I am trying to do module preservation to two data sets-- healthy and flare.

My input has data expression and module list for each group. But I got an error when i used the module preservation function. Here's my code and the error:

datHealthy <-read.csv("R:/Medicine/Rheumatology/Jawaheer_Lab/Gail/Postpartum/New_project/output/WGCNA/output/T3_PP3-1162024/Healthy_DataExpression.txt", sep="\t", header=T)

datFlare <-read.csv("R:/Medicine/Rheumatology/Jawaheer_Lab/Gail/Postpartum/New_project/output/WGCNA/output/T3_PP3-1162024/Flare_DataExpression.txt", sep="\t", header=T)

# Expression data files for two treatments
# columns = genes, rows = samples
multiExpr = multiData(Set1 = datHealthy, Set2 = datFlare)

# Input module files
# one column for geneID, one for assigned module
Healthymodule=read.csv("R:/Medicine/Rheumatology/Jawaheer_Lab/Gail/Postpartum/New_project/output/WGCNA/output/T3_PP3-1162024/Healthy-modulelist.tsv",sep="\t", header=TRUE) 
Flaremodule=read.csv("R:/Medicine/Rheumatology/Jawaheer_Lab/Gail/Postpartum/New_project/output/WGCNA/output/T3_PP3-1162024/Flare/Flare-modulelist.tsv",sep="\t", header=TRUE) 

# Create list holding module labels for each set
colorList = list(Set1 = Healthymodule, Set2 = Flaremodule)

# Calculate module preservation statistics
mp = modulePreservation(multiExpr, colorList, referenceNetworks=1,
                   nPermutations = 1000,
                   networkType = "signed",
                   randomSeed = 2905,
                   quickCor=0,
                   verbose = 4, 
                   indent = 0)

..checking data for excessive amounts of missing data..
     Flagging genes and samples with too many missing values...
      ..step 1
     Flagging genes and samples with too many missing values...
      ..step 1
  ..unassigned 'module' name: grey 
  ..all network sample 'module' name: gold
Error in modulePreservation(multiExpr, colorList, referenceNetworks = 1,  : 
  Color vector for set 1 does not have the correct number of entries.