View source: R/blockwiseModulesC.R
blockwiseModules  R Documentation 
This function performs automatic network construction and module detection on large expression datasets in a blockwise manner.
blockwiseModules(
# Input data
datExpr,
weights = NULL,
# Data checking options
checkMissingData = TRUE,
# Options for splitting data into blocks
blocks = NULL,
maxBlockSize = 5000,
blockSizePenaltyPower = 5,
nPreclusteringCenters = as.integer(min(ncol(datExpr)/20,
100*ncol(datExpr)/maxBlockSize)),
randomSeed = 54321,
# load TOM from previously saved file?
loadTOM = FALSE,
# Network construction arguments: correlation options
corType = "pearson",
maxPOutliers = 1,
quickCor = 0,
pearsonFallback = "individual",
cosineCorrelation = FALSE,
# Adjacency function options
power = 6,
networkType = "unsigned",
replaceMissingAdjacencies = FALSE,
# Topological overlap options
TOMType = "signed",
TOMDenom = "min",
suppressTOMForZeroAdjacencies = FALSE,
suppressNegativeTOM = FALSE,
# Saving or returning TOM
getTOMs = NULL,
saveTOMs = FALSE,
saveTOMFileBase = "blockwiseTOM",
# Basic tree cut options
deepSplit = 2,
detectCutHeight = 0.995,
minModuleSize = min(20, ncol(datExpr)/2 ),
# Advanced tree cut options
maxCoreScatter = NULL, minGap = NULL,
maxAbsCoreScatter = NULL, minAbsGap = NULL,
minSplitHeight = NULL, minAbsSplitHeight = NULL,
useBranchEigennodeDissim = FALSE,
minBranchEigennodeDissim = mergeCutHeight,
stabilityLabels = NULL,
stabilityCriterion = c("Individual fraction", "Common fraction"),
minStabilityDissim = NULL,
pamStage = TRUE, pamRespectsDendro = TRUE,
# Gene reassignment, module trimming, and module "significance" criteria
reassignThreshold = 1e6,
minCoreKME = 0.5,
minCoreKMESize = minModuleSize/3,
minKMEtoStay = 0.3,
# Module merging options
mergeCutHeight = 0.15,
impute = TRUE,
trapErrors = FALSE,
# Output options
numericLabels = FALSE,
# Options controlling behaviour
nThreads = 0,
useInternalMatrixAlgebra = FALSE,
useCorOptionsThroughout = TRUE,
verbose = 0, indent = 0,
...)
datExpr 
Expression data. A matrix (preferred) or
data frame in which columns are genes and rows ar samples. NAs are
allowed, but not too many. See 
weights 
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 column (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. 
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 
loadTOM 
logical: should Topological Overlap Matrices be loaded from previously saved files ( 
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. 
networkType 
network type. Allowed values are (unique abbreviations of) 
replaceMissingAdjacencies 
logical: should missing values in the calculation of 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

getTOMs 
deprecated, please use saveTOMs below. 
saveTOMs 
logical: should the consensus topological overlap matrices for each block be saved and returned? 
saveTOMFileBase 
character string containing the file name base for files containing the
consensus topological overlaps. The full file names have 
deepSplit 
integer value between 0 and 4. Provides a simplified control over how sensitive
module detection should be to module splitting, with 0 least and 4 most sensitive. See

detectCutHeight 
dendrogram cut height for module detection. See

minModuleSize 
minimum module size for module detection. See

maxCoreScatter 
maximum scatter of the core for a branch to be a cluster, given as the fraction
of 
minGap 
minimum cluster gap given as the fraction of the difference between 
maxAbsCoreScatter 
maximum scatter of the core for a branch to be a cluster given as absolute
heights. If given, overrides 
minAbsGap 
minimum cluster gap given as absolute height difference. If given, overrides

minSplitHeight 
Minimum split height given as the fraction of the difference between

minAbsSplitHeight 
Minimum split height given as an absolute height.
Branches merging below this height will automatically be merged. If not given (default), will be determined
from 
useBranchEigennodeDissim 
Logical: should branch eigennode (eigengene) dissimilarity be considered when merging branches in Dynamic Tree Cut? 
minBranchEigennodeDissim 
Minimum consensus branch eigennode (eigengene) dissimilarity for
branches to be considerd separate. The branch eigennode dissimilarity in individual sets
is simly 1correlation of the
eigennodes; the consensus is defined as quantile with probability 
stabilityLabels 
Optional matrix of cluster labels that are to be used for calculating branch
dissimilarity based on split stability. The number of rows must equal the number of genes in

stabilityCriterion 
One of 
minStabilityDissim 
Minimum stability dissimilarity criterion for two branches to be considered
separate. Should be a number between 0 (essentially no dissimilarity required) and 1 (perfect dissimilarity
or distinguishability based on 
pamStage 
logical. If TRUE, the second (PAMlike) stage of module detection will be performed.
See 
pamRespectsDendro 
Logical, only used when 
minCoreKME 
a number between 0 and 1. If a detected module does not have at least

minCoreKMESize 
see 
minKMEtoStay 
genes whose eigengene connectivity to their module eigengene is lower than

reassignThreshold 
pvalue ratio threshold for reassigning genes between modules. See Details. 
mergeCutHeight 
dendrogram cut height for module merging. 
impute 
logical: should imputation be used for module eigengene calculation? See

trapErrors 
logical: should errors in calculations be trapped? 
numericLabels 
logical: should the returned modules be labeled by colors ( 
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. 
useCorOptionsThroughout 
Logical: should correlation options passed to network analysis also be used
in calculation of kME? Set to 
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. 
... 
Other arguments. 
Before module detection starts, genes and samples are optionally checked for the presence of NA
s.
Genes and/or samples that have too many NA
s are flagged as bad and removed from the analysis; bad
genes will be automatically labeled as unassigned, while the returned eigengenes will have NA
entries for all bad samples.
If blocks
is not given and
the number of genes exceeds maxBlockSize
, genes are preclustered into blocks using the function
projectiveKMeans
; 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.
If requested, the topological overlaps are returned as part of the return value list.
Genes are then clustered using average linkage hierarchical clustering and modules are identified in the
resulting dendrogram by the Dynamic Hybrid tree cut. Found modules are trimmed of genes whose
correlation with module eigengene (KME) is less than minKMEtoStay
. Modules in which
fewer than minCoreKMESize
genes have KME higher than minCoreKME
are disbanded, i.e., their constituent genes are pronounced
unassigned.
After all blocks have been processed, the function checks whether there are genes whose KME in the module
they assigned is lower than KME to another module. If pvalues of the higher correlations are smaller
than those of the native module by the factor reassignThresholdPS
,
the gene is reassigned to the closer module.
In the last step, modules whose eigengenes are highly correlated are merged. This is achieved by
clustering module eigengenes using the dissimilarity given by one minus their correlation,
cutting the dendrogram at the height mergeCutHeight
and merging all modules on each branch. The
process is iterated until no modules are merged. See mergeCloseModules
for more details on
module merging.
The argument quick
specifies the precision of handling of missing data in the correlation
calculations. Zero will cause all
calculations to be executed precisely, which may be significantly slower than calculations without
missing data. Progressively higher values will speed up the
calculations but introduce progressively larger errors. Without missing data, all column means and
variances can be precalculated before the covariances are calculated. When missing data are present,
exact calculations require the column means and variances to be calculated for each covariance. The
approximate calculation uses the precalculated mean and variance and simply ignores missing data in the
covariance calculation. If the number of missing data is high, the precalculated means and variances may
be very different from the actual ones, thus potentially introducing large errors.
The quick
value times the
number of rows specifies the maximum difference in the
number of missing entries for mean and variance calculations on the one hand and covariance on the other
hand that will be tolerated before a recalculation is triggered. The hope is that if only a few missing
data are treated approximately, the error introduced will be small but the potential speedup can be
significant.
A list with the following components:
colors 
a vector of color or numeric module labels for all genes. 
unmergedColors 
a vector of color or numeric module labels for all genes before module merging. 
MEs 
a data frame containing module eigengenes of the found modules (given by 
goodSamples 
numeric vector giving indices of good samples, that is samples that do not have too many missing entries. 
goodGenes 
numeric vector giving indices of good genes, that is genes that do not have too many missing entries. 
dendrograms 
a list whose components conatain hierarchical clustering dendrograms of genes in each block. 
TOMFiles 
if 
blockGenes 
a list whose components give the indices of genes in each block. 
blocks 
if input 
blockOrder 
a vector giving the order in which blocks were processed and in which

MEsOK 
logical indicating whether the module eigengenes were calculated without errors. 
significantly affects the memory footprint (and whether the function will fail with a memory allocation error). From a theoretical point of view it is advantageous to use blocks as large as possible; on the other hand, using smaller blocks is substantially faster and often the only way to work with large numbers of genes. As a rough guide, it is unlikely a standard desktop computer with 4GB memory or less will be able to work with blocks larger than 8000 genes.
Peter Langfelder
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
goodSamplesGenes
for basic quality control and filtering;
adjacency
, TOMsimilarity
for network construction;
hclust
for hierarchical clustering;
cutreeDynamic
for adaptive branch cutting in hierarchical clustering
dendrograms;
mergeCloseModules
for merging of close modules.
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