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Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers.

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Weighted Network Analysis: Applications in Genomics and Systems Biology

Bortz, E. Host-and strain-specific regulation of influenza virus polymerase activity by interacting cellular proteins. MBio 2 , e— Honig, A. Das, T.

Ebook Weighted Network Analysis Applications In Genomics And Systems Biology 2011

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How to analyze RNA-Seq data? Find differentially expressed genes in your research.

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Download references. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Zhou and E. All the authors read, edited and approved of the manuscript. Correspondence to Bin Zhang. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Reprints and Permissions. Molecular Genetics and Metabolism Advanced search. Skip to main content. Subjects Molecular biology Systems analysis. Abstract Influenza A virus, with the limited coding capacity of 10—14 proteins, requires the host cellular machinery for many aspects of its life cycle. Full size image.

Weighted correlation network analysis

Table 2 Abbreviations used in this manuscript Full size table. Table 3 The top ranked consensus modules based on the enrichment of various differentially expressed gene sets Full size table. Discussion In this study, we systematically analyzed a large amount of gene expression data from 12 molecular studies of influenza infection covering MOIs between 0. Methods A brief description of key methods and sample description is provided below, whereas complete details are discussed in the supplement.

Identification of differentially expressed genes We used three distinct methods to identify differentially expressed genes. Co-expression modules and consensus modules CMs We performed WGCNA 17 , 18 to identify modules of highly co-regulated genes from the assembled 12 data sets. Enrichment analysis and internal verification of data To functionally annotate gene signatures and gene modules identified in this study, we performed enrichment analysis of the established pathways and signatures including the gene ontology GO categories and MSigDB, and the subject area specific gene sets including influenza host factors, inflammasome, interferome, and InnateDB.

Data availability statement Analytic results are available in a large number of supplementary tables. References 1. Article Google Scholar Ethics declarations Competing interests The authors declare that they have no competing financial interests. Additional information Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Electronic supplementary material Supplementary Tables. Supplementary Methods and Figures. About this article. Download PDF.


Browse Articles. Nature Research menu. Search Article search Search. The components of Conformity and IntramodularFactorizability correspond to the entries of Cl. Its entries report the factorizability measure for each module. The components correspond to the entries of Cl. One can easily show that the conformity vector is not unique if adj contains only 2 nodes. However, for more than 2 nodes the conformity is uniquely defined when dealing with an exactly factorizable weighted network whose entries adj[i,j] are larger than 0. In this case, one can get explicit formulas for the conformity Dong and Horvath PloS Computational Biology.

Applications in Genomics and Systems Biology. Springer Book. Currently a draft. For more information on customizing the embed code, read Embedding Snippets. Man pages API Source code Usage 1. Cl another block diagonal matrix blockdiag. WGCNA index. R Package Documentation rdrr. Sorry, but we can't respond to individual comments.

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