do.MEGENA {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

Details

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

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.

Author(s)

Won-Min Song

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)

## End(Not run)