WeightedModulePartitionSpectral: Modules detection by spectral clustering

In MODA: MODA: MOdule Differential Analysis for weighted gene co-expression network

Description

Module detection based on the spectral clustering algorithm, which mainly solve the eigendecomposition on Laplacian matrix

Usage

WeightedModulePartitionSpectral(datExpr, foldername, indicatename, GeneNames,
  power = 6, nn = 10, k = 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
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)

Value

None

Author(s)

Dong Li, [e-mail]

References

Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4 (2007): 395-416.

Examples

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)

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