MEGENA (version 1.3.7)

do.MEGENA: MEGENA clustering + MHA

Description

multiscale clustering analysis (MCA) and multiscale hub analysis (MHA) pipeline

Usage

do.MEGENA(g,
do.hubAnalysis = TRUE,
mod.pval = 0.05,hub.pval = 0.05,remove.unsig = TRUE,
min.size = 10,max.size = 2500,
doPar = FALSE,num.cores = 4,n.perm = 100,singleton.size = 3,
save.output = FALSE)

Arguments

g

igraph object of PFN.

do.hubAnalysis

TRUE/FALSE indicating to perform multiscale hub analysis (MHA) in downstream. Default is TRUE.

mod.pval

cluster significance p-value threshold w.r.t random planar networks

hub.pval

hub significance p-value threshold w.r.t random planar networks

remove.unsig

TRUE/FALSE indicating to remove insignificant clusters in MHA.

min.size

minimum cluster size

max.size

maximum cluster size

doPar

TRUE/FALSE indicating parallelization usage

num.cores

number of cores to use in parallelization.

n.perm

number of permutations to calculate hub significance p-values/cluster significance p-values.

singleton.size

Minimum module size to regard as non-singleton module. Default is 3.

save.output

TRUE/FALSE to save outputs from each step of analysis

Value

A series of output files are written in wkdir. Major outputs are,

module.output

outputs from MCA

hub.output

outputs from MHA

node.summary

node table summarizing clustering results.

Details

Performs MCA and MHA by taking PFN as input. Returns a list object containing clustering outputs, hub analysis outputs, and node summary table.

Examples

# NOT RUN {
rm(list = ls())
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
# }