Identification of Single-Cell RNA Sequencing Molecular Signatures for COVID-19 Infection Severity Classification.

In this study, we propose a graph-based framework to identify scRNA-seq molecular signatures for COVID-19 infection severity identification. We conduct extensive experiments on scRNA-seq data from bronchoalveolar lavage fluid (BALF) with four machine learning models: Support Vector Machine, Random Forest, Graph Convolutional Network (GCN), and Graph Attention Network (GAT). In addition, we employ an explainable artificial intelligence approach, GNNExplainer, to interpret model predictions by identifying the top 15 features that contribute to the severity classification. Our finding suggests that graphical models could accurately distinguish healthy people from COVID-19 patients (F1-score > 0.9) based on patient scRNA-seq data, and traditional machine learning approaches could accurately distinguish COVID-19 patients with different severity (F1-score > 0.99), along with meaningful molecular signatures identification and discovery. Our implementation is available on a github repository: https://github.com/Da2f1eW/COVID19_Infection_Severity_Classification.