WeightedModulePartitionSpectral {MODA} | R Documentation |
Module detection based on the spectral clustering algorithm, which mainly solve the eigendecomposition on Laplacian matrix
WeightedModulePartitionSpectral(datExpr, foldername, indicatename, GeneNames, power = 6, nn = 10, k = 2)
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 |
power |
the power parameter of WGCNA, W_ij=|cor(x_i,x_j)|^power |
nn |
the number of nearest neighbor, used to construct the affinity matrix |
k |
the number of clusters(modules) |
None
Dong Li, dxl466@cs.bham.ac.uk
Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4 (2007): 395-416.
data(synthetic) ResultFolder <- 'ForSynthetic' # where middle files are stored indicator <- 'X' # indicator for data profile 1 GeneNames <- colnames(datExpr1) WeightedModulePartitionSpectral(datExpr1,ResultFolder,indicator, GeneNames,k=5) 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)