WeightedModulePartitionLouvain: Modules detection by Louvain algorithm
In MODA: MODA: MOdule Differential Analysis for weighted gene co-expression network

Description Usage Arguments Value Author(s) References Examples

Module detection based on the Louvain algorithm, which tries to maximize overall modularity of resulting partition.

1 2	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)