kda.prepare.screen {Mergeomics} | R Documentation |
kda.prepare.screen
finds hubs and their neighborhoods
(hubnets) from the given graph.
kda.prepare.screen(graph, depth, direction, efactor, dmin, dmax)
graph |
entire graph, whose hubs and hubnets will be obtained |
depth |
search depth for subgraph search |
direction |
the direction of the interactions among graph components. 0 for undirected, negative for downstream, and positive for upstream |
efactor |
influence of node strengths (weights): 0.0 no influence, 1.0 full influence |
dmin |
minimum hub degree to include |
dmax |
maximum hub degree to include |
graph |
Updated graph including obtained hubs and hubnets: hubs: hub nodes list hubnets: neighborhoods of hubs (hubnets) |
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.
job.kda <- list() job.kda$label<-"HDLC" ## parent folder for results job.kda$folder<- "Results" ## Input a network ## columns: TAIL HEAD WEIGHT job.kda$netfile<-"network.mouseliver.mouse.txt" ## Gene sets derived from ModuleMerge, containing two columns, MODULE, ## NODE, delimited by tab job.kda$modfile<- "mergedModules.txt" ## 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 ## 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) by kda.prepare() ## and its auxiliary functions kda.prepare.screen and kda.prepare.overlap ## First, determine the minimum and maximum hub degrees: # nnodes <- length(job.kda$graph$nodes) # if (job.kda$mindegree == "automatic") { # dmin <- as.numeric(quantile(job.kda$graph$stats$DEGREE,0.75)) # job.kda$mindegree <- dmin # } # if (job.kda$maxdegree == "automatic") { # dmax <- as.numeric(quantile(job.kda$graph$stats$DEGREE,1)) # job.kda$maxdegree <- dmax # } ## Collect neighbors. # job.kda$graph <- kda.prepare.screen(job.kda$graph, job.kda$depth, # job.kda$direction, job.kda$edgefactor, job.kda$mindegree, job.kda$maxdegree) ## Then, extract overlapping co-hubs by kda.prepare.overlap()