Tutorial for the WGCNA package for R II. Consensus network analysis of liver expression data, female and male mice 2.c Dealing with large data sets: block-wise network construction and consensus module detection
نویسندگان
چکیده
2 Network construction and module detection 2 2.a Automatic block-wise network construction and consensus module detection . . . . . . . . . . . . 2 2.a.1 Choosing the soft-thresholding power: analysis of network topology . . . . . . . . . . . . . . 2 2.a.2 Block-wise network construction and consensus module detection . . . . . . . . . . . . . . . 3 2.a.3 Comparing the block-wise and standard modules . . . . . . . . . . . . . . . . . . . . . . . . 6
منابع مشابه
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...
متن کاملTutorial 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
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 outpu...
متن کاملCorrected R code from chapter 12 of the book
Integrated weighted correlation network analysis of mouse liver gene expression data Chapter 12 and this R software tutorial describe a case study for carrying out an integrated weighted correlation network analysis of mouse gene expression, sample trait, and genetic marker data. It describes how to i) use sample networks (signed correlation networks) for detecting outlying observations, ii) fi...
متن کاملTutorial for the WGCNA package for R: III. Using simulated data to evaluate different module detection methods and gene screening approaches 7. Module membership, intramodular connectivity and screening for intramodular hub genes
7 Intramodular connectivity, module membership, and screening for intramodular hub genes 2 7.a Intramodular connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 7.b Relationship between gene significance and intramodular connectivity . . . . . . . . . . . . . . . . . . 2 7.c Generalizing intramodular connectivity for all genes on the array . . . ....
متن کاملA new virtual leader-following consensus protocol to internal and string stability analysis of longitudinal platoon of vehicles with generic network topology under communication and parasitic delays
In this paper, a new virtual leader following consensus protocol is introduced to perform the internal and string stability analysis of longitudinal platoon of vehicles under generic network topology. In all previous studies on multi-agent systems with generic network topology, the control parameters are strictly dependent on eigenvalues of network matrices (adjacency or Laplacian). Since some ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2016