AUTHOR=Wang Xinyi , Almet  Axel A. , Nie Qing 

TITLE=Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.948508

DOI=10.3389/fgene.2022.948508

ISSN=1664-8021

ABSTRACT=Cell-cell interactions (CCI) play significant roles in manipulating biological functions of cells. Analyzing the differences in CCI between healthy and diseased conditions of a biological system yields greater insight than analyzing either condition alone. There has been a recent and rapid growth of methods to infer CCI from single-cell RNA-sequencing (scRNA-seq), revealing complex CCI networks at a previously inaccessible scale. However, the majority of current CCI analyses from scRNA-seq data focuses on direct comparisons between individual CCI networks of individual samples from patients, rather than ``group-level'' comparisons between sample groups of patients comprising different conditions. To illustrate new biological features among different disease status, we investigated the diversity of key network features on groups of CCI networks, as defined by different disease status. We considered three levels of network features: node level, as defined by cell type; node-to-node level; and network-level features. By applying these analysis to a large-scale single cell RNA-sequencing dataset of COVID-19, we observe biologically meaningful patterns aligned with the progression and subsequent convalescence of COVID-19.