², Xiang Li¹, Hai Lin², Jianwei Shuai^{1

2

3}

Affiliations

¹ Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China.
² Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, China.
³ National Institute for Data Science in Health and Medicine, and State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, China.
⁴ First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, China.
⁵ Department of Biomedical Science, University of California Riverside, Riverside, CA, United States.

Abstract

Introduction: Single-cell RNA sequencing (scRNA-seq) is a powerful tool for understanding cellular heterogeneity and identifying cell types in virus-related research. However, direct identification of SARS-CoV-2-infected cells at the single-cell level remains challenging, hindering the understanding of viral pathogenesis and the development of effective treatments.

Methods: In this study, we propose a deep learning framework, the single-cell virus detection network (scVDN), to predict the infection status of single cells. The scVDN is trained on scRNA-seq data from multiple nasal swab samples obtained from several contributors with varying cell types. To objectively evaluate scVDN's performance, we establish a model evaluation framework suitable for real experimental data.

Results and discussion: Our results demonstrate that scVDN outperforms four state-of-the-art machine learning models in identifying SARS-CoV-2-infected cells, even with extremely imbalanced labels in real data. Specifically, scVDN achieves a perfect AUC score of 1 in four cell types. Our findings have important implications for advancing virus research and improving public health by enabling the identification of virus-infected cells at the single-cell level, which is critical for diagnosing and treating viral infections. The scVDN framework can be applied to other single-cell virus-related studies, and we make all source code and datasets publicly available on GitHub at https://github.com/studentiz/scvdn.

Keywords: SARS-CoV-2; deep learning; imbalanced data; single-cell RNA sequencing; virus detection.