Tutorial for the WGCNA package for R: I. Network analysis of liver expression data in female mice 3. Relating modules to external information and identifying important genes
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چکیده
3 Relating modules to external clinical traits 2 3.a Quantifying module–trait associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3.b Gene relationship to trait and important modules: Gene Significance and Module Membership . . . . 2 3.c Intramodular analysis: identifying genes with high GS and MM . . . . . . . . . . . . . . . . . . . . . . 3 3.d Summary output of network analysis results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
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Tutorial for the WGCNA package for R II. Consensus network analysis of liver expression data, female and male mice 4. Relating consensus modules to external microarray sample information and exporting network analysis results
# Display the current working directory getwd(); # If necessary, change the path below to the directory where the data files are stored. # "." means current directory. On Windows use a forward slash / instead of the usual \. workingDir = "."; setwd(workingDir); # Load the WGCNA package library(WGCNA) # The following setting is important, do not omit. options(stringsAsFactors = FALSE); # Load th...
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