consensusTOM: Consensus network (topological overlap).

Description Usage Arguments Details Value Author(s) References See Also

View source: R/consensusTOM.R

Description

Calculation of a consensus network (topological overlap).

Usage

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consensusTOM(
      # Supply either ...
      # ... information needed to calculate individual TOMs
      multiExpr,

      # Data checking options
      checkMissingData = TRUE,

      # Blocking options
      blocks = NULL,
      maxBlockSize = 5000,
      blockSizePenaltyPower = 5,
      nPreclusteringCenters = NULL,
      randomSeed = 54321,

      # Network construction arguments: correlation options

      corType = "pearson",
      maxPOutliers = 1,
      quickCor = 0,
      pearsonFallback = "individual",
      cosineCorrelation = FALSE,
      replaceMissingAdjacencies = FALSE,

      # Adjacency function options

      power = 6,
      networkType = "unsigned",
      checkPower = TRUE,

      # Topological overlap options

      TOMType = "unsigned",
      TOMDenom = "min",
      suppressNegativeTOM = FALSE,

      # Save individual TOMs?

      saveIndividualTOMs = TRUE,
      individualTOMFileNames = "individualTOM-Set%s-Block%b.RData",

      # ... or individual TOM information

      individualTOMInfo = NULL,
      useIndivTOMSubset = NULL,

   ##### Consensus calculation options 

      useBlocks = NULL,

      networkCalibration = c("single quantile", "full quantile", "none"),

      # Save calibrated TOMs?
      saveCalibratedIndividualTOMs = FALSE,
      calibratedIndividualTOMFilePattern = "calibratedIndividualTOM-Set%s-Block%b.RData",

      # Simple quantile calibration options
      calibrationQuantile = 0.95,
      sampleForCalibration = TRUE, sampleForCalibrationFactor = 1000,
      getNetworkCalibrationSamples = FALSE,

      # Consensus definition
      consensusQuantile = 0,
      useMean = FALSE,
      setWeights = NULL,

      # Return options
      saveConsensusTOMs = TRUE,
      consensusTOMFilePattern = "consensusTOM-Block%b.RData",
      returnTOMs = FALSE,

      # Internal handling of TOMs
      useDiskCache = NULL, chunkSize = NULL,
      cacheDir = ".",
      cacheBase = ".blockConsModsCache",

      nThreads = 1,

      # Diagnostic messages
      verbose = 1,
      indent = 0)

Arguments

multiExpr

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.

checkMissingData

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

blocks

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.

maxBlockSize

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.

blockSizePenaltyPower

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.

nPreclusteringCenters

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.

randomSeed

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.

corType

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.

maxPOutliers

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.

quickCor

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

pearsonFallback

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.

cosineCorrelation

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.

power

soft-thresholding power for network construction.

networkType

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

checkPower

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.

replaceMissingAdjacencies

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

TOMType

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.

TOMDenom

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.

suppressNegativeTOM

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

saveIndividualTOMs

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

individualTOMFileNames

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.

individualTOMInfo

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.

useIndivTOMSubset

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.

useBlocks

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

networkCalibration

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

saveCalibratedIndividualTOMs

logical: should the calibrated individual TOMs be saved?

calibratedIndividualTOMFilePattern

pattern of file names for saving calibrated individual TOMs.

calibrationQuantile

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

sampleForCalibration

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.

sampleForCalibrationFactor

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.

getNetworkCalibrationSamples

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

consensusQuantile

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

useMean

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

setWeights

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

saveConsensusTOMs

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

consensusTOMFilePattern

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.

returnTOMs

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

useDiskCache

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.

chunkSize

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.

cacheDir

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.

cacheBase

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.

nThreads

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.

verbose

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

indent

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

Details

The function starts by optionally filtering out samples that have too many missing entries and genes that have either too many missing entries or zero variance in at least one set. Genes that are filtered out are left unassigned by the module detection. Returned eigengenes will contain NA in entries corresponding to filtered-out samples.

If blocks is not given and the number of genes exceeds maxBlockSize, genes are pre-clustered into blocks using the function consensusProjectiveKMeans; otherwise all genes are treated in a single block.

For each block of genes, the network is constructed and (if requested) topological overlap is calculated in each set. To minimize memory usage, calculated topological overlaps are optionally saved to disk in chunks until they are needed again for the calculation of the consensus network topological overlap.

Before calculation of the consensus Topological Overlap, individual TOMs are optionally calibrated. Calibration methods include single quantile scaling and full quantile normalization.

Single quantile scaling raises individual TOM in sets 2,3,... to a power such that the quantiles given by calibrationQuantile agree with the quantile in set 1. Since the high TOMs are usually the most important for module identification, the value of calibrationQuantile is close to (but not equal) 1. To speed up quantile calculation, the quantiles can be determined on a randomly-chosen component subset of the TOM matrices.

Full quantile normalization, implemented in normalize.quantiles, adjusts the TOM matrices such that all quantiles equal each other (and equal to the quantiles of the component-wise average of the individual TOM matrices).

Note that network calibration is performed separately in each block, i.e., the normalizing transformation may differ between blocks. This is necessary to avoid manipulating a full TOM in memory.

The consensus TOM is calculated as the component-wise consensusQuantile quantile of the individual (set) TOMs; that is, for each gene pair (TOM entry), the consensusQuantile quantile across all input sets. Alternatively, one can also use (weighted) component-wise mean across all imput data sets. If requested, the consensus topological overlaps are saved to disk for later use.

Value

List with the following components:

consensusTOM

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

TOMFiles

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.

saveConsensusTOMs

a copy of the inputsaveConsensusTOMs.

individualTOMInfo

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

Further components are retained for debugging and/or convenience.

useIndivTOMSubset

a copy of the input useIndivTOMSubset.

goodSamplesAndGenes

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.

nGGenes

number of "good" genes in goodSamplesGenes above.

nSets

number of input sets.

saveCalibratedIndividualTOMs

a copy of the input saveCalibratedIndividualTOMs.

calibratedIndividualTOMFileNames

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.

networkCalibrationSamples

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.

consensusQuantile

a copy of the input consensusQuantile.

originCount

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.

Author(s)

Peter Langfelder

References

WGCNA methodology has been described in

Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted Gene Co-Expression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17 PMID: 16646834

The original reference for the WGCNA package is

Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559 PMID: 19114008

For consensus modules, see

Langfelder P, Horvath S (2007) "Eigengene networks for studying the relationships between co-expression modules", BMC Systems Biology 2007, 1:54

This function uses quantile normalization described, for example, in

Bolstad BM1, Irizarry RA, Astrand M, Speed TP (2003) "A comparison of normalization methods for high density oligonucleotide array data based on variance and bias", Bioinformatics. 2003 Jan 22;19(2):1

See Also

blockwiseIndividualTOMs for calculation of topological overlaps across multiple sets.


WGCNA documentation built on March 1, 2021, 1:05 a.m.

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