WeightedModulePartitionLouvain: Modules detection by Louvain algorithm

Description Usage Arguments Value Author(s) References Examples

View source: R/MODA.R

Description

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

Usage

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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, 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
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)

MODA documentation built on Nov. 8, 2020, 6:39 p.m.