## ---- global_options, include=FALSE---------------------------------------- knitr::opts_chunk$set(message=FALSE) fig.cap1 <- "Toy example result for a double mutant (A,B) and one modulator (C)." fig.cap2 <- "Distribution of inferred logics for each double knock-out." fig.cap3 <- "Ranked modulators for one double knock-out." fig.cap4 <- "Perfect binary effects matrix for each logic." fig.cap5 <- "" fig.cap6 <- "Global results for the van Wageningen data set. See the help pages of plot.epiScreen() and HeatmapOP() for additional parameters." fig.cap7 <- "Results for one double knock-out of the van Wageningen data set. See ?plot.epiScreen for further parameters." fig.cap8 <- "Global result for the Sameith data set." fig.cap9 <- "Example for one knock-out of the Sameith data set." fig.cap10 <- "Density of the string-db interaction scores (van Wageningen). Background (turqoise) and inferred by epiNEM (pink)." fig.cap11 <- "Density of the string-dbinteraction scores (Sameith). Baackground (turqoise) and inferred by epiNEM (pink)." fig.cap12 <- "Density plot for graph-based GO similarity score (van Wageningen)." fig.cap13 <- "Density plot for graph-based GO similarity score (Sameith)." fig.cap14 <- "Enrichment of van Wageningen modulators by KEGG pathways. Colors refer to false discovery rates. NAs are colored in grey." fig.cap15 <- "Enrichment of Sameith modulators by KEGG pathways. Colors refer to false discovery rates. NAs are colored in grey." fig.cap16 <- "Effect reporter KEGG pathway enrichment (van Wageningen). Colors refer to false discovery rates. NAs are colored in grey." fig.cap17 <- "Effect reporter KEGG pathway enrichment (Sameith). Colors refer to false discovery rates. NAs are colored in grey." ## -------------------------------------------------------------------------- library(epiNEM) ## -------------------------------------------------------------------------- data <- matrix(sample(c(0,1), 100*4, replace = TRUE), 100, 4) colnames(data) <- c("A", "A.B", "B", "C") rownames(data) <- paste("E", 1:100, sep = "_") print(head(data)) res <- epiNEM(data, method = "exhaustive") ## ---- fig.width = 7, fig.height = 7, fig.cap=fig.cap1---------------------- plot(res) ## -------------------------------------------------------------------------- data <- matrix(sample(c(0,1), 100*9, replace = TRUE), 100, 9) colnames(data) <- c("A.B", "A.C", "B.C", "A", "B", "C", "D", "E", "G") rownames(data) <- paste("E", 1:100, sep = "_") res <- epiScreen(data) ## ---- fig.width = 4, fig.height = 4, fig.cap = fig.cap2-------------------- plot(res) ## ---- fig.width = 5, fig.height = 4, fig.cap = fig.cap3-------------------- plot(res, global = FALSE, ind = 1) ## ---- warning=FALSE, fig.width = 9, fig.height=5, fig.cap = fig.cap4------- epiAnno() ## -------------------------------------------------------------------------- data(sim) ## ---- fig.width = 7, fig.height = 7, fig.cap = "Simulation results."------- plot(sim) ## ---- fig.width = 8, fig.height = 4, fig.cap = fig.cap6-------------------- data(wagscreen) doubles <- wagscreen$doubles dataWag <- wagscreen$dataWag ## clean up the results: if (length(grep("fus3|ptp2.ptc2", wagscreen$doubles)) > 0) { wagscreen$doubles <- wagscreen$doubles[-grep("fus3|ptp2.ptc2", wagscreen$doubles)] wagscreen$dataWag <- wagscreen$dataWag[, -grep("fus3|ptp2.ptc2", colnames(wagscreen$dataWag))] wagscreen$ll <- wagscreen$ll[, -grep("fus3|ptp2.ptc2", colnames(wagscreen$ll))] wagscreen$logic <- wagscreen$logic[, -grep("fus3|ptp2.ptc2", colnames(wagscreen$logic))] } plot(wagscreen, xrot = 45, borderwidth = 0) ## ---- fig.width = 8, fig.height = 4, fig.cap = fig.cap7-------------------- plot(wagscreen, global = FALSE, ind = 3, cexGene = 0.7, cexLegend = 0.9, off = 0.2) ## ---- fig.width = 10, fig.height = 6, fig.cap = fig.cap8------------------- data(samscreen) doubles <- samscreen$doubles dataSam <- samscreen$dataSam plot(samscreen, xrot = 45, cexCol = 0.6, borderwidth = 0) ## ---- fig.width = 8, fig.height = 4, fig.cap = fig.cap9-------------------- plot(samscreen, global = FALSE, ind = 23, cexGene = 0.7, cexLegend = 0.9, off = 0.2) ## ---- fig.width = 8, fig.height = 5, fig.cap = fig.cap10------------------- library(STRINGdb) get_STRING_species(version="10", species_name=NULL)[26, ] # 4932 string_db <- STRINGdb$new( version="10", species=4932, score_threshold=0, input_directory="") data(wageningen_string) string.scores <- wageningen_string$string.scores string.names <- wageningen_string$string.names tmp <- string_db$get_interactions( string_db$mp(unique(unlist(strsplit(colnames(dataWag), "\\."))))) stsc <- unlist(string.scores) denspval <- wilcox.test(stsc, unlist(tmp$combined_score), alternative = "greater")$p.value for (i in 100:1) { if (denspval < 10^(-i)) { denspval <- paste("< ", 10^(-i), sep = "") } } plot(density(stsc), col = "#00000000", ylim = c(0, max(c(max(density(stsc)$y), max(density(unlist(tmp$combined_score))$y)))), main = paste("van Wageningen String-db interaction scores", sep = ""), xlab = "", cex.main = 1.5) polygon(density(stsc), col = "#ff000066") legend("topright", legend=paste("p-value", denspval, " "), cex = 1.5) mtext = mtext("A", side = 3, line = 1, outer = FALSE, cex = 3, adj = 0, at = par("usr")[1] - (par("usr")[2]-par("usr")[1])*0.1) lines(density(unlist(tmp$combined_score)), col = "#00000000") polygon(density(unlist(tmp$combined_score)), col = "#00ffff66") data(sameith_string) string.scores2 <- sameith_string$string.scores2 string.names2 <- sameith_string$string.names2 tmp <- string_db$get_interactions( string_db$mp(unique(unlist(strsplit(colnames(dataSam), "\\."))))) stsc <- unlist(string.scores2) denspval <- wilcox.test(stsc, unlist(tmp$combined_score), alternative = "greater")$p.value for (i in 100:1) { if (denspval < 10^(-i)) { denspval <- paste("< ", 10^(-i), sep = "") } } plot(density(stsc), col = "#00000000", ylim = c(0, max(c(max(density(stsc)$y), max(density(unlist(tmp$combined_score))$y)))), main = paste("Sameith String-db interaction scores", sp = ""), xlab = "", cex.main = 1.5) polygon(density(stsc), col = "#ff000066") legend("topright", legend=paste("p-value", denspval, " "), cex = 1.5) mtext = mtext("B", side = 3, line = 1, outer = FALSE, cex = 3, adj = 0, at = par("usr")[1] - (par("usr")[2]-par("usr")[1])*0.1) lines(density(unlist(tmp$combined_score)), col = "#00000000") polygon(density(unlist(tmp$combined_score)), col = "#00ffff66") ## ---- fig.width = 8, fig.height = 5, fig.cap = fig.cap12------------------- data(wageningen_GO) GOepi <- wageningen_GO$epi GOall <- wageningen_GO$all denspval <- wilcox.test(GOepi, GOall, alternative = "greater")$p.value for (i in 100:1) { if (i <= 2) { for (j in 1:9) { if (denspval < j*10^(-i)) { denspval <- paste("< ", j*10^(-i), sep = "") } } } else { if (denspval < 10^(-i)) { denspval <- paste("< ", 10^(-i), sep = "") } } } plot(density(GOepi), col = "#00000000", ylim = c(0, max(c(max(density(GOepi)$y), max(density(unlist(GOall))$y)))), main = "van Wageningen Go similarity scores", xlab = "", cex.main = 1.5) polygon(density(GOepi), col = "#ff000066") legend("topleft", legend=paste("p-value", denspval, " "), cex = 1.5) mtext = mtext("C", side = 3, line = 1, outer = FALSE, cex = 3, adj = 0, at = par("usr")[1] - (par("usr")[2]-par("usr")[1])*0.1) lines(density(unlist(GOall)), col = "#00000000") polygon(density(unlist(GOall)), col = "#00ffff66") ## ---- fig.width = 8, fig.height = 5, fig.cap = fig.cap13------------------- data(sameith_GO) GOepi2 <- sameith_GO$epi GOall2 <- sameith_GO$all denspval <- wilcox.test(GOepi2, GOall2, alternative = "greater")$p.value for (i in 100:1) { if (i <= 2) { for (j in 1:9) { if (denspval < j*10^(-i)) { denspval <- paste("< ", j*10^(-i), sep = "") } } } else { if (denspval < 10^(-i)) { denspval <- paste("< ", 10^(-i), sep = "") } } } plot(density(GOepi2), col = "#00000000", ylim = c(0, max(c(max(density(GOepi2)$y), max(density(unlist(GOall2))$y)))), main = "Sameith Go similarity scores", xlab = "", cex.main = 1.5) polygon(density(GOepi2), col = "#ff000066") legend("topleft", legend=paste("p-value", denspval, " "), cex = 1.5) mtext = mtext("D", side = 3, line = 1, outer = FALSE, cex = 3, adj = 0, at = par("usr")[1] - (par("usr")[2]-par("usr")[1])*0.1) lines(density(unlist(GOall2)), col = "#00000000") polygon(density(unlist(GOall2)), col = "#00ffff66") ## ---- fig.width = 8, fig.height = 5, fig.cap = fig.cap14------------------- data(wageningen_GO) golist <- wageningen_GO$golist goterms <- character() for (i in 1:length(golist)) { if (i %in% c(5,8)) { next() } goterms <- c(goterms, golist[[i]]$term_description[which(golist[[i]]$pvalue_fdr < 1)]) } gomat <- matrix(NA, length(unique(goterms)), ncol(wagscreen$ll)) rownames(gomat) <- sort(unique(goterms)) colnames(gomat) <- colnames(wagscreen$ll) for (i in 1:ncol(wagscreen$ll)) { gotmp <- golist[[i]] gotmp <- gotmp[order(gotmp$term_description), ] gomat[which(rownames(gomat) %in% golist[[i]]$term_description), i] <- golist[[i]][which(golist[[i]]$term_description %in% rownames(gomat)), 4] } if (nrow(gomat) > 20) { rownames(gomat) <- NULL } HeatmapOP(gomat, bordercol = "transparent", main = "", sub = "", xrot = 45, col = "RdYlBu", breaks = 100) ## ---- fig.width = 12, fig.height = 3, fig.cap = fig.cap15------------------ data(sameith_GO) golist2 <- sameith_GO$golist goterms <- character() for (i in 1:length(golist2)) { goterms <- c(goterms, golist2[[i]]$term_description[which(golist2[[i]]$pvalue_fdr < 0.1)]) } gomat <- matrix(NA, length(unique(goterms)), ncol(samscreen$ll)) rownames(gomat) <- sort(unique(goterms)) colnames(gomat) <- colnames(samscreen$ll) for (i in 1:ncol(samscreen$ll)) { gotmp <- golist2[[i]] gotmp <- gotmp[order(gotmp$term_description), ] gomat[which(rownames(gomat) %in% golist2[[i]]$term_description), i] <- golist2[[i]][which(golist2[[i]]$term_description %in% rownames(gomat)), 4] } if (nrow(gomat) > 20) { rownames(gomat) <- NULL } colnames(gomat) <- tolower(colnames(gomat)) HeatmapOP(gomat, bordercol = "transparent", main = "", sub = "", xrot = 45, cexCol = 0.5, col = "RdYlBu", breaks = 100) ## ---- fig.height = 7, fig.width = 14, fig.cap = fig.cap16------------------ gos <- unique(wageningen_GO$gos) egenego <- wageningen_GO$egenego gomat <- array(NA, c(length(gos), nrow(wagscreen$ll), ncol(wagscreen$ll))) rownames(gomat) <- sort(gos) colnames(gomat) <- rownames(wagscreen$ll) dimnames(gomat)[[3]] <- colnames(wagscreen$ll) for (i in 1:length(wagscreen$targets)) { if (length(wagscreen$targets[[i]]) == 0) { next() } for (j in 1:length(wagscreen$targets[[i]])) { if (dim(egenego[[i]][[j]])[1] > 0) { gomat[which(rownames(gomat) %in% egenego[[i]][[j]]$term_description), which(dimnames(gomat)[[2]] %in% names(wagscreen$targets[[i]])[j]), i] <- egenego[[i]][[j]]$pvalue_fdr[ order(egenego[[i]][[j]]$term_description)] } } } gomat <- apply(gomat, c(1,2), mean, na.rm = TRUE) gomat <- gomat[order(apply(gomat, 1, function(x) return(sum(is.na(x) == FALSE)))), ] gomat <- gomat[, rev(order(apply(gomat, 2, function(x) return(sum(is.na(x) == FALSE)))))] gomat <- gomat[, which(apply(gomat, 2, function(x) return(any(is.na(x) == FALSE))))] HeatmapOP(gomat, xrot = 45, Colv = FALSE, Rowv = FALSE, col = "RdYlBu", main = "", sub = "", breaks = 100) ## ---- fig.height = 9, fig.width = 16, fig.cap = fig.cap17------------------ gos2 <- unique(sameith_GO$gos) egenego2 <- sameith_GO$egenego gomat <- array(NA, c(length(gos2), nrow(samscreen$ll), ncol(samscreen$ll))) rownames(gomat) <- sort(gos2) colnames(gomat) <- rownames(samscreen$ll) dimnames(gomat)[[3]] <- colnames(samscreen$ll) for (i in 1:length(samscreen$targets)) { if (length(samscreen$targets[[i]]) == 0) { next() } for (j in 1:length(samscreen$targets[[i]])) { if (dim(egenego2[[i]][[j]])[1] > 0) { gomat[which(rownames(gomat) %in% egenego2[[i]][[j]]$term_description), which(dimnames(gomat)[[2]] %in% names(samscreen$targets[[i]])[j]), i] <- egenego2[[i]][[j]]$pvalue_fdr[ order( egenego2[[i]][[j]]$term_description)] } } } gomat <- apply(gomat, c(1,2), mean, na.rm = TRUE) gomat <- gomat[order(apply(gomat, 1, function(x) return(sum(is.na(x) == FALSE)))), ] gomat <- gomat[, rev(order(apply(gomat, 2, function(x) return(sum(is.na(x) == FALSE)))))] gomat <- gomat[, which(apply(gomat, 2, function(x) return(any(is.na(x) == FALSE))))] colnames(gomat) <- tolower(colnames(gomat)) HeatmapOP(gomat, xrot = 45, Colv = FALSE, Rowv = FALSE, col = "RdYlBu", main = "", sub = "", breaks = 100) ## ---- eval=FALSE----------------------------------------------------------- # ###### simulation: # # ## install_github("MartinFXP/B-NEM"); library(bnem) # # library(nem) # # library(minet) # # library(pcalg) # # runs <- 100 # # noiselvls <- c(0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5) # # random <- list(FPrate = 0.1, FNrate = noiselvls, # single = 4, double = 1, reporters = 100, replicates = 3) # # do <- c("n", "p", "a", "e", "b") # # maxTime <- FALSE # # forcelogic <- TRUE # # epinemsearch <- "greedy" # # nIterations <- 3 # # bnemsearch <- "genetic" # # simresults <- SimEpiNEM(runs, do, random, maxTime, forcelogic, # epinemsearch, bnemsearch, nIterations) # # sim <- simresults # # ###### yeast van Wageningen et al.: # # file <- paste("http://www.holstegelab.nl/", # "publications/sv/signaling_redundancy/downloads/DataS1.txt", # sep = "") # # data <- read.delim(file) # # dataM <- data[-(1), (1+(1:(324/2))*2)] # # dataP <- data[-(1), (2+(1:(324/2))*2)] # # data[, 2] <- as.character(data[, 2]) # # rndup <- which(duplicated(data[, 2]) == TRUE) # # data[rndup, 2] <- paste(data[rndup, 2], "_dup", sep = "") # # rownames(dataM) <- rownames(dataP) <- data[2:nrow(data), 2] # # dataM <- dataM[-1, ] # # dataP <- dataP[-1, ] # # dataM <- apply(dataM, c(1,2), as.numeric) # # dataP <- apply(dataP, c(1,2), as.numeric) # # dataBin <- dataM # # sig <- 0.05 # # cutoff <- log2(1.7) # # dataBin[which(dataP < sig & dataP > 0 & abs(dataM) >= cutoff)] <- 1 # # dataBin[which(dataP >= sig | dataP == 0 | abs(dataM) < cutoff)] <- 0 # # dataBin <- dataBin[-which(apply(dataBin, 1, max) == 0), ] # # dataBinWag <- dataBin # # colnames(dataBin) <- gsub(".del.vs..wt", "", colnames(dataBin)) # # colnames(dataBin) <- gsub(".del", "", colnames(dataBin)) # # doubles <- colnames(dataBin)[grep("\\.", colnames(dataBin))] # # if (length(grep("vs", doubles)) > 0) { # doubles <- sort(doubles[-grep("vs", doubles)]) # } else { doubles <- sort(doubles) } # # doubles.genes <- unique(unlist(strsplit(doubles, "\\."))) # # if (length(grep("\\.", colnames(dataBin))) > 0) { # singles <- colnames(dataBin)[-grep("\\.", colnames(dataBin))] # } else { singles <- sort(singles) } # # singles <- unique(sort(singles)) # # wagscreen <- epiScreen(dataBin[, -grep("fus3\\.|ptp2.ptc2", colnames(dataBin))]) # # wagscreen$dataWag <- dataBin[, -grep("fus3.|ptp2.ptc2", colnames(dataBin))] # # ###### yeast Sameith et al.: # # file <- paste("http://www.holstegelab.nl/", # "publications/GSTF_geneticinteractions/", # "downloads/del_mutants_limma.txt", sep = "") # # data <- read.delim(file) # # data <- apply(data, c(1,2), as.character) # # dataM <- data[-1, which(data[1, ] %in% "M")] # # dataM <- apply(dataM, c(1,2), as.numeric) # # dataP <- data[-1, which(data[1, ] %in% "p.value")] # # dataP <- apply(dataP, c(1,2), as.numeric) # # rownames(dataM) <- rownames(dataP) <- data[2:nrow(data), 1] # # dataBin <- dataM # # sig <- 0.01 # # cutoff <- log2(1.5) # # dataBin[which(dataP < sig & dataP > 0 & abs(dataM) >= cutoff)] <- 1 # # dataBin[which(dataP >= sig | dataP == 0 | abs(dataM) < cutoff)] <- 0 # # dataBin <- dataBin[-which(apply(dataBin, 1, max) == 0), ] # # colnames(dataBin) <- gsub("\\.\\.\\.", "\\.", colnames(dataBin)) # # doubles <- colnames(dataBin)[grep("\\.", colnames(dataBin))] # # if (length(grep("vs", doubles)) > 0) { # doubles <- sort(doubles[-grep("vs", doubles)]) # } else { doubles <- sort(doubles) } # # doubles.genes <- unique(unlist(strsplit(doubles, "\\."))) # # if (length(grep("\\.", colnames(dataBin))) > 0) { # singles <- colnames(dataBin)[-grep("\\.", colnames(dataBin))] # } else { singles <- sort(singles) } # # singles <- unique(sort(singles)) # # samscreen <- epiScreen(dataBin) # # samscreen$dataSam <- dataBin # # ## String-db interaction scores: # # library(STRINGdb) # # get_STRING_species(version="10", species_name=NULL)[26, ] # 4932 # # string_db <- STRINGdb$new( version="10", species=4932, score_threshold=0, # input_directory="") # # llmat <- wagscreen$ll # # logicmat <- wagscreen$logic # # string.scores <- list() # # string.names <- character() # # for (i in 1:ncol(llmat)) { # if (sum(!(llmat[, i] %in% c(0,-Inf))) > 0) { # top30 <- llmat[, i] # top30[which(top30 == 0)] <- -Inf # top30 <- top30[which(!(llmat[, i] %in% c(0,-Inf)))] # top30 <- top30[order(top30,decreasing = TRUE)[1:min(30, sum(!(llmat[, i] # %in% c(0,-Inf))))]] # # doubles <- unlist(strsplit(colnames(llmat)[i], "\\.")) # # for (j in names(top30)) { # tmp <- string_db$get_interactions(string_db$mp(c(doubles[1], j))) # string.scores <- c(string.scores, tmp$combined_score) # string.names <- c(string.names, paste(sort(c(doubles[1], j)), # collapse = "_")) # tmp <- string_db$get_interactions(string_db$mp(c(doubles[2], j))) # string.scores <- c(string.scores, tmp$combined_score) # string.names <- c(string.names, paste(sort(c(doubles[2], j)), # collapse = "_")) # } # # } else { # next() # } # } # # # llmat <- samscreen$ll # # logicmat <- samscreen$logic # # string.scores2 <- list() # # string.names2 <- character() # # for (i in 1:ncol(llmat)) { # # if (sum(!(llmat[, i] %in% c(0,-Inf))) > 0) { # top30 <- llmat[, i] # top30[which(top30 == 0)] <- -Inf # top30 <- top30[which(!(llmat[, i] %in% c(0,-Inf)))] # top30 <- top30[order(top30, decreasing = TRUE) # [1:min(30, sum(!(llmat[, i] %in% c(0,-Inf))))]] # # doubles <- unlist(strsplit(colnames(llmat)[i], "\\.")) # # for (j in names(top30)) { # tmp <- string_db$get_interactions(string_db$mp(c(doubles[1], j))) # string.scores2 <- c(string.scores2, tmp$combined_score) # string.names2 <- c(string.names2, paste(sort(c(doubles[1], j)), # collapse = "_")) # tmp <- string_db$get_interactions(string_db$mp(c(doubles[2], j))) # string.scores2 <- c(string.scores2, tmp$combined_score) # string.names2 <- c(string.names2, paste(sort(c(doubles[2], j)), # collapse = "_")) # } # # } else { # next() # } # # } # # ## graph based GO similarity scores: # # library(GOSemSim) # library(AnnotationHub) # library(org.Sc.sgd.db) # # ystGO <- godata("org.Sc.sgd.db", ont = "BP", # keytype = keytypes(org.Sc.sgd.db)[11], computeIC = FALSE) # # ## van Wageningen et al.: # # GOepi <- numeric() # # for (i in 1:ncol(wagscreen$ll)) { # if (i %in% grep("fus3|ptp2.ptc2", colnames(wagscreen$ll))) { next() } # pair <- toupper(unlist(strsplit(colnames(wagscreen$ll)[i], "\\."))) # for (j in which(!is.infinite(wagscreen$ll[, i]) == TRUE & # wagscreen$ll[, i] != 0)) { # tmp <- clusterSim(pair, toupper(rownames(wagscreen$ll)[j]), # semData = ystGO, combine = "max") # if (!is.na(tmp[1])) { # GOepi <- c(GOepi, tmp) # } # } # } # # GOall <- numeric() # # for (i in colnames(wagscreen$ll)) { # pair <- toupper(unlist(strsplit(i, "\\."))) # for (j in rownames(wagscreen$ll)) { # tmp <- clusterSim(pair, toupper(j), semData = ystGO, combine = "max") # if (!is.na(tmp[1])) { # GOall <- c(GOall, tmp) # } # } # } # # ## Sameith et al.: # # GOepi2 <- numeric() # # for (i in 1:ncol(samscreen$ll)) { # if (i %in% grep("fus3|ptp2.ptc2", colnames(samscreen$ll))) { next() } # pair <- toupper(unlist(strsplit(colnames(samscreen$ll)[i], "\\."))) # for (j in which(!is.infinite(samscreen$ll[, i]) == TRUE & # samscreen$ll[, i] != 0)) { # tmp <- clusterSim(pair, toupper(rownames(samscreen$ll)[j]), # semData = ystGO, combine = "max") # if (!is.na(tmp[1])) { # GOepi2 <- c(GOepi2, tmp) # } # } # } # # GOall2 <- numeric() # # for (i in colnames(samscreen$ll)) { # pair <- toupper(unlist(strsplit(i, "\\."))) # for (j in rownames(samscreen$ll)) { # tmp <- clusterSim(pair, toupper(j), semData = ystGO, combine = "max") # if (!is.na(tmp[1])) { # GOall2 <- c(GOall2, tmp) # } # } # } # # ###### Go enrichment analysis: # # ## van Wageningen et al.: # # string_db$set_background( # string_db$mp(unique(c(unlist(strsplit(colnames(wagscreen$ll), "\\.")), # rownames(wagscreen$ll))))) # # golist <- list() # # for (i in 1:ncol(wagscreen$ll)) { # golist[[i]] <- string_db$get_enrichment(string_db$mp(unique( # c(unlist(strsplit(colnames(wagscreen$ll)[i], "\\.")), # rownames(wagscreen$ll)[which(!(wagscreen$logic[, i] %in% # c("NOINFO", "NOEPI")))]))), # category = "KEGG", methodMT = "fdr", iea = TRUE) # } # # string_db$set_background(string_db$mp(rownames(wagscreen$dataWag))) # # egenego <- list() # # gos <- character() # # for (i in 1:length(wagscreen$targets)) { # egenego[[i]] <- list() # if (length(wagscreen$targets[[i]]) == 0) { next() } # for (j in 1:length(wagscreen$targets[[i]])) { # egenego[[i]][[j]] <- string_db$get_enrichment( # string_db$mp(wagscreen$targets[[i]][[j]]), # category = "KEGG", methodMT = "fdr", iea = TRUE) # if (dim(egenego[[i]][[j]])[1] > 0) { # gos <- c(gos, egenego[[i]][[j]]$term_description) # } # } # } # ## Sameith et al.: # # string_db$set_background(string_db$mp(unique(c(unlist( # strsplit(colnames(samscreen$ll), "\\.")), rownames(samscreen$ll))))) # # golist2 <- list() # # for (i in 1:ncol(samscreen$ll)) { # golist2[[i]] <- string_db$get_enrichment(string_db$mp( # unique(c(unlist(strsplit(colnames(samscreen$ll)[i], "\\.")), # rownames(samscreen$ll) # [which(!(samscreen$logic[, i] %in% c("NOINFO", "NOEPI")))]))), # category = "KEGG", methodMT = "fdr", iea = TRUE) # } # # string_db$set_background(string_db$mp(rownames(samscreen$dataWag))) # # egenego2 <- list() # # gos2 <- character() # # for (i in 1:length(samscreen$targets)) { # egenego2[[i]] <- list() # if (length(samscreen$targets[[i]]) == 0) { next() } # for (j in 1:length(samscreen$targets[[i]])) { # egenego2[[i]][[j]] <- string_db$get_enrichment( # string_db$mp(samscreen$targets[[i]][[j]]), # category = "KEGG", methodMT = "fdr", iea = TRUE) # if (dim(egenego2[[i]][[j]])[1] > 0) { # gos2 <- c(gos2, egenego2[[i]][[j]]$term_description) # } # } # } ## -------------------------------------------------------------------------- sessionInfo()