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.

易感宿主感染病毒后，往往会触发宿主应答机制，以遏制入侵病原体所引发的损伤。本研究采用了荟萃分析（meta-analysis）与共表达分析相结合的双路径研究策略，以解析不同宿主对病毒感染的应答模式。本数据集包含了本研究各分析阶段的预处理数据文件。
通过对多组重复数据集整合的荟萃分析，可统计某一基因在系列数据集间的差异表达频次。该方法可去除混杂的基因表达特征，同时强化最能反映宿主感染应答的稳健基因信号。
共表达分析可明确基因间的调控关联，并通过构建共表达网络，进一步挖掘具有协同功能的基因集合。本研究专门构建了人类基因共表达网络，以解析人类基因间的上述关联。通过筛选出表达可靠性最高的差异表达基因，且这些基因间存在强共表达关联，即可识别出构成宿主病毒感染应答核心的保守性功能网络。
该研究策略具有良好的普适性，可用于评估不同物种在遭遇各类应激事件（如感染性疾病等）时的应答模式。本研究还整合了染色质免疫共沉淀测序（ChIP-seq）与单细胞RNA测序（single-cell RNA-seq）数据，针对特定感染性疾病——流感，对荟萃分析和共表达分析的结果进行了更细致的验证与表征解析。