Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics - Wang, Xinyi; Almet , Axel A.; Nie, Qing (Frontiers in Genetics, 2022)
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.