Merges modules in gene expression networks that are too close as measured by the correlation of their eigengenes.
mergeCloseModules(
# input data
exprData, colors, # Optional starting eigengenes
MEs = NULL,
# Optional restriction to a subset of all sets
useSets = NULL,
# If missing data are present, impute them?
impute = TRUE,
# Input handling options
checkDataFormat = TRUE,
unassdColor = if (is.numeric(colors)) 0 else "grey",
# Options for eigengene network construction
corFnc = cor, corOptions = list(use = 'p'),
useAbs = FALSE,
# Options for constructing the consensus
equalizeQuantiles = FALSE,
quantileSummary = "mean",
consensusQuantile = 0,
# 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)
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.
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.
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.
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.
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.
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 an error.
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.
Correlation function to be used to calculate correlation of module eigengenes.
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.
Specifies whether absolute value of correlation or plain correlation (of module eigengenes) should be used in calculating module dissimilarity.
Logical: should quantiles of the eigengene dissimilarity matrix be equalized
("quantile normalized")? The default is FALSE for reproducibility of old code; when there are many
eigengenes (e.g., at least 50), better results may be achieved if quantile equalization is used.
One of "mean" or "median". Controls how a reference dissimilarity
is computed from the input ones (using mean or median, respectively).
A number giving the desired quantile to use in the consensus similarity calculation (see details).
Maximum dissimilarity (i.e., 1-correlation) that qualifies modules for merging.
Controls whether the merging procedure should be repeated until there is no change. If FALSE, only one iteration will be executed.
Controls whether, after merging, color labels should be ordered by module size.
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.
Controls whether module eigengenes of merged modules should be calculated and returned.
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.
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.
Controls verbosity of printed progress messages. 0 means silent, up to (about) 5 the verbosity gradually increases.
A single non-negative integer controlling indentation of printed messages. 0 means no indentation, each unit above that adds two spaces.
If no errors occurred, a list with components
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).
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.
Hierarchical clustering dendrogram (average linkage) of the eigengenes of the original modules.
The input cutHeight.
Module eigengenes of the original modules in the sets given by useSets.
Module eigengenes of the merged modules in the sets given by useSets.
A boolean set to TRUE.
If an error occurred and trapErrors==TRUE, the list only contains these components:
A copy of the input colors.
a boolean set to FALSE.
This function merges input modules
that are closely related. The similarities are measured 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.