do.MEGENA: MEGENA clustering + MHA

In MEGENA: Multiscale Clustering of Geometrical Network

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)

Example output

Loading required package: doParallel
Loading required package: foreach
Loading required package: iterators
Loading required package: parallel
Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

i = 1
i = 2
- outputting correlation results...
####### PFN Calculation commences ########
[1] "PFG is complete."
Commence multiscale clustering....
Calculating distance metric and similarity...
iteration:1
- #. tested:1
- k=2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,
- #. of split:4
- assess improvements over compactness
iteration:2
- #. tested:4
- k=2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,
- #. of split:1
- k=2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,
- #. of split:4
- assess improvements over compactness
- k=2,3,4,5,6,7,
- #. of split:0
- k=2,3,4,5,6,7,8,
- #. of split:0
iteration:3
- #. tested:3
- k=2,3,4,5,6,
- #. of split:0
- k=2,3,4,5,6,7,
- #. of split:0
- k=2,3,4,5,6,7,8,9,
- #. of split:0
Commence MHA...
Calculating hub significance.....
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
Identifying similar scales....
- Calculating within-module degree profiles.....
K.max:8
Cluster scales based on degree profiles...
k = 2,3,4,5,6,7,8,
- identified: 3
Identifying hub genes significant in each scale level...
Assigning module/KDA membership
Calculating node topological properties

MEGENA documentation built on May 1, 2019, 8:07 p.m.