WeightedModulePartitionAmoutain: Modules detection by AMOUNTAIN algorithm

Description Usage Arguments Value Author(s) References Examples

Description

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

Usage

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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, dxl466@cs.bham.ac.uk

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

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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.