WGCNA: pruneConsensusModules – R documentation

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pruneConsensusModules

Prune (hierarchical) consensus modules by removing genes with low eigengene-based intramodular connectivity

Description

This function prunes (hierarchical) consensus modules by removing genes with low eigengene-based intramodular connectivity (KME) and by removing modules that do not have a certain minimum number of genes with a required minimum KME.

Usage

pruneConsensusModules(  multiExpr,
  multiWeights = NULL,
  multiExpr.imputed = NULL,
  MEs = NULL,
  labels,

  unassignedLabel = if (is.numeric(labels)) 0 else "grey",

  networkOptions,
  consensusTree,

  minModuleSize,
  minCoreKMESize = minModuleSize/3,
  minCoreKME = 0.5, 
  minKMEtoStay = 0.2,

  # Module eigengene calculation options
  impute = TRUE, 
  collectGarbage = FALSE,
  checkWeights = TRUE,

  verbose = 1, indent=0)

Arguments

`multiExpr`	Expression data in the multi-set format (see `checkSets`). A vector of lists, one per set. Each set must contain a component `data` that contains the expression data, with rows corresponding to samples and columns to genes or probes.
`multiWeights`	optional observation weights in the same format (and dimensions) as `multiExpr`. These weights are used for correlation calculations with data in `multiExpr`.
`multiExpr.imputed`	If `multiExpr` contain missing data, this argument can be used to supply the expression data with missing data imputed. If not given, the `impute.knn` function will be used to impute the missing data.
`MEs`	Optional consensus module eigengenes, in multi-set format analogous to that of `multiExpr`.
`labels`	A vector (numeric, character or a factor) giving module labels for each variable (gene) in multiExpr.
`unassignedLabel`	The label (value in `labels`) that represents unassigned genes. Module of this label will not enter the module eigengene clustering and will not be merged with other modules.
`networkOptions`	A single list of class `NetworkOptions` giving options for network calculation for all of the networks, or a `multiData` structure containing one such list for each input data set.
`consensusTree`	A list of class `ConsensusTree` specifying the consensus calculation.
`minModuleSize`	Minimum number of genes in a module. Modules that have fewer genes (after trimming) will be removed (i.e., their genes will be given the unassigned label).
`minCoreKME`	a number between 0 and 1. If a detected module does not have at least `minModuleKMESize` genes with consensus eigengene connectivity at least `minCoreKME`, the module is disbanded (its genes are unlabeled).
`minCoreKMESize`	see `minCoreKME` above.
`minKMEtoStay`	genes whose consensus eigengene connectivity to their module eigengene is lower than `minKMEtoStay` are removed from the module.
`impute`	logical: should imputation be used for module eigengene calculation? See `moduleEigengenes` for more details.
`collectGarbage`	Logical: should garbage be collected after some of the memory-intensive steps?
`checkWeights`	Logical: should `multiWeights` be checked to make sure their dimensions are concordant with `multiExpr` and the weights are valid?
`verbose`	integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose.
`indent`	indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.

Value

The pruned module labels: a vector of the same form as the input labels.

Author(s)

Peter Langfelder

WGCNA

Weighted Correlation Network Analysis

v1.70-3

GPL (>= 2)

Authors

Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath <SHorvath@mednet.ucla.edu> with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang

Initial release

2021-02-17