kda.analyze.simulate: Weighted key driver analysis (wKDA) simulation
In Mergeomics: Integrative network analysis of omics data

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

Generates simulations for permutation test, which is performed to obtain the p-value for the enrichment score of a given hub for a specified module during the wKDA process.

1	kda.analyze.simulate(o, g, nmemb, nnodes, nsim)

`o`	Observed enrichment score of a hub node assigned for a given module.
`g`	Sub-graph of a given hub and its neighbors (hubnet).
`nmemb`	Number of the members included in a given module.
`nnodes`	Number of the nodes in the whole graph (network) of the dataset.
`nsim`	Number of the iterations (simulations) performed for the permutation test.

kda.analyze.simulate performs permutation tests to obtain p-values for the enrichment score of a given hub node for a given module. It takes the observed enrichment score of the given hub, hubnet (subgraph of the hub and its neighbors), number of the members of the given module, total number of the nodes in the entire graph of the dataset, and number of the simulations for the permutation test. In each iteration (simulation), it samples nmemb nodes randomly among the entire nodes of the graph. Then, it tests the overlapped nodes among the randomly chosen nodes and the given node's neigborhood. At the end, it obtains an enrichment score for each simulation and evaluates these permuted enrichment scores with respect to the observed enrichment score of the hub. Among nsim random simulations; maximally, enrichment scores of 10 iterations are allowed to be greater than the observed (actual) enrichment score of the hub. If this limitation is exceeded, simulation will be finalized at that point and the enrichment score list of the iterations will be returned.

`x`	A list containing enrichment scores of the simulation's iterations

Ville-Petteri Makinen

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.

kda.analyze, kda.analyze.exec, kda.analyze.test

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"

## Let's prepare KDA object for KDA:
job.kda <- kda.configure(job.kda)
job.kda <- kda.start(job.kda)
job.kda <- kda.prepare(job.kda)
set.seed(job.kda$seed)
i = 1 ## index of the module, whose p-val is calculated:
memb <- job.kda$module2nodes[[i]]
graph <- job.kda$graph  ## we need to import a network
nsim <- job.kda$nperm   ## number of simulations
## This auxiliary function is called by kda.analyze.exec(), which is called
## by kda.analyze() main function, see this main function for more details

hubs <- graph$hubs
hubnets <- graph$hubnets
nhubs <- length(hubs)
nnodes <- length(graph$nodes)
nmemb <- length(memb)
    
## Observed enrichment scores.
# obs <- rep(NA, nhubs)
# k <- 1 ## actual using: for(k in 1:nhubs){}, for unit test, use the 1st hub
# g <- hubnets[[hubs[k]]]
# obs[k] <- kda.analyze.test(g$RANK, g$STRENG, memb, nnodes)

## Estimate P-values.
# pvals <- rep(NA, nhubs)
# for(k in which(obs > 0)) {
# g <- hubnets[[hubs[k]]]
## First pass:
# x <- kda.analyze.simulate(obs[k], g, nmemb, nnodes, 200)
## Then, use x to estimate preliminary and final P-values. 
## See kda.analyze() for more detail

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

Writing to file... 
Saved 1346 rows in 'subsetof.supersets.txt'.
[1] "subsetof.supersets.txt"

KDA Version:12.7.2015

Parameters:
  Search depth: 1
  Search direction: 1
  Maximum overlap: 0.33
  Minimum module size: 20
  Minimum degree: automatic
  Maximum degree: automatic
  Edge factor: 0
  Random seed: 1

Importing edges...
     TAIL               HEAD               WEIGHT 
 Length:140663      Length:140663      Min.   :1  
 Class :character   Class :character   1st Qu.:1  
 Mode  :character   Mode  :character   Median :1  
                                       Mean   :1  
                                       3rd Qu.:1  
                                       Max.   :1  

Importing modules...
    MODULE              NODE          
 Length:825         Length:825        
 Class :character   Class :character  
 Mode  :character   Mode  :character  
Graph: 7.251717 Mb

Minimum degree set to 20 

Maximum degree set to 278 

Collecting hubs...
4876 hubs (25.21%)
Graph: 12.46993 Mb
[1] TRUE