Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19. Ghandikota et al

Supplemental dataset associated with our work, "Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19". In this study, we proposed an integrated network analysis framework that integrates transcriptional gene signatures from multiple model systems with protein-protein interactions to find gene clusters. By performing a meta-analysis of multiple feature types enriched in these gene modules, we extract communities of similar and interconnected features. These higher-order feature clusters, working as a multifeatured machine, enable us to assess their contributions towards a disease or phenotype. We show the utility of our proposed workflow using transcriptomics data from three different models of SARS-CoV-2 infection and identified several pathways and biological processes that could help towards understanding and hypothesizing molecular signatures involved in COVID-19.