WeightedModulePartitionLouvain {MODA} | R Documentation |
Module detection based on the Louvain algorithm, which tries to maximize overall modularity of resulting partition.
WeightedModulePartitionLouvain(datExpr, foldername, indicatename, GeneNames, maxsize = 200, minsize = 30, power = 6, tao = 0.2)
datExpr |
gene expression profile, rows are samples and columns genes |
foldername |
where to store the clusters |
indicatename |
normally a specific tag of condition |
GeneNames |
normally the gene official names to replace the colnames of datExpr |
maxsize |
the maximal nodes allowed in one module |
minsize |
the minimal nodes allowed in one module |
power |
the power parameter of WGCNA, W_ij=|cor(x_i,x_j)|^power |
tao |
the threshold to cut the adjacency matrix |
The number of clusters
Dong Li, dxl466@cs.bham.ac.uk
Blondel, Vincent D., et al. "Fast unfolding of communities in large networks." Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.
data(synthetic) ResultFolder <- 'ForSynthetic' # where middle files are stored indicator <- 'X' # indicator for data profile 1 GeneNames <- colnames(datExpr1) intModules1 <- WeightedModulePartitionLouvain(datExpr1,ResultFolder,indicator,GeneNames) truemodule <- c(rep(1,100),rep(2,100),rep(3,100),rep(4,100),rep(5,100)) #mymodule <- getPartition(ResultFolder) #randIndex(table(mymodule,truemodule),adjust=F)