View source: R/blockwiseModulesC.R
blockwiseIndividualTOMs  R Documentation 
Calculates topological overlaps in the given (expression) data. If the number of variables (columns) in the input data is too large, the data is first split using preclustering, then topological overlaps are calculated in each block.
blockwiseIndividualTOMs(
multiExpr,
multiWeights = NULL,
# 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,
# Adjacency function options
power = 6,
networkType = "unsigned",
checkPower = TRUE,
replaceMissingAdjacencies = FALSE,
# Topological overlap options
TOMType = "unsigned",
TOMDenom = "min",
suppressTOMForZeroAdjacencies = FALSE,
suppressNegativeTOM = FALSE,
# Save individual TOMs? If not, they will be returned in the session.
saveTOMs = TRUE,
individualTOMFileNames = "individualTOMSet%sBlock%b.RData",
# General options
nThreads = 0,
useInternalMatrixAlgebra = FALSE,
verbose = 2, indent = 0)
multiExpr 
expression data in the multiset format (see 
multiWeights 
optional observation weights in the same format (and dimensions) as 
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 
maxBlockSize 
integer giving maximum block size for module detection. Ignored if 
blockSizePenaltyPower 
number specifying how strongly blocks should be penalized for exceeding the
maximum size. Set to a lrge number or 
nPreclusteringCenters 
number of centers for preclustering. Larger numbers typically results in better
but slower preclustering. The default is 
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 
corType 
character string specifying the correlation to be used. Allowed values are (unique
abbreviations of) 
maxPOutliers 
only used for 
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)

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 
softthresholding power for network construction. Either a single number or a vector of the same length as the number of sets, with one power for each set. 
networkType 
network type. Allowed values are (unique abbreviations of) 
checkPower 
logical: should basic sanity check be performed on the supplied 
replaceMissingAdjacencies 
logical: should missing values in calculated adjacency be replaced by 0? 
TOMType 
one of 
TOMDenom 
a character string specifying the TOM variant to be used. Recognized values are

suppressTOMForZeroAdjacencies 
Logical: should TOM be set to zero for zero adjacencies? 
suppressNegativeTOM 
Logical: should the result be set to zero when negative? Negative TOM values can occur when

saveTOMs 
logical: should calculated TOMs be saved to disk ( 
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: 
nThreads 
nonnegative 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. 
useInternalMatrixAlgebra 
Logical: should WGCNA's own, slow, matrix multiplication be used instead of Rwide BLAS? Only useful for debugging. 
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. 
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 excluded from the TOM calculations.
If blocks
is not given and
the number of genes exceeds maxBlockSize
, genes are preclustered 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. The topological overlaps can be saved to disk as RData files, or returned directly within the return value (see below). Note that the matrices can be big and returning them within the return value can quickly exhaust the system's memory. In particular, if the blockwise calculation is necessary, it is nearly certain that returning all matrices via the return value will be impossible.
A list with the following components:
actualTOMFileNames 
Only returned if input 
TOMSimilarities 
Only returned if input 
blocks 
if input 
blockGenes 
a list with one component for each block of genes. Each component is a vector giving
the indices (relative to the input 
goodSamplesAndGenes 
if input

The following components are present mostly to streamline the interaction of this function with
blockwiseConsensusModules
.
nGGenes 
Number of genes that passed missing data filters (if input

gBlocks 
the vector 
nThreads 
number of threads used to calculate correlation and TOM matrices. 
saveTOMs 
logical: were calculated matrices saved in files ( 
intNetworkType , intCorType 
integer codes for network and correlation type. 
nSets 
number of sets in input data. 
setNames 
the 
Peter Langfelder
For a general discussion of the weighted network formalism, see
Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted Gene CoExpression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17
The blockwise approach is briefly described in the article describing this package,
Langfelder P, Horvath S (2008) "WGCNA: an R package for weighted correlation network analysis". BMC Bioinformatics 2008, 9:559
blockwiseConsensusModules
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