WGCNA: hierarchicalMergeCloseModules – R documentation

Pricing

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

Documentation

WGCNA

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

Description

Merges hierarchical consensus modules that are too close as measured by the correlation of their eigengenes.

Usage

hierarchicalMergeCloseModules(
  # input data
  multiExpr, 
  multiExpr.imputed = NULL,
  labels,

  # Optional starting eigengenes
  MEs = NULL,

  unassdColor = if (is.numeric(labels)) 0 else "grey",
  # If missing data are present, impute them?
  impute = TRUE,


  # Options for eigengene network construction
  networkOptions,

  # Options for constructing the consensus
  consensusTree,
  calibrateMESimilarities = FALSE,

  # Merging options
  cutHeight = 0.2,
  iterate = TRUE,

  # Output options
  relabel = FALSE,
  colorSeq = NULL,
  getNewMEs = TRUE,
  getNewUnassdME = TRUE,

  # Options controlling behaviour of the function
  trapErrors = FALSE,
  verbose = 1, indent = 0)

Arguments

`multiExpr`	Expression data in the multi-set format (see `multiData`). 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.
`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 within each module (see `imputeByModule`.
`labels`	A vector (numeric, character or a factor) giving module labels for genes (variables) in `multiExpr`.
`MEs`	If module eigengenes have been calculated before, the user can save some computational time by inputting them. `MEs` should have the same format as `multiExpr`. If they are not given, they will be calculated.
`unassdColor`	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.
`impute`	Should missing values be imputed in eigengene calculation? If imputation is disabled, the presence of `NA` entries will cause the eigengene calculation to fail and eigengenes will be replaced by their hubgene approximation. See `moduleEigengenes` for more details.
`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 specifying the consensus calculation. See `newConsensusTree` for details.
`calibrateMESimilarities`	Logical: should module eigengene similarities be calibrated? This setting overrides the calibration options in `consensusTree`.
`cutHeight`	Maximum dissimilarity (i.e., 1-correlation) that qualifies modules for merging.
`iterate`	Controls whether the merging procedure should be repeated until there is no change. If FALSE, only one iteration will be executed.
`relabel`	Controls whether, after merging, color labels should be ordered by module size.
`colorSeq`	Color labels to be used for relabeling. Defaults to the standard color order used in this package if `colors` are not numeric, and to integers starting from 1 if `colors` is numeric.
`getNewMEs`	Controls whether module eigengenes of merged modules should be calculated and returned.
`getNewUnassdME`	When doing module eigengene manipulations, the function does not normally calculate the eigengene of the 'module' of unassigned ('grey') genes. Setting this option to `TRUE` will force the calculation of the unassigned eigengene in the returned newMEs, but not in the returned oldMEs.
`trapErrors`	Controls whether computational errors in calculating module eigengenes, their dissimilarity, and merging trees should be trapped. If `TRUE`, errors will be trapped and the function will return the input colors. If `FALSE`, errors will cause the function to stop.
`verbose`	Controls verbosity of printed progress messages. 0 means silent, up to (about) 5 the verbosity gradually increases.
`indent`	A single non-negative integer controlling indentation of printed messages. 0 means no indentation, each unit above that adds two spaces.

Details

This function merges input modules that are closely related. The similarities are quantified by correlations of module eigengenes; a “consensus” similarity is calculated using hierarchicalConsensusMEDissimilarity according to the recipe in consensusTree. Once the (dis-)similarities are calculated, average linkage hierarchical clustering of the module eigengenes is performed, the dendrogram is cut at the height cutHeight and modules on each branch are merged. The process is (optionally) repeated until no more modules are merged.

If, for a particular module, the module eigengene calculation fails, a hubgene approximation will be used.

The user should be aware that if a computational error occurs and trapErrors==TRUE, the returned list (see below) will not contain all of the components returned upon normal execution.

Value

If no errors occurred, a list with components

`labels`	Labels for the genes corresponding to merged modules. The function attempts to mimic the mode of the input `labels`: if the input `labels` is numeric, character and factor, respectively, so is the output. Note, however, that if the function performs relabeling, a standard sequence of labels will be used: integers starting at 1 if the input `labels` is numeric, and a sequence of color labels otherwise (see `colorSeq` above).
`dendro`	Hierarchical clustering dendrogram (average linkage) of the eigengenes of the most recently computed tree. If `iterate` was set TRUE, this will be the dendrogram of the merged modules, otherwise it will be the dendrogram of the original modules.
`oldDendro`	Hierarchical clustering dendrogram (average linkage) of the eigengenes of the original modules.
`cutHeight`	The input cutHeight.
`oldMEs`	Module eigengenes of the original modules in the sets given by `useSets`.
`newMEs`	Module eigengenes of the merged modules in the sets given by `useSets`.
`allOK`	A logical set to `TRUE`.

If an error occurred and trapErrors==TRUE, the list only contains these components:

`colors`	A copy of the input colors.
`allOK`	a logical set to `FALSE`.

Author(s)

Peter Langfelder