Only 1 gene/ feature is present in module after WGCNA analysis
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@df10c8f5
Last seen 8 weeks ago
United States

I am not sure if that is normal to get some (or one) modules that only contain 1 gene when running WGCNA. I believe I have set the minModuleSize = 20. If that is not uncommon, how should we explain the presence of a module with only 1 member? Is it possible that all other "members" were excluded during the module trimming? I am running a signed network using power = 8, deepSplit = 2, and pamState = FALSE, I ran the blockwiseModules() with the following options that I was copied from the user manual:

net = blockwiseModules(
  # Input data

  datExpr,
  weights = NULL,

  # Data checking options

  checkMissingData = TRUE,

  # Options for splitting data into blocks

  blocks = NULL,
  maxBlockSize = 50000,
  blockSizePenaltyPower = 5,
  # nPreclusteringCenters = as.integer(min(ncol(datExpr)/20,
  #                            100*ncol(datExpr)/maxBlockSize)),
  randomSeed = 54321,

 # load TOM from previously saved file?

  loadTOM = FALSE,

  # Network construction arguments: correlation options

  corType = "bicor",
  maxPOutliers = 1,
  quickCor = 0,
  pearsonFallback = "all",
  cosineCorrelation = FALSE,

  # Adjacency function options

  power = power_option,
  networkType = signness,
  replaceMissingAdjacencies = FALSE,

  # Topological overlap options

  TOMType = signness,
  TOMDenom = "min",
  suppressTOMForZeroAdjacencies = FALSE,
  suppressNegativeTOM = FALSE,

  # Saving or returning TOM

  getTOMs = NULL,
  saveTOMs = TRUE,
  saveTOMFileBase = paste0(data_source, "_", test_net, "_blockwiseTOM_D", tree_deep, "_", signness, "_P", power_option, "_pam", pam_option),

  # Basic tree cut options

  deepSplit = tree_deep,
  detectCutHeight = 0.995,
  minModuleSize = 20,

  # Advanced tree cut options

  maxCoreScatter = NULL, minGap = NULL,
  maxAbsCoreScatter = NULL, minAbsGap = NULL,
  minSplitHeight = NULL, minAbsSplitHeight = NULL,

  useBranchEigennodeDissim = FALSE,
  # minBranchEigennodeDissim = mergeCutHeight,

  stabilityLabels = NULL,
  stabilityCriterion = "Individual fraction",
  minStabilityDissim = NULL,

  pamStage = pam_option, pamRespectsDendro = TRUE,

  # Gene reassignment, module trimming, and module "significance" criteria

  reassignThreshold = 1e-6,
  minCoreKME = 0.5,
  # minCoreKMESize = minModuleSize/3,
  minKMEtoStay = 0.2,

  # Module merging options

  mergeCutHeight = 0.15,
  impute = TRUE,
  trapErrors = FALSE,

  # Output options

  numericLabels = TRUE,

  # Options controlling behaviour

  nThreads = 0,
  useInternalMatrixAlgebra = FALSE,
  useCorOptionsThroughout = TRUE,
  verbose = 2, indent = 1)
RNASeq Clustering cluster WGCNA • 95 views
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