WeightedModulePartitionAmoutain: Modules detection by AMOUNTAIN algorithm

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

Description

Module detection based on the AMOUNTAIN algorithm, which tries to find the optimal module every time and use a modules extraction way

Usage

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

Arguments

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

Value

None

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
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)

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