Selected article for: "differential expression and Meta analysis"

Author: Menachery, Vineet D.; Eisfeld, Amie J.; Schäfer, Alexandra; Josset, Laurence; Sims, Amy C.; Proll, Sean; Fan, Shufang; Li, Chengjun; Neumann, Gabriele; Tilton, Susan C.; Chang, Jean; Gralinski, Lisa E.; Long, Casey; Green, Richard; Williams, Christopher M.; Weiss, Jeffrey; Matzke, Melissa M.; Webb-Robertson, Bobbie-Jo; Schepmoes, Athena A.; Shukla, Anil K.; Metz, Thomas O.; Smith, Richard D.; Waters, Katrina M.; Katze, Michael G.; Kawaoka, Yoshihiro; Baric, Ralph S.
Title: Pathogenic Influenza Viruses and Coronaviruses Utilize Similar and Contrasting Approaches To Control Interferon-Stimulated Gene Responses
  • Document date: 2014_5_20
  • ID: s3zeppze_34
    Snippet: Meta-analysis of A549 cells infected with H1N1 1918 viruses. RNA expression data from studies with H1N1 1918 virus infections (33) were reanalyzed, and ISG expression was examined. Two-channel analysis was performed using R statistical programming language and bioconductor package LIMMA. To lower variance, two channel arrays were background corrected with a zero value and normalized with the Loess method. A contrast matrix file was used to direct.....
    Document: Meta-analysis of A549 cells infected with H1N1 1918 viruses. RNA expression data from studies with H1N1 1918 virus infections (33) were reanalyzed, and ISG expression was examined. Two-channel analysis was performed using R statistical programming language and bioconductor package LIMMA. To lower variance, two channel arrays were background corrected with a zero value and normalized with the Loess method. A contrast matrix file was used to direct which conditions and files should be used to perform differential expression analysis. LIMMA produced a gene list which was carried further for downstream functional analysis.

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