WeightedModulePartitionHierarchical {MODA}R Documentation

Modules detection by hierarchical clustering

Description

Module detection based on the optimal cutting height of dendrogram, which is selected to make the average density or modularity of resulting partition maximal. The clustering and visulization function are from WGCNA.

Usage

WeightedModulePartitionHierarchical(datExpr, foldername, indicatename,
  cutmethod = c("Density", "Modularity"), power = 10)

Arguments

datExpr

gene expression profile, rows are samples and columns genes

foldername

where to store the clusters

indicatename

normally a specific tag of condition

cutmethod

cutting the dendrogram based on maximal average Density or Modularity

power

the power parameter of WGCNA, W_ij=|cor(x_i,x_j)|^power

Value

The number of clusters

Author(s)

Dong Li, dxl466@cs.bham.ac.uk

References

Langfelder, Peter, and Steve Horvath. "WGCNA: an R package for weighted correlation network analysis." BMC bioinformatics 9.1 (2008): 1.

See Also

PartitionDensity

PartitionModularity

Examples

data(synthetic)
ResultFolder = 'ForSynthetic' # where middle files are stored
CuttingCriterion = 'Density' # could be Density or Modularity
indicator1 = 'X'     # indicator for data profile 1
indicator2 = 'Y'      # indicator for data profile 2
specificTheta = 0.1 #threshold to define condition specific modules
conservedTheta = 0.1#threshold to define conserved modules
intModules1 <- WeightedModulePartitionHierarchical(datExpr1,ResultFolder,
indicator1,CuttingCriterion) 
#mymodule <- getPartition(ResultFolder)
#randIndex(table(mymodule,truemodule),adjust=F)

[Package MODA version 1.18.0 Index]