kda.prepare: Prepare graph topology for weighted key driver analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/cle.LS.R

Description

kda.prepare gets graph topology required by wKDA process, then provides the information including hub list, hubnets, and overlapping co-hubs.

Usage

1

Arguments

job

a parameter including restirictions while determining the graph topology information (such as hubs, hubnets, co-hubs, etc.), which is required by the wKDA process:

graph: graph of the dataset
depth: search depth for subgraph search
direction: use 0 for undirected, negative for downstream 
and positive for upstream
maxoverlap: maximum allowed overlap between two key driver
neighborhoods
mindegree: minimum hub degree to include
edgefactor: influence of node strengths; 0.0 no influence,
1.0 full influence

Details

kda.prepare determines minimum hub degree if it is not specified by the user, finds hubs and their neighborhoods (hubnets), extracts overlapping co-hubs, returns this information to user, and prints it to the screen.

Value

job

Updated data frame including information about the graph topology in terms of hubs, hubnets, and overlapping co-hubs:

hubs: hub nodes list
hubnets: neighborhoods of hubs (hubnets)
cohubsets: overlapping hubs (co-hubs)

Author(s)

Ville-Petteri Makinen

References

Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD, Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC genomics. 2016;17(1):874.

See Also

kda.analyze, kda.prepare.overlap, kda.prepare.screen

Examples

 1
 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
33
34
35
36
37
38
39
40
41
42
43
44
job.kda <- list()
job.kda$label<-"HDLC"
## parent folder for results
job.kda$folder<- "Results"
## Input a network
## columns: TAIL HEAD WEIGHT
job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt", 
package="Mergeomics")
## Gene sets derived from ModuleMerge, containing two columns, MODULE, 
## NODE, delimited by tab 
job.kda$modfile<- system.file("extdata","mergedModules.txt", 
package="Mergeomics")
## "0" means we do not consider edge weights while 1 is opposite.
job.kda$edgefactor<-0.0
## The searching depth for the KDA
job.kda$depth<-1
## 0 means we do not consider the directions of the regulatory interactions
## while 1 is opposite.
job.kda$direction <- 1
job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests

## kda.start() process takes long time while seeking hubs in the given net
## Here, we used a very small subset of the module list (1st 10 mods
## from the original module file):
moddata <- tool.read(job.kda$modfile)
mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
## save this to a temporary file and set its path as new job.kda$modfile:
tool.save(moddata, "subsetof.supersets.txt")
job.kda$modfile <- "subsetof.supersets.txt"

## Configure the parameters for KDA:
job.kda <- kda.configure(job.kda)
## Create the object properly
job.kda <- kda.start(job.kda)
## Find the hubs, co-hubs, and hub neighborhoods (hubnets), etc.:
job.kda <- kda.prepare(job.kda)
## After that, we need to call kda.analyze() and kda.finish()

## Remove the temporary files used for the test:
file.remove("subsetof.supersets.txt")
## remove the results folder
unlink("Results", recursive = TRUE)

Mergeomics documentation built on Nov. 8, 2020, 6:58 p.m.