### R code from vignette source 'BLMA.Rnw' ################################################### ### code chunk number 1: BLMA.Rnw:114-142 ################################################### # one-sample tests library(BLMA) set.seed(1) x <- rnorm(10, mean = 0) # one-sample left-tailed t-test t.test(x, mu=1, alternative = "less")$p.value # one-sample left-tailed intra-experiment analysis with t-test intraAnalysisClassic(x, func=t.test, mu=1, alternative = "less") # one-sample right-tailed t-test t.test(x, mu=1, alternative = "greater")$p.value # one-sample right-tailed intra-experiment analysis with t-test intraAnalysisClassic(x, func=t.test, mu=1, alternative = "greater") # one-sample two-tailed t-test t.test(x, mu=1)$p.value # one-sample two-tailed intra-experiment analysis with t-test intraAnalysisClassic(x, func=t.test, mu=1) # one-sample left-tailed Wilcoxon test wilcox.test(x, mu=1, alternative = "less")$p.value # one-sample left-tailed intra-experiment analysis with Wilcoxon test intraAnalysisClassic(x, func=wilcox.test, mu=1, alternative = "less") # one-sample right-tailed Wilcoxon test wilcox.test(x, mu=1, alternative = "greater")$p.value # one-sample right-tailed intra-experiment analysis with Wilcoxon test intraAnalysisClassic(x, func=wilcox.test, mu=1, alternative = "greater") # one-sample two-tailed Wilcoxon test wilcox.test(x, mu=1)$p.value # one-sample two-tailed intra-experiment analysis with Wilcoxon test intraAnalysisClassic(x, func=wilcox.test, mu=1) ################################################### ### code chunk number 2: BLMA.Rnw:147-162 ################################################### # two-sample tests set.seed(1) x <- rnorm(20, mean=0); y=rnorm(20, mean=1) # two-sample left-tailed t-test t.test(x,y,alternative="less")$p.value # two-sample left-tailed intra-experiment analysis with t-test intraAnalysisClassic(x, y, func=t.test, alternative = "less") # two-sample right-tailed t-test t.test(x,y,alternative="greater")$p.value # two-sample right-tailed intra-experiment analysis with t-test intraAnalysisClassic(x, y, func=t.test, alternative = "greater") # two-sample two-tailed t-test t.test(x,y)$p.value # two-sample two-tailed intra-experiment analysis with t-test intraAnalysisClassic(x, y, func=t.test) ################################################### ### code chunk number 3: BLMA.Rnw:168-189 ################################################### # one-sample tests set.seed(1) l1 <- lapply(as.list(seq(3)),FUN=function (x) rnorm(n=10, mean=1)) l0 <- lapply(as.list(seq(3)),FUN=function (x) rnorm(n=10, mean=0)) # one-sample right-tailed t-test lapply(l1, FUN=function(x) t.test(x, alternative="greater")$p.value) # combining the p-values of one-sample t-test: addCLT(unlist(lapply(l1, FUN=function(x) t.test(x, alternative="greater")$p.value))) #Bi-level meta-analysis with one-sample right-tailed t-test bilevelAnalysisClassic(x=l1, func=t.test, alternative="greater") # two-sample left-tailed t-test lapply(seq(l1), FUN=function(i,l1,l0) t.test(l1[[i]], l0[[i]], alternative="greater")$p.value, l1, l0) # combining the p-values of one-sample t-test: addCLT(unlist(lapply(seq(l1), FUN=function(i,l1,l0) t.test(l1[[i]], l0[[i]], alternative="greater")$p.value, l1, l0))) #Bi-level meta-analysis with two-sample right-tailed t-test bilevelAnalysisClassic(x=l1, y=l0, func=t.test, alternative="greater") #Bi-level meta-analysis with two-sample left-tailed t-test bilevelAnalysisClassic(x=l1, y=l0, func=t.test, alternative="less") ################################################### ### code chunk number 4: BLMA.Rnw:232-253 ################################################### library(BLMA) # load KEGG pathways and create genesets x=loadKEGGPathways() gslist <- lapply(x$kpg,FUN=function(y){return (nodes(y));}) gs.names <- x$kpn[names(gslist)] # load the 4 AML datasets dataSets <- c("GSE17054", "GSE57194", "GSE33223", "GSE42140") data(list=dataSets, package="BLMA") # prepare dataList and groupList names(dataSets) <- dataSets dataList <- lapply(dataSets, function(dataset) get(paste0("data_", dataset))) groupList <- lapply(dataSets, function(dataset) get(paste0("group_", dataset))) # perform bi-level meta-analysis in conjunction with ORA system.time(ORAComb <- bilevelAnalysisGeneset(gslist, gs.names, dataList, groupList, enrichment = "ORA")) #print the results options(digits=2) head(ORAComb[, c("Name", "pBLMA", "pBLMA.fdr", "rBLMA")]) ################################################### ### code chunk number 5: BLMA.Rnw:268-275 ################################################### # perform bi-level meta-analysis in conjunction with GSA system.time(GSAComb <- bilevelAnalysisGeneset(gslist, gs.names, dataList, groupList, enrichment = "GSA", nperms=200, random.seed = 1)) #print the results options(digits=2) head(GSAComb[, c("Name", "pBLMA", "pBLMA.fdr", "rBLMA")]) ################################################### ### code chunk number 6: BLMA.Rnw:288-294 ################################################### set.seed(1) system.time(PADOGComb <- bilevelAnalysisGeneset(gslist, gs.names, dataList, groupList, enrichment = "PADOG", NI=200)) #print the results options(digits=2) head(PADOGComb[, c("Name", "pBLMA", "pBLMA.fdr", "rBLMA")]) ################################################### ### code chunk number 7: BLMA.Rnw:304-309 ################################################### x <- loadKEGGPathways() system.time(IAComb <- bilevelAnalysisPathway(x$kpg, x$kpn, dataList, groupList)) #print the results options(digits=2) head(IAComb[, c("Name", "pBLMA", "pBLMA.fdr", "rBLMA")]) ################################################### ### code chunk number 8: BLMA.Rnw:337-355 ################################################### #perform intra-experiment analysis of the dataset GSE33223 using addCLT library(BLMA) data(GSE33223) system.time(X <- intraAnalysisGene(data_GSE33223, group_GSE33223)) X <- X[order(X$pIntra), ] # top 10 genes head(X) # bottom 10 genes tail(X) #perform intra-experiment analysis of GSE33223 using Fisher's method system.time(Y <- intraAnalysisGene(data_GSE33223, group_GSE33223, metaMethod=fisherMethod)) Y = Y[order(Y$pIntra), ] # top 10 genes head(Y) # bottom 10 genes tail(Y) ################################################### ### code chunk number 9: BLMA.Rnw:367-373 ################################################### system.time(Z <- bilevelAnalysisGene(dataList = dataList, groupList = groupList)) # top 10 genes head(Z) # bottom 10 genes tail(Z)