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

ABSTRACT: Standard transcriptomic analyses alone have limited power in capturing the molecular mechanisms driving disease pathophysiology and outcomes. To overcome this, unsupervised network analyses are used to identify clusters of genes that can be associated with distinct molecular mechanisms and outcomes for a disease. In this study, we developed an integrated network analysis framework that integrates transcriptional signatures from multiple model systems with protein-protein interaction data to find gene modules. Through a meta-analysis of different enriched features from these gene modules, we extract communities of highly interconnected features. These clusters of higher-order features, working as a multifeatured machine, enable collective assessment of their contribution for disease or phen

SUBMITTER: Ghandikota S 

PROVIDER: S-EPMC8020120 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature