Package 'moduleColor'

A vector of lists; in each list there must be a component named data whose content is a matrix or dataframe or array of dimension 2.

If FALSE, incorrect structure of data will trigger an error. If TRUE, an appropriate flag (see output) will be set to indicate whether data has correct structure.

Optional specification of entries of the vector data that are to be checked. Defaults to all components. This may be useful when data only contains information for some of the sets.

Number of sets (length of the vector data).

Number of columns in the data components in the lists. This number must be the same for all sets.

A vector of length nSets giving the number of rows in the data components.

Only set if the argument checkStructure equals TRUE. The value is TRUE if the paramter data passes a few tests of its structure, and FALSE otherwise. The tests are not exhaustive and are meant to catch obvious user errors rather than be bulletproof.

Module eigengenes of the same modules in several sets.

Controls whether absolute value of correlation should be used instead of correlation in the calculation of dissimilarity.

If the consensus is to include only a selection of the given sets, this vector (or scalar in the case of a single set) can be used to specify the selection. If NULL, all sets will be used.

A character string giving the method to use. Allowed values are (abbreviations of) "consensus" and "majority". The consensus dissimilarity is calculated as the minimum of given set dissimilarities for "consensus" and as the average for "majority".

Module eigengenes of several sets in a multi-set format (see checkSets). A vector of lists, with each list corresponding to one dataset and the module eigengenes in the component data, that is MEs[[set]]$data[sample, module] is the expression of the eigengene of module module in sample sample in dataset set. The number of samples can be different between the sets, but the modules must be the same.

Controls whether vector similarity should be given by absolute value of correlation or plain correlation.

Allows the user to specify for which sets the eigengene ordering is to be performed.

Normally the color grey is reserved for unassigned genes; hence the grey module is not a proper module and it is conventional to put it last. If this is not desired, set the parameter to FALSE.

Name of the grey module eigengene.

A character string giving the method to be used calculating the consensus dissimilarity. Allowed values are (abbreviations of) "consensus" and "majority". The consensus dissimilarity is calculated as the maximum of given set dissimilarities for "consensus" and as the average for "majority".

A dataframe, matrix or array with two dimensions to be encapsulated.

Controls verbosity. 0 is silent.

Controls indentation of printed progress messages. 0 means no indentation, every unit adds two spaces.

Vector of non-negative integer labels.

If TRUE, labels 0 will be assigned color grey. Otherwise, labels below 1 will trigger an error.

Color sequence corresponding to labels. If not given, a standard sequence will be used.

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.

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

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.

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.

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

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.

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.

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.

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.

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.

A copy of the input colors.

a boolean set to FALSE.

A character string of length 2. Recommended values are "PC" (the default start-up value) and "ME".

Expression data for a single set in the form of a data frame where rows are samples and columns are genes (probes).

A vector of the same length as the number of probes in expr, giving module color for all probes (genes). Color "grey" is reserved for unassigned genes.

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 datum. The function impute.knn uses a fixed random seed giving repeatable results.

Number of principal components and variance explained entries to be calculated. Note that only the first principal component is returned; the rest are used only for the calculation of proportion of variance explained. The number of returned variance explained entries is currently min(nPC, 10). If given nPC is greater than 10, a warning is issued.

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.

Should the improper module consisting of 'grey' genes be excluded from the eigengenes?

Value of colors designating the improper module. Note that if colors is a factor of numbers, the default value will be incorrect.

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 genes in the corresponding module. If this calculation fails, or if subHubs==FALSE, the value of trapErrors will determine whether the offending module will be removed or whether the function will issue an error and stop.

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 the function to stop. Note, however, that subHubs takes precedence in the sense that if subHubs==TRUE and trapErrors==FALSE, an error will be issued only if both the principal component and the hubgene calculations have failed.

Boolean. Controls whether the returned data frame of module eigengenes contains columns corresponding only to modules whose eigengenes or hub genes could be calculated correctly (TRUE), or whether the data frame should have columns for each of the input color labels (FALSE).

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 indication that it leads to incorrect results.

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.

Module eigengenes in a dataframe, with each column corresponding to one eigengene. The columns are named by the corresponding color with an "ME" prepended, e.g., MEturquoise etc. If returnValidOnly==FALSE, module eigengenes whose calculation failed have all components set to NA.

If 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.

A 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. The calculation is exact irrespective of the number of computed principal components. At most 10 variance explained values are recorded in this dataframe.

A copy of the input nPC.

A 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.

A copy of the input colors with entries corresponding to invalid modules set to grey if given, otherwise 0 if colors is numeric and "grey" otherwise.

Boolean flag signalling whether all eigengenes have been calculated correctly, either as principal components or as the hubgene average approximation.

Boolean flag signalling whether all returned eigengenes are principal components.

Boolean 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.

Boolean 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.

Boolean vector. Each component (corresponding to the columns in eigengenes) is TRUE if the corresponding module average expression is valid.

Boolean 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.

a hierarchical clustering dendorgram such as one returned by hclust.

Maximum joining heights that will be considered.

Minimum cluster size.

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[[set]]$data[sample, probe] is the expression of probe probe in sample sample in dataset set. The number of samples can be different between the sets, but the probes must be the same.

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.

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.

If calculations are requested in (a) selected set(s) only, the set(s) can be specified here. Defaults to all sets.

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.

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 datum. The function impute.knn uses a fixed random seed giving repeatable results.

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 slower.

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.

Should the improper module consisting of 'grey' genes be excluded from the eigengenes?

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.

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 genes in the corresponding module. If this calculation fails, or if subHubs==FALSE, the value of trapErrors will determine whether the offending module will be removed or whether the function will issue an error and stop.

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 the function to stop. Note, however, that subHubs takes precedence in the sense that if subHubs==TRUE and trapErrors==FALSE, an error will be issued only if both the principal component and the hubgene calculations have failed.

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 columns for each of the input color labels (FALSE).

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 indication that it leads to incorrect results.

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.

Module 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.)

If 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.

A 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.

A copy of the input nPC.

A 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.

A 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.

Boolean 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.

Boolean flag signalling whether all returned eigengenes are principal components. This flag (as well as the subsequent ones) is set independently for each set.

Boolean 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.

Boolean 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.

Boolean vector. Each component (corresponding to the columns in eigengenes) is TRUE if the corresponding module average expression is valid.

Boolean 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.

Numerical labels.

If TRUE (the default), labels 0 are preserved.

Module eigengenes in a multi-set format (see checkSets). A vector of lists, with each list corresponding to one dataset and the module eigengenes in the component data, that is MEs[[set]]$data[sample, module] is the expression of the eigengene of module module in sample sample in dataset set. The number of samples can be different between the sets, but the modules must be the same.

Normally the color grey is reserved for unassigned genes; hence the grey module is not a proper module and it is conventional to put it last. If this is not desired, set the parameter to FALSE.

Name of the grey module eigengene.

Specifies the set by which the eigengenes are to be ordered (in all other sets as well). Defaults to the first set in useSets (or the first set, if useSets is not given).

Allows the user to specify a custom ordering.

Allows the user to specify for which sets the eigengene ordering is to be performed.

Controls verbostity of printed progress messages. 0 means silent, nonzero verbose.

A single non-negative integer controling indentation of printed messages. 0 means no indentation, each unit above zero adds two spaces.

A dendrogram such as returned by hclust.

Coloring of objects on the dendrogram. Either a vector (one color per object) or a matrix (can also be an array or a data frame) with each column giving one color per object. Each column will be plotted as a horizontal row of colors under the dendrogram.

Labels for the colorings given in colors. The labels will be printed to the left of the color rows in the plot. If the argument is given, it must be a vector of length equal to the number of columns in colors. If not given, names(colors) will be used if available. If not, sequential numbers starting from 1 will be used.

Font size scale factor for the row labels. See par.

Other parameters to be passed on to the plotting method (such as main for the main title etc).

Module eigengenes, either in a single data frame (typicaly for a single set), or in a multi-set format. See checkSets for a description of the multi-set format.

Name of the module eigengene (in each corresponding data frame) that corresponds to the grey color. This will typically be "PCgrey" or "MEgrey". If the module eigengenes were calculated using standard functions in this library, the default should work.

Number of colors requested. If NULL, all (approx. 450) colors will be returned. Any other invalid argument such as less than one or more than maximum (length(standardColors())) will trigger an error.