## ----style, echo = FALSE, results = 'asis'------------------------------- BiocStyle::markdown() ## ---- eval=FALSE, message=FALSE------------------------------------------ # library(graphite) # # # pathwayDatabases() #to have a look at pathways and species available # # get the pathway list: # paths <- graphite::pathways("hsapiens", "kegg") # # # convert the first 3 pathways to graphs: # kegg_human <- lapply(paths[1:3], graphite::pathwayGraph) # head(kegg_human) ## ---- eval=TRUE---------------------------------------------------------- library(signet) data(daub13) head(scores) # gene scores ## ---- eval=TRUE, message=FALSE, results="hide"--------------------------- # Run simulated annealing on the first 3 KEGG pathways: HSS <- searchSubnet(kegg_human, scores) ## ---- echo=FALSE, eval=TRUE, message=FALSE, results="hide"--------------- null <- rnorm(1000, mean = 1.5) ## ---- eval=FALSE, message=FALSE------------------------------------------ # #Generate the empirical null distribution # null <- nullDist(kegg_human, scores, n = 1000) ## ---- eval=TRUE---------------------------------------------------------- HSS <- testSubnet(HSS, null) ## ---- eval=TRUE---------------------------------------------------------- # Results: generate a summary table tab <- summary(HSS) head(tab) # you can write the summary table as follow: # write.table(tab, # file = "signet_output.tsv", # sep = "\t", # quote = FALSE, # row.names = FALSE) ## ---- eval=TRUE, echo=FALSE---------------------------------------------- fname <- tempfile() writeXGMML(HSS[[1]], filename = fname) ## ---- eval=FALSE--------------------------------------------------------- # writeXGMML(HSS[[1]], filename = "cytoscape_input.xgmml") ## ---- eval=FALSE--------------------------------------------------------- # writeXGMML(HSS, filename = "cytoscape_input.xgmml", threshold = 0.01)