WeightedModulePartitionHierarchical {MODA} | R Documentation |
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.
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.
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)