Summary Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases. For complete details on the use and execution of this protocol, please refer to Ghandikota et al. (2021) . Graphical abstract Highlights • Steps for meta-analysis of multiple transcriptomic studies and protein interactions • Network analysis-based workflow to identify gene and functional modules • Data-driven higher-order functional features provide a basis for characterizing disease Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases.
【저자키워드】 immunology, Gene Expression, bioinformatics, Systems biology, Genomics, Health Sciences, Single Cell, RNAseq, 【초록키워드】 COVID-19, Meta-analysis, protocol, underlying disease, molecular mechanism, outcomes, pathophysiology, single-cell RNA-seq, protein interaction, network analysis, network, disease, interactions, association, Analysis, Standard, capture, can not, Abstract, other diseases, molecular mechanisms, heterogeneous, protein interactions, step, multiple model systems, Complete, transcriptomic, feature, transcriptional, identify, functional, complexes, pathophysiological, 【제목키워드】 COVID-19, Data analysis, functional,