Package 'WGCNA' - Laboratory Web Sites - UCLA

30 consensusProjectiveKMeans
Author(s)
Peter Langfelder, 〈Peter.Langfelder@gmail.com〉
See Also
moduleEigengenes, multiSetMEs, orderMEs
consensusProjectiveKMeans
Consensus projective K-means (pre-)clustering of expression data
Description
Usage
Implementation of a consensus variant of K-means clustering for expression data across multiple
data sets.
consensusProjectiveKMeans(
multiExpr,
preferredSize = 5000,
nCenters = NULL,
sizePenaltyPower = 4,
networkType = "unsigned",
randomSeed = 54321,
checkData = TRUE,
useMean = (length(multiExpr) > 3),
maxIterations = 1000,
verbose = 0, 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.
preferredSize
preferred maximum size of clusters.
nCenters number of initial clusters. Empirical evidence suggests that more centers will
give a better preclustering; the default is as.integer(min(nGenes/20,
preferredSize^2/nGenes)) and is an attempt to arrive at a reasonable
number given the resources available.
sizePenaltyPower
parameter specifying how severe is the penalty for clusters that exceed preferredSize.
networkType network type. Allowed values are (unique abbreviations of) "unsigned",
"signed", "signed hybrid". See adjacency.
randomSeed
checkData
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.
logical: should data be checked for genes with zero variance and genes and
samples with excessive numbers of missing samples Bad samples are ignored;
returned cluster assignment for bad genes will be NA.