Single‐cell co‐expression analysis using computational machine learning reveals oxidative, immunopathologic, and myocardial responses for multi‐organ failure in COVID‐19

Single-cell co-expression analysis using computational machine learning reveals oxidative, immunopathologic, and myocardial responses for multi-organ failure in COVID-19Dear Editor, Since more than half of the hospitalized coronavirus disease 2019 (COVID-19) patients died of multi-organ failure, it suggested severe challenges to COVID-19 management in terms of currently limited knowledge.1 Herein, taking advantage of bulk RNA-seq data (GSE162113 and GSE164805) and single-cell RNA-seq data (GSE165080), this study identified potential gene modules representing 'Oxidative impairment', 'Immunopathological response', and 'Myocardial responses' in COVID-19 using R language programming.Also, drug candidates for multiorgan failure in COVID-19 were indicated (Figure 1A). 1. Functional gene modules representing "oxidative impairment", "immune-pathological response", and "myocardial dysfunction" in multi-organ failure of COVID-19 have been identified by single-cell co-expression analysis using machine learning 2. The pseudo time of FCGR3A + monocytes to dendritic cells might be prior to FCGR3A + monocytes-derived pro-inflammatory macrophage in COVID-19.3. FDA-approved 20 medicines are potentially repurposed for COVID-19 management For co-expression analysis, using the R package WGCNA, those genes (3702 genes, Data S1) in GSE162113 with expression variances greater than the 90th percentile of the whole genome were involved in hierarchical clustering (Figure 1B). 2 The scale-free soft threshold was determined by the criteria of approximating scalefree topology (Figure S1A).The co-expressed genes among modules were shown in Figure 1C.Finally, 12 co-expression modules were clustered into six modules (Figure S1B,D).The adjacency matrix-based pairwise relationships among modules were shown in Figure S1C.In addition, we further quantified the correlation profiles between modules by calculating the Module

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   WGCNA: an R package for weighted correlation network analysis · 2008

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