WeightedModulePartitionLouvain: Modules detection by Louvain algorithm

In fairmiracle/MODA: MODA: MOdule Differential Analysis for weighted gene co-expression network

Description

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

Usage

WeightedModulePartitionLouvain(datExpr, foldername, indicatename, GeneNames,
  maxsize = 200, minsize = 30, power = 6, tao = 0.2)

Arguments

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

Value

The number of clusters

Author(s)

Dong Li, [e-mail]

References

Blondel, Vincent D., et al. "Fast unfolding of communities in large networks." Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.

Examples

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)

fairmiracle/MODA documentation built on May 16, 2019, 9:59 a.m.