WGCNA (version 1.25-1)

mergeCloseModules: Merge close modules in gene expression data

Description

Merges modules in gene expression networks that are too close as measured by the correlation of their eigengenes.

Usage

mergeCloseModules(exprData, colors, 
                  consensusQuantile = 0,
                  cutHeight = 0.2, 
                  MEs = NULL, 
                  impute = TRUE,
                  useAbs = FALSE, 
                  corFnc = cor, corOptions = list(use = 'p'),
                  iterate = TRUE, 
                  relabel = FALSE, 
                  colorSeq = NULL, 
                  getNewMEs = TRUE, 
                  getNewUnassdME = TRUE,
                  useSets = NULL,
                  checkDataFormat = TRUE,
                  unassdColor = ifelse(is.numeric(colors), 0, "grey"),
                  trapErrors = FALSE,
                  verbose = 1, indent = 0)

Arguments

exprData

Expression data, either a single data frame with rows corresponding to samples and columns to genes, or in a multi-set format (see checkSets). See checkDataStructure below.

colors

A vector (numeric, character or a factor) giving module colors for genes. The method only makes sense when genes have the same color label in all sets, hence a single vector.

consensusQuantile

A number giving the desired quantile to use in the consensus similarity calculation (see details).

cutHeight

Maximum dissimilarity (i.e., 1-correlation) that qualifies modules for merging.

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 exprData. If they are not given, they will be calculated.

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

useAbs

Specifies whether absolute value of correlation or plain correlation (of module eigengenes) should be used in calculating module dissimilarity.

corFnc

Correlation function to be used to calculate correlation of module eigengenes.

corOptions

Can be used to specify options to the correlation function, in addition to argument x which is used to pass the actual data to calculate the correlation of.

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

useSets

A vector of scalar allowing the user to specify which sets will be used to calculate the consensus dissimilarity of module eigengenes. Defaults to all given sets.

checkDataFormat

If TRUE, the function will check exprData and MEs for correct multi-set structure. If single set data is given, it will be converted into a format usable for the function. If FALSE, incorrect structure of input data will trigger

unassdColor

Specifies the string that labels unassigned genes. Module of this color will not enter the module eigengene clustering and will not be merged with other modules.

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 wil

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.

Value

• If no errors occurred, a list with components
• colors
  Color labels for the genes corresponding to merged modules. The function attempts to mimic the mode of the input colors: if the input colors is numeric, character and factor, respectively, so is the output. Note, however, that if the fnction performs relabeling, a standard sequence of labels will be used: integers starting at 1 if the input colors 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 boolean 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 boolean set to FALSE.

Details

This function returns the color labels for modules that are obtained from the input modules by merging ones that are closely related. The relationships are quantified by correlations of module eigengenes; a ``consensus'' measure is defined as the ``consensus quantile'' over the corresponding relationship in each set. Once the (dis-)similarity is 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.