Question: How to interpret WGCNA's modulePreservation output when comparing 2 networks?
0
gravatar for jol.espinoz
16 months ago by
jol.espinoz20
jol.espinoz20 wrote:

I'm trying to compare 2 networks that I've created as a toy dataset from WGCNA's tutorial section I'm not exactly sure how to interpret the resulting data since there is so much content. X_A and X_B are adjacency matrices while modules_A and modules_B are the module assignments (starting at 0 and ascending with the outlier module set as -1).  I want to look at the differences between modules and which modules are significant to the experimental/treatment phenotype network represented here as `Male`. 

Can somebody answer the following questions about this functions output?

Basing these questions off of the [documentation](https://rdrr.io/cran/WGCNA/man/modulePreservation.html)

(1) What is the meaning of: all network sample 'module' name: 0.1

(2) How is `quality` and `preservation` measured? 

(3) What `accuracy` is this referring to in the output?  

(4) What are `referenceSeparability` and `testSeparability`? 

(5) Does this function calculate significance for modules that are indicative of the test network? 

Here is my code below for running the function with my 2 precomputed adjacency matrices:

 

# Read DataFrames
read_dataframe = function(path,  sep="\t") {
  df = read.table(path, sep=sep, row.names=1, header = TRUE, check.names=FALSE)
  return(df)
}

# Data Overview
cat("Similarity", dim(X_A), "\nModule assignment", dim(A_modules), "\nModule Number Range", min(A_modules), max(A_modules))
#Similarity 375 375
#Module assignment 375 1
#Module Number Range 0 8

cat("Similarity", dim(X_B), "\nModule assignment", dim(B_modules), "\nModule Number Range", min(B_modules), max(B_modules))
#Similarity 375 375
#Module assignment 375 1
#Module Number Range 0 21

# Load data
library(WGCNA)
X_A = read_dataframe("./Google/Prototype/Data/FemaleLiver-Data/X_A.corr.abs.tsv")
X_A = data.matrix(X_A)
X_B = read_dataframe("./Google/Prototype/Data/FemaleLiver-Data/X_B.corr.abs.tsv")
X_B = data.matrix(X_B)

A_modules = read_dataframe("./Google/Prototype/Data/FemaleLiver-Data/X_A.corr.abs.modules.tsv")
B_modules = read_dataframe("./Google/Prototype/Data/FemaleLiver-Data/X_B.corr.abs.modules.tsv")

# Format data
multiExpr = list(
  "Female" = list(data=X_A),
  "Male" = list(data=X_B)
)

multiColor = list(
  "Female" = A_modules[rownames(X_A),],
  "Male" = B_modules[rownames(X_B),]
)

mp = modulePreservation(multiExpr, multiColor,
                          referenceNetworks = 1,
                          nPermutations = 10,
                          randomSeed = 0,
                          verbose = 3,
                          dataIsExpr=FALSE,
                          checkData = FALSE,
                         greyName=-1
                          )

..unassigned 'module' name: -1
  ..all network sample 'module' name: 0.1
  ..calculating observed preservation values
  ..calculating permutation Z scores
..Working with set 1 as reference set
....working with set 2 as test set
  ......working on permutation 1
  ......working on permutation 2
  ......working on permutation 3
  ......working on permutation 4
  ......working on permutation 5
  ......working on permutation 6
  ......working on permutation 7
  ......working on permutation 8
  ......working on permutation 9
  ......working on permutation 10
ADD COMMENTlink modified 16 months ago • written 16 months ago by jol.espinoz20
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