kda.prepare gets graph topology required by wKDA process,
then provides the information including hub list, hubnets, and overlapping
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
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.
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)
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.
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.