Supporting data for Meta-analysis of host responses identifies gene network dysfunction during viral infection

Viral infection of a susceptible host is often accompanied by the induction a response in the host to attempt to restrict the harm caused by these invading pathogens. My project utilize a two-pronged approach combining meta-analysis and co-expression to identify how different hosts respond to viral infection. This dataset contains the processed data files for many stages of this analysis. Meta-analysis through recurrence identifies the number of times a gene is differentially expressed across a series of datasets. This enables the remove of nuisance gene expression signatures while amplifying the most robust gene signal that best describes a hosts response to infection. Co-expression enables the determination of gene-gene relationships and expands on identifying genes with shared functionality in the form of co-expression networks. A human network was purposely built for this project to examine these relationships between human genes. By identifying genes that are most reliably differentially expressed and also have strong co-expression with each other, it is possible to identify conserved functional networks that form the foundation of a host's response to viral infection. This is a very applicable approach and can be used to assess how different species respond when subject to any particular distress (e.g. infectious disease etc). An integrative analysis using ChIP-seq and single-cell RNA-seq data were also performed to assess the characterizations from meta-analysis and co-expression in more detail with respect to a particular infectious disease, influenza.