This function finds modules from network adjacency matrix using MEGENA.
Usage
findModules.megena(
data,
method = "pearson",
FDR.cutoff = 0.05,
module.pval = 0.05,
hub.pval = 0.05,
doPar = TRUE,
n.cores = NULL,
cor.perm = 10,
hub.perm = 100,
min_module = 30
)Arguments
- data
Required. An n x n upper triangular adjacency in the matrix class format.
- method
Optional. Method for correlation. either pearson or spearman. (Default = "pearson")
- FDR.cutoff
Optional. FDR threshold to define significant correlations upon shuffling samples. (Default = 0.05)
- module.pval
Optional. Module significance p-value. Recommended is 0.05. (Default = 0.05)
- hub.pval
Optional. Connectivity significance p-value based random tetrahedral networks. (Default = 0.05)
- doPar
Optional. If parallelization of clusters is allowed (Default =TRUE)
- n.cores
Optional. The number of cores/threads to call for PCP. If NULL, n.cores = detectCores() - 1. (Default = NULL)
- cor.perm
Optional. Number of permutations for calculating FDRs for all correlation pairs. (Default = 10)
- hub.perm
Optional. number of permutations for calculating connectivity significance p-value. (Default = 100)
- min_module
Optional. minimum number of nodes/genes allowed for filtering (Default = 30)