Package 'WGCNA' - Laboratory Web Sites - UCLA

20 blockwiseModules
blockwiseModules
Automatic network construction and module detection
Description
Usage
This function performs automatic network construction and module detection on large expression
datasets in a block-wise manner.
blockwiseModules(
datExpr,
blocks = NULL,
maxBlockSize = 5000,
randomSeed = 12345,
corType = "pearson",
power = 6,
networkType = "unsigned",
TOMType = "signed",
deepSplit = 2,
detectCutHeight = 0.995, minModuleSize = min(20, ncol(datExpr)/2 ),
maxCoreScatter = NULL, minGap = NULL,
maxAbsCoreScatter = NULL, minAbsGap = NULL,
pamStage = TRUE, pamRespectsDendro = TRUE,
minKMEtoJoin =0.7,
minCoreKME = 0.5, minCoreKMESize = minModuleSize/3,
minKMEtoStay = 0.3,
reassignThreshold = 1e-6,
mergeCutHeight = 0.15, impute = TRUE,
getTOMs = NULL,
saveTOMs = FALSE,
saveTOMFileBase = "blockwiseTOM",
trapErrors = FALSE, numericLabels = FALSE,
checkMissingData = TRUE,
quickCor = 0,
nThreads = 0,
verbose = 0, indent = 0)
Arguments
datExpr
blocks
expression data. A data frame in which columns are genes and rows ar samples.
NAs are allowed, but not too many.
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 exprData 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.