expression data in the multi-set format (see checkSets). 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.

logical: should data be checked for excessive numbers of missing entries in genes and samples, and for genes with zero variance? See details.

optional specification of blocks in which hierarchical clustering and module detection should be performed. If given, must be a numeric vector with one entry per gene of multiExpr giving the number of the block to which the corresponding gene belongs.

integer giving maximum block size for module detection. Ignored if blocks above is non-NULL. Otherwise, if the number of genes in datExpr exceeds maxBlockSize, genes will be pre-clustered into blocks whose size should not exceed maxBlockSize.

number specifying how strongly blocks should be penalized for exceeding the maximum size. Set to a lrge number or Inf if not exceeding maximum block size is very important.

number of centers for pre-clustering. Larger numbers typically results in better but slower pre-clustering. The default is as.integer(min(nGenes/20, 100*nGenes/preferredSize)) and is an attempt to arrive at a reasonable number given the resources available.

integer to be used as seed for the random number generator before the function starts. If a current seed exists, it is saved and restored upon exit. If NULL is given, the function will not save and restore the seed.

character string specifying the correlation to be used. Allowed values are (unique abbreviations of) "pearson" and "bicor", corresponding to Pearson and bidweight midcorrelation, respectively. Missing values are handled using the pariwise.complete.obs option.

only used for corType=="bicor". Specifies the maximum percentile of data that can be considered outliers on either side of the median separately. For each side of the median, if higher percentile than maxPOutliers is considered an outlier by the weight function based on 9*mad(x), the width of the weight function is increased such that the percentile of outliers on that side of the median equals maxPOutliers. Using maxPOutliers=1 will effectively disable all weight function broadening; using maxPOutliers=0 will give results that are quite similar (but not equal to) Pearson correlation.

real number between 0 and 1 that controls the handling of missing data in the calculation of correlations. See details.

Specifies whether the bicor calculation, if used, should revert to Pearson when median absolute deviation (mad) is zero. Recongnized values are (abbreviations of) "none", "individual", "all". If set to "none", zero mad will result in NA for the corresponding correlation. If set to "individual", Pearson calculation will be used only for columns that have zero mad. If set to "all", the presence of a single zero mad will cause the whole variable to be treated in Pearson correlation manner (as if the corresponding robust option was set to FALSE). Has no effect for Pearson correlation. See bicor.

logical: should the cosine version of the correlation calculation be used? The cosine calculation differs from the standard one in that it does not subtract the mean.

soft-thresholding power for network construction.

network type. Allowed values are (unique abbreviations of) "unsigned", "signed", "signed hybrid". See adjacency.

logical: should basic sanity check be performed on the supplied power? If you would like to experiment with unusual powers, set the argument to FALSE and proceed with caution.

logical: should missing values in the calculation of adjacency be replaced by 0?

one of "none", "unsigned", "signed", "signed Nowick", "unsigned 2", "signed 2" and "signed Nowick 2". If "none", adjacency will be used for clustering. See TOMsimilarityFromExpr for details.

a character string specifying the TOM variant to be used. Recognized values are "min" giving the standard TOM described in Zhang and Horvath (2005), and "mean" in which the min function in the denominator is replaced by mean. The "mean" may produce better results but at this time should be considered experimental.

Logical: should the result be set to zero when negative? Negative TOM values can occur when TOMType is "signed Nowick".

logical: should individual TOMs be saved to disk for later use?

character string giving the file names to save individual TOMs into. The following tags should be used to make the file names unique for each set and block: %s will be replaced by the set number; %N will be replaced by the set name (taken from names(multiExpr)) if it exists, otherwise by set number; %b will be replaced by the block number. If the file names turn out to be non-unique, an error will be generated.

Optional data for TOM matrices in individual data sets. This object is returned by the function blockwiseIndividualTOMs. If not given, appropriate topological overlaps will be calculated using the network contruction options below.

If individualTOMInfo is given, this argument allows to only select a subset of the individual set networks contained in individualTOMInfo. It should be a numeric vector giving the indices of the individual sets to be used. Note that this argument is NOT applied to multiExpr.

optional specification of blocks that should be used for the calcualtions. The default is to use all blocks.

network calibration method. One of "single quantile", "full quantile", "none" (or a unique abbreviation of one of them).

logical: should the calibrated individual TOMs be saved?

pattern of file names for saving calibrated individual TOMs.

if networkCalibration is "single quantile", topological overlaps (or adjacencies if TOMs are not computed) will be scaled such that their calibrationQuantile quantiles will agree.

if TRUE, calibration quantiles will be determined from a sample of network similarities. Note that using all data can double the memory footprint of the function and the function may fail.

determines the number of samples for calibration: the number is 1/calibrationQuantile * sampleForCalibrationFactor. Should be set well above 1 to ensure accuracy of the sampled quantile.

logical: should the sampled values used for network calibration be returned?

quantile at which consensus is to be defined. See details.

logical: should the consensus be determined from a (possibly weighted) mean across the data sets rather than a quantile?

Optional vector (one component per input set) of weights to be used for weighted mean consensus. Only used when useMean above is TRUE.

logical: should the consensus topological overlap matrices for each block be saved and returned?

character string containing the file namefiles containing the consensus topological overlaps. The tag %b will be replaced by the block number. If the resulting file names are non-unique (for example, because the user gives a file name without a %b tag), an error will be generated. These files are standard R data files and can be loaded using the load function.

logical: should calculated consensus TOM(s) be returned?

should calculated network similarities in individual sets be temporarilly saved to disk? Saving to disk is somewhat slower than keeping all data in memory, but for large blocks and/or many sets the memory footprint may be too big. If not given (the default), the function will determine the need of caching based on the size of the data. See chunkSize below for additional information.

network similarities are saved in smaller chunks of size chunkSize. If NULL, an appropriate chunk size will be determined from an estimate of available memory. Note that if the chunk size is greater than the memory required for storing intemediate results, disk cache use will automatically be disabled.

character string containing the directory into which cache files should be written. The user should make sure that the filesystem has enough free space to hold the cache files which can get quite large.

character string containing the desired name for the cache files. The actual file names will consists of cacheBase and a suffix to make the file names unique.

non-negative integer specifying the number of parallel threads to be used by certain parts of correlation calculations. This option only has an effect on systems on which a POSIX thread library is available (which currently includes Linux and Mac OSX, but excludes Windows). If zero, the number of online processors will be used if it can be determined dynamically, otherwise correlation calculations will use 2 threads.

integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose.

indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.

only present if input returnTOMs is TRUE. A list containing consensus TOM for each block, stored as a distance structure.

only present if input saveConsensusTOMs is TRUE. A vector of file names, one for each block, in which the TOM for the corresponding block is stored. TOM is saved as a distance structure to save space.

a copy of the inputsaveConsensusTOMs.

information about individual set TOMs. A copy of the input individualTOMInfo if given; otherwise the result of calling blockwiseIndividualTOMs. See blockwiseIndividualTOMs for details.

a copy of the input useIndivTOMSubset.

a list containing information about which samples and genes are "good" in the sense that they do not contain more than a certain fraction of missing data and (for genes) have non-zero variance. See goodSamplesGenesMS for details.

number of "good" genes in goodSamplesGenes above.

number of input sets.

a copy of the input saveCalibratedIndividualTOMs.

if input saveCalibratedIndividualTOMs is TRUE, this component will contain the file names of calibrated individual networks. The file names are arranged in a character matrix with each row corresponding to one input set and each column to one block.

if input getNetworkCalibrationSamples is TRUE, a list with one component per block. Each component is in turn a list with two components: sampleIndex is a vector contain the indices of the TOM samples (the indices refer to a flattened distance structure), and TOMSamples is a matrix of TOM samples with each row corresponding to a sample in sampleIndex, and each column to one input set.

a copy of the input consensusQuantile.

A vector of length nSets that contains, for each set, the number of (calibrated) elements that were less than or equal the consensus for that element.