MEGENA.ModuleSummary: MEGENA module summary
In MEGENA: Multiscale Clustering of Geometrical Network

Description Usage Arguments Details Value Author(s) Examples

Summarizes modules into a table.

MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 2500,
annot.table = NULL,symbol.col = NULL,id.col = NULL, 
output.sig = TRUE)

`MEGENA.output`	A list object. The output from "do.MEGENA()".
`mod.pvalue`	module compactness significance p-value, to identify modules with significant compactness.
`hub.pvalue`	node degree significance p-value to identify nodes with significantly high degree.
`min.size`	minimum module size allowed to finalize in the summary output.
`max.size`	maximum module size allowed to finalize in the summary output.
`annot.table`	Default value is NULL, indicating no mapping is provided between node names to gene symbols. If provided, the mapping between node names (id.col) and gene symbol (symbol.col) are used.
`id.col`	column index of annot.table for node names.
`symbol.col`	column index of annot.table for gene symbols.
`output.sig`	Default value is TRUE, indicating significant modules are outputted.

output$module.table contains many important information including module hierarchy, as indicated by

A list object with the components:

`modules`	Final set of modules obtained upon apply mod.pvalue for significance, min.size and max.size for module size thresholding.
`mapped.modules`	gene symbol mapped modules when "annot.table" is provided.
`module.table`	data.frame object for module summary table. Columns include: id, module.size, module.parent, module.hub, module.scale and module.pvalue.

Won-Min Song

## 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)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)

## End(Not run)

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:4
- k=2,3,4,5,6,
- #. of split:0
- k=2,3,4,5,6,7,
- #. of split:0
- k=2,3,4,5,6,7,8,9,10,11,12,13,14,
- #. of split:1
- 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