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WGCNA (version 1.27-1)

multiSetMEs: Calculate module eigengenes.

Description

Calculates module eigengenes for several sets.

Usage

multiSetMEs(exprData, 
            colors, 
            universalColors = NULL, 
            useSets = NULL, 
            useGenes = NULL,
            impute = TRUE, 
            nPC = 1, 
            align = "along average", 
            excludeGrey = FALSE,
            grey = ifelse(is.null(universalColors), ifelse(is.numeric(colors), 0, "grey"),
                          ifelse(is.numeric(universalColors), 0, "grey")),
            subHubs = TRUE,
            trapErrors = FALSE, 
            returnValidOnly = trapErrors,
            softPower = 6,
            verbose = 1, indent = 0)

Arguments

exprData
Expression data in a multi-set format (see checkSets). A vector of lists, with each list corresponding to one microarray dataset and expression data in the component data, that is expr[
colors
A matrix of dimensions (number of probes, number of sets) giving the module assignment of each gene in each set. The color "grey" is interpreted as unassigned.
universalColors
Alternative specification of module assignment. A single vector of length (number of probes) giving the module assignment of each gene in all sets (that is the modules are common to all sets). If given, takes precedence over color.
useSets
If calculations are requested in (a) selected set(s) only, the set(s) can be specified here. Defaults to all sets.
useGenes
Can be used to restrict calculation to a subset of genes (the same subset in all sets). If given, validColors in the returned list will only contain colors for the genes specified in useGenes.
impute
Logical. If TRUE, expression data will be checked for the presence of NA entries and if the latter are present, numerical data will be imputed, using function impute.knn and probes from the same module as the missing
nPC
Number of principal components to be calculated. If only eigengenes are needed, it is best to set it to 1 (default). If variance explained is needed as well, use value NULL. This will cause all principal components to be computed, which is sl
align
Controls whether eigengenes, whose orientation is undetermined, should be aligned with average expression (align = "along average", the default) or left as they are (align = ""). Any other value will trigger an error.
excludeGrey
Should the improper module consisting of 'grey' genes be excluded from the eigengenes?
grey
Value of colors or universalColors (whichever applies) designating the improper module. Note that if the appropriate colors argument is a factor of numbers, the default value will be incorrect.
subHubs
Controls whether hub genes should be substituted for missing eigengenes. If TRUE, each missing eigengene (i.e., eigengene whose calculation failed and the error was trapped) will be replaced by a weighted average of the most connected hub gen
trapErrors
Controls handling of errors from that may arise when there are too many NA entries in expression data. If TRUE, errors from calling these functions will be trapped without abnormal exit. If FALSE, errors will cause t
returnValidOnly
Boolean. Controls whether the returned data frames of module eigengenes contain columns corresponding only to modules whose eigengenes or hub genes could be calculated correctly in every set (TRUE), or whether the data frame should have colu
softPower
The power used in soft-thresholding the adjacency matrix. Only used when the hubgene approximation is necessary because the principal component calculation failed. It must be non-negative. The default value should only be changed if there is a clear indic
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

  • A vector of lists similar in spirit to the input exprData. For each set there is a list with the following components:
  • dataModule eigengenes in a data frame, with each column corresponding to one eigengene. The columns are named by the corresponding color with an "ME" prepended, e.g., MEturquoise etc. Note that, when trapErrors == TRUE and returnValidOnly==FALSE, this data frame also contains entries corresponding to removed modules, if any. (validMEs below indicates which eigengenes are valid and allOK whether all module eigengens were successfully calculated.)
  • averageExprIf align == "along average", a dataframe containing average normalized expression in each module. The columns are named by the corresponding color with an "AE" prepended, e.g., AEturquoise etc.
  • varExplainedA dataframe in which each column corresponds to a module, with the component varExplained[PC, module] giving the variance of module module explained by the principal component no. PC. This is only accurate if all principal components have been computed (input nPC = NULL). At most 5 principal components are recorded in this dataframe.
  • nPCA copy of the input nPC.
  • validMEsA boolean vector. Each component (corresponding to the columns in data) is TRUE if the corresponding eigengene is valid, and FALSE if it is invalid. Valid eigengenes include both principal components and their hubgene approximations. When returnValidOnly==FALSE, by definition all returned eigengenes are valid and the entries of validMEs are all TRUE.
  • validColorsA copy of the input colors (universalColors if set, otherwise colors[, set]) with entries corresponding to invalid modules set to grey if given, otherwise 0 if the appropriate input colors are numeric and "grey" otherwise.
  • allOKBoolean flag signalling whether all eigengenes have been calculated correctly, either as principal components or as the hubgene approximation. If universalColors is set, this flag signals whether all eigengenes are valid in all sets.
  • allPCBoolean flag signalling whether all returned eigengenes are principal components. This flag (as well as the subsequent ones) is set independently for each set.
  • isPCBoolean vector. Each component (corresponding to the columns in eigengenes) is TRUE if the corresponding eigengene is the first principal component and FALSE if it is the hubgene approximation or is invalid.
  • isHubBoolean vector. Each component (corresponding to the columns in eigengenes) is TRUE if the corresponding eigengene is the hubgene approximation and FALSE if it is the first principal component or is invalid.
  • validAEsBoolean vector. Each component (corresponding to the columns in eigengenes) is TRUE if the corresponding module average expression is valid.
  • allAEOKBoolean flag signalling whether all returned module average expressions contain valid data. Note that returnValidOnly==TRUE does not imply allAEOK==TRUE: some invalid average expressions may be returned if their corresponding eigengenes have been calculated correctly.

Details

This function calls moduleEigengenes for each set in exprData.

Module eigengene is defined as the first principal component of the expression matrix of the corresponding module. The calculation may fail if the expression data has too many missing entries. Handling of such errors is controlled by the arguments subHubs and trapErrors. If subHubs==TRUE, errors in principal component calculation will be trapped and a substitute calculation of hubgenes will be attempted. If this fails as well, behaviour depends on trapErrors: if TRUE, the offending module will be ignored and the return value will allow the user to remove the module from further analysis; if FALSE, the function will stop. If universalColors is given, any offending module will be removed from all sets (see validMEs in return value below).

From the user's point of view, setting trapErrors=FALSE ensures that if the function returns normally, there will be a valid eigengene (principal component or hubgene) for each of the input colors. If the user sets trapErrors=TRUE, all calculational (but not input) errors will be trapped, but the user should check the output (see below) to make sure all modules have a valid returned eigengene.

While the principal component calculation can fail even on relatively sound data (it does not take all that many "well-placed" NA to torpedo the calculation), it takes many more irregularities in the data for the hubgene calculation to fail. In fact such a failure signals there likely is something seriously wrong with the data.

See Also

moduleEigengenes