Predicting COVID-19 Infection Severity Using Graph Convolutional Neural Networks on Single Cell RNA Seq Data

Date
2021
Authors
Huang, Wenjian
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract

One of the mysteries of Coronavirus Disease 19 (COVID-19) is why some people suffer severe symptoms, even life-threatening complications, while others suffer no symptoms or just mild ones. Several studies have related the severity of COVID-19 infection to immune system features resulting in more vulnerable groups to this viral infection. The goal of this study is to elucidate the response signatures of COVID-19 infection by identifying gene markers and biological processes related to patients with different degrees of severity. In particular, single-cell RNA-Sequencing (scRNA-seq) datasets of severe and mild cases were compared to uninfected cases using a Graph Convolutional Neural Network(GCNN) approach. We first examined the properties of the filtered dataset. Expression levels of genes were normalized, clustered, presented using the UMAP method. Further, a novel GCNN approach has been employed to establish deep learning models to classify and predict different cases of COVID-19 infection. A novel network-based interpretation of GCNN models was also proposed to find potential gene markers for COVID-19 by determining the signatures learned by the nodes in the graph. The highlighted network modules were further analyzed using DAVID to find significant biological processes. The novel GCNN approach has been employed to establish deep learning models to classify and predict the severity of COVID-19 infection, identify the leading genes and functional modules for immune response features for different severities of COVID-19 infection.

Description
This item is available only to currently enrolled UTSA students, faculty or staff.
Keywords
COVID-19, Infection severity, Convolutional neural networks, Single cell, RNA Seq Data, Coronavirus disease 2019
Citation
Department
Electrical and Computer Engineering