## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----------------------------------------------------------------------------- library("sigora") ## ----------------------------------------------------------------------------- data(kegH) set.seed(seed = 12345) a1 <- genesFromRandomPathways( kegH, 3, 50) ## originally selected pathways: a1[["selectedPathways"]] ## what are the genes a1[["genes"]] ## Traditional ora identifies dozens of statistically significant pathways! head(ora(a1[["genes"]], kegH)) ## Now let us try sigora with the same input: sigoraRes <- sigora(GPSrepo = kegH, queryList = a1[["genes"]], level = 4) ## Again, the three originally selected pathways were: a1[["selectedPathways"]] ## ----------------------------------------------------------------------------- data(nciTable) ## what does the input look like? head(nciTable) ## create a SigObject. use the saveFile parameter for future reuse. data(idmap) nciH <- makeGPS(pathwayTable = nciTable, saveFile = NULL) ils <- grep("^IL", idmap[, "Symbol"], value = TRUE) ilnci <- sigora(queryList = ils, GPSrepo = nciH, level = 3) ## ----------------------------------------------------------------------------- sigRes <- sigora(kegH, queryList = a1$genes, level = 2, saveFile = NULL) ## ----------------------------------------------------------------------------- data('kegH') data('idmap') barplot( table(kegH$L1$degs), col = "red", main = "distribution of number of functions per gene in KEGG human pathways.", ylab = "frequency", xlab = "number of functions per gene" ) ## creating your own GPS repository nciH <- makeGPS(pathwayTable = load_data('nciTable')) ils <- grep("^IL", idmap[, "Symbol"], value = TRUE) ## signature overrepresentation analysis: sigRes.ilnci <- sigora(queryList = ils, GPSrepo = nciH, level = 3) ## ----------------------------------------------------------------------------- sessionInfo()