WeightedModulePartitionHierarchical: Modules detection by hierarchical clustering
In MODA: MODA: MOdule Differential Analysis for weighted gene co-expression network

Description Usage Arguments Value Author(s) References See Also Examples

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.

1 2	WeightedModulePartitionHierarchical(datExpr, foldername, indicatename, cutmethod = c("Density", "Modularity"), power = 10)

`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

The number of clusters

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

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

PartitionDensity

PartitionModularity

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)