WeightedModulePartitionAmoutain: Modules detection by AMOUNTAIN 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 AMOUNTAIN algorithm, which tries to find the optimal module every time and use a modules extraction way

1 2	WeightedModulePartitionAmoutain(datExpr, Nmodule, foldername, indicatename, GeneNames, maxsize = 200, minsize = 3, power = 6, tao = 0.2)

`datExpr`	gene expression profile, rows are samples and columns genes
`Nmodule`	the number of clusters(modules)
`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)\|^pwr
`tao`	the threshold to cut the adjacency matrix

None

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
GeneNames <- colnames(datExpr1)
intModules1 <- WeightedModulePartitionAmoutain(datExpr1,5,ResultFolder,'X',
GeneNames,maxsize=100,minsize=50)
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)