rm(list=ls()) quad <- read.table("all_PQS_and_TSS_mappings.csv", sep=",",header=TRUE,stringsAsFactors=FALSE) colnames(quad)[11:19] <- c("stacks","fa","fc","ft","ng","length2","l1","l2","l3") # import differential gene expression dataset for BS or WS bdat <- read.table(".csv", sep=",", header=TRUE) # split gene sets into up- and down-regulated blm.up1 <- as.data.frame(bdat[with(bdat, logFC > 0), ]$gene) blm.down1 <- as.data.frame(bdat[with(bdat, logFC < 0), ]$gene) colnames(blm.up1) <- colnames(blm.down1) <- "gene_name" # merge blm.up1 and blm.down1 datasets with quad --> full TSS and PQS lists blm.up2 <- merge(quad, blm.up1, by="gene_name") blm.down2 <- merge(quad, blm.down1, by="gene_name") # calculate numbers of TSS in resepctive datasets genomic.tss <- length(unique(quad$unique.tss.number)) blm.up.tss <- length(unique(blm.up2$unique.tss.number)) blm.down.tss <- length(unique(blm.down2$unique.tss.number)) # print datasets to file ####################### sink("dataset stats.txt") print("number of unique genes") print(length(unique(quad$gene_name))) print("number of unique TSS") print(length(unique(quad$unique.tss.number))) print("number of PQS per TSS genome-wide") print(sum(!is.na(quad$delta))/genomic.tss) print("number of genes in bdat dataset") print(nrow(bdat)) print("number of up-regulated genes") print(nrow(blm.up1)) print("number of TSS in up-regulated gene set") print(blm.up.tss) print("number of PQS in up-regulated gene set") print(sum(!is.na(blm.up2$delta))) print("number of PQS per TSS in up-regulated gene set") print(sum(!is.na(blm.up2$delta))/blm.up.tss) print("number of down-regulated genes") print(nrow(blm.down1)) print("number of TSS in down-regulated gene set") print(blm.down.tss) print("number of PQS in down-regulated gene set") print(sum(!is.na(blm.down2$delta))) print("number of PQS per TSS in down-regulated gene set") print(sum(!is.na(blm.down2$delta))/blm.down.tss) sink() ###################### # split into TS and NTS strand datasets ###################### # split into plus and minus strands, keep only row entries with PQS present all <- quad[with(quad, is.na(delta) == FALSE), ] plus <- quad[with(quad, strand.orient == "t" & is.na(delta) == FALSE), ] minus <- quad[with(quad, strand.orient == "nt" & is.na(delta) == FALSE), ] blm.up <- blm.up2[with(blm.up2, is.na(delta) == FALSE), ] blm.up.plus <- blm.up2[with(blm.up2, strand.orient == "t" & is.na(delta) == FALSE), ] blm.up.minus <- blm.up2[with(blm.up2, strand.orient == "nt" & is.na(delta) == FALSE), ] blm.down <- blm.down2[with(blm.down2, is.na(delta) == FALSE), ] blm.down.plus <- blm.down2[with(blm.down2, strand.orient == "t" & is.na(delta) == FALSE), ] blm.down.minus <- blm.down2[with(blm.down2, strand.orient == "nt" & is.na(delta) == FALSE), ] sink("feature averages.txt") print("The order of values printed is fa,fc,ft,stacks,l1,l2,l3,length.") print("Dataset=all") dat <- all for(i in c(12,13,14,11,17,18,19,16)){ print(mean(dat[,i],na.rm=TRUE)) } print(" ") print("Dataset=plus") dat <- plus for(i in c(12,13,14,11,17,18,19,16)){ print(mean(dat[,i],na.rm=TRUE)) } print(" ") print("Dataset=minus") dat <- minus for(i in c(12,13,14,11,17,18,19,16)){ print(mean(dat[,i],na.rm=TRUE)) } print(" ") print("Dataset=blm.up") dat <- blm.up for(i in c(13,14,15,12,18,19,20,17)){ print(mean(dat[,i],na.rm=TRUE)) } print(" ") print("Dataset=blm.up.plus") dat <- blm.up.plus for(i in c(13,14,15,12,18,19,20,17)){ print(mean(dat[,i],na.rm=TRUE)) } print(" ") print("Dataset=blm.up.minus") dat <- blm.up.minus for(i in c(13,14,15,12,18,19,20,17)){ print(mean(dat[,i],na.rm=TRUE)) } print(" ") print("Dataset=blm.down") dat <- blm.down for(i in c(13,14,15,12,18,19,20,17)){ print(mean(dat[,i],na.rm=TRUE)) } print(" ") print("Dataset=blm.down.plus") dat <- blm.down.plus for(i in c(13,14,15,12,18,19,20,17)){ print(mean(dat[,i],na.rm=TRUE)) } print(" ") print("Dataset=blm.down.minus") dat <- blm.down.minus for(i in c(13,14,15,12,18,19,20,17)){ print(mean(dat[,i],na.rm=TRUE)) } print(" ") sink() width <- 200 # examine whether PQS sequence parameters are homogeneous along region near TSS by <- 100 map.points <- seq(from=-2000+width/2, to=2000-width/2, by=by) # vectors for p-value export (compare to random sample of PQS from a2 distribution. use same size sample as in the specified bin) # vectors for plus strand ################ p.fa.outp <- NULL p.fc.outp <- NULL p.ft.outp <- NULL p.length.outp <- NULL p.l1.outp <- NULL p.l2.outp <- NULL p.l3.outp <- NULL p.stacks.outp <- NULL # blm up plus p.fa.outpblmup <- NULL p.fc.outpblmup <- NULL p.ft.outpblmup <- NULL p.length.outpblmup <- NULL p.l1.outpblmup <- NULL p.l2.outpblmup <- NULL p.l3.outpblmup <- NULL p.stacks.outpblmup <- NULL # blm down.plus p.fa.outpblmdown <- NULL p.fc.outpblmdown <- NULL p.ft.outpblmdown <- NULL p.length.outpblmdown <- NULL p.l1.outpblmdown <- NULL p.l2.outpblmdown <- NULL p.l3.outpblmdown <- NULL p.stacks.outpblmdown <- NULL # vecotrs for minus strand p.fa.outm <- NULL p.fc.outm <- NULL p.ft.outm <- NULL p.length.outm <- NULL p.l1.outm <- NULL p.l2.outm <- NULL p.l3.outm <- NULL p.stacks.outm <- NULL # blm up minus p.fa.outmblmup <- NULL p.fc.outmblmup <- NULL p.ft.outmblmup <- NULL p.length.outmblmup <- NULL p.l1.outmblmup <- NULL p.l2.outmblmup <- NULL p.l3.outmblmup <- NULL p.stacks.outmblmup <- NULL # blm down minus p.fa.outmblmdown <- NULL p.fc.outmblmdown <- NULL p.ft.outmblmdown <- NULL p.length.outmblmdown <- NULL p.l1.outmblmdown <- NULL p.l2.outmblmdown <- NULL p.l3.outmblmdown <- NULL p.stacks.outmblmdown <- NULL # p values for control samples from either + or - strand # blm up plus p.fa.outpblmup.c <- NULL p.fc.outpblmup.c <- NULL p.ft.outpblmup.c <- NULL p.length.outpblmup.c <- NULL p.l1.outpblmup.c <- NULL p.l2.outpblmup.c <- NULL p.l3.outpblmup.c <- NULL p.stacks.outpblmup.c <- NULL # blm down.plus p.fa.outpblmdown.c <- NULL p.fc.outpblmdown.c <- NULL p.ft.outpblmdown.c <- NULL p.length.outpblmdown.c <- NULL p.l1.outpblmdown.c <- NULL p.l2.outpblmdown.c <- NULL p.l3.outpblmdown.c <- NULL p.stacks.outpblmdown.c <- NULL # blm up minus p.fa.outmblmup.c <- NULL p.fc.outmblmup.c <- NULL p.ft.outmblmup.c <- NULL p.length.outmblmup.c <- NULL p.l1.outmblmup.c <- NULL p.l2.outmblmup.c <- NULL p.l3.outmblmup.c <- NULL p.stacks.outmblmup.c <- NULL # blm down minus p.fa.outmblmdown.c <- NULL p.fc.outmblmdown.c <- NULL p.ft.outmblmdown.c <- NULL p.length.outmblmdown.c <- NULL p.l1.outmblmdown.c <- NULL p.l2.outmblmdown.c <- NULL p.l3.outmblmdown.c <- NULL p.stacks.outmblmdown.c <- NULL # vectors for exporting mean values for parameters # vectors for plus strand fa.outp <- NULL fc.outp <- NULL ft.outp <- NULL length.outp <- NULL l1.outp <- NULL l2.outp <- NULL l3.outp <- NULL stacks.outp <- NULL # blm up plus fa.outpblmup <- NULL fc.outpblmup <- NULL ft.outpblmup <- NULL length.outpblmup <- NULL l1.outpblmup <- NULL l2.outpblmup <- NULL l3.outpblmup <- NULL stacks.outpblmup <- NULL # blm down plus fa.outpblmdown <- NULL fc.outpblmdown <- NULL ft.outpblmdown <- NULL length.outpblmdown <- NULL l1.outpblmdown <- NULL l2.outpblmdown <- NULL l3.outpblmdown <- NULL stacks.outpblmdown <- NULL # vectors for minus strand fa.outm <- NULL fc.outm <- NULL ft.outm <- NULL length.outm <- NULL l1.outm <- NULL l2.outm <- NULL l3.outm <- NULL stacks.outm <- NULL # blm up minus fa.outmblmup <- NULL fc.outmblmup <- NULL ft.outmblmup <- NULL length.outmblmup <- NULL l1.outmblmup <- NULL l2.outmblmup <- NULL l3.outmblmup <- NULL stacks.outmblmup <- NULL # blm down minus fa.outmblmdown <- NULL fc.outmblmdown <- NULL ft.outmblmdown <- NULL length.outmblmdown <- NULL l1.outmblmdown <- NULL l2.outmblmdown <- NULL l3.outmblmdown <- NULL stacks.outmblmdown <- NULL gene.names <- unique(quad$gene_name) ############### for(h in 1:1){ # control datasets have rows that correspond to the PQS in a dataset of TSS that correspond to the same number # of genes in test datasets cdat.up.plus <- plus[with(plus, gene_name %in% sample(gene.names, length(unique(blm.up2$gene_name)), replace=FALSE)),] cdat.down.plus <- plus[with(plus, gene_name %in% sample(gene.names, length(unique(blm.down2$gene_name)), replace=FALSE)),] cdat.up.minus <- minus[with(minus, gene_name %in% sample(gene.names, length(unique(blm.up2$gene_name)), replace=FALSE)),] cdat.down.minus <- minus[with(minus, gene_name %in% sample(gene.names, length(unique(blm.down2$gene_name)), replace=FALSE)),] for(k in 1:6){ for(i in map.points){ if(k == 1){ dat <- plus[with(plus, abs(delta-i) < width/2), ] cdat <- all[with(all, abs(delta-i) < width/2), ] for(j in 1:8){ if(j ==1){Y <- dat$fa Y2 <- cdat$fa fa.outp <- c(fa.outp, mean(Y)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fa.outp <- c(p.fa.outp, p$p.value) } if(j ==2){Y <- dat$fc Y2 <- cdat$fc fc.outp <- c(fc.outp, mean(Y)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fc.outp <- c(p.fc.outp, p$p.value) } if(j ==3){Y <- dat$ft Y2 <- cdat$ft ft.outp <- c(ft.outp, mean(Y)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.ft.outp <- c(p.ft.outp, p$p.value) } if(j ==4){Y <- dat$length Y2 <- cdat$length length.outp <- c(length.outp, mean(Y)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.length.outp <- c(p.length.outp, p$p.value) } if(j ==5){Y <- dat$l1 Y2 <- cdat$l1 l1.outp <- c(l1.outp, mean(Y, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l1.outp <- c(p.l1.outp, p$p.value) } if(j ==6){Y <- dat$l2 Y2 <- cdat$l2 l2.outp <- c(l2.outp, mean(Y, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l2.outp <- c(p.l2.outp, p$p.value) } if(j ==7){Y <- dat$l3 Y2 <- cdat$l3 l3.outp <- c(l3.outp, mean(Y, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l3.outp <- c(p.l3.outp, p$p.value) } if(j ==8){Y <- dat$stacks Y2 <- cdat$stacks stacks.outp <- c(stacks.outp, mean(Y)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.stacks.outp <- c(p.stacks.outp, p$p.value) } } } if(k == 2){ dat <- blm.up.plus[with(blm.up.plus, abs(delta-i) < width/2), ] cdat <- plus[with(plus, abs(delta-i) < width/2), ] ctl <- cdat.up.plus[with(cdat.up.plus, abs(delta-i) < width/2), ] for(j in 1:8){ if(j ==1){Y <- dat$fa Y2 <- cdat$fa fa.outpblmup <- c(fa.outpblmup, mean(Y)/mean(Y2)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fa.outpblmup <- c(p.fa.outpblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.fa.outpblmup.c <- c(p.fa.outpblmup.c,p2$p.value) } if(j ==2){Y <- dat$fc Y2 <- cdat$fc fc.outpblmup <- c(fc.outpblmup, mean(Y)/mean(Y2)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fc.outpblmup <- c(p.fc.outpblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.fc.outpblmup.c <- c(p.fc.outpblmup.c,p2$p.value) } if(j ==3){Y <- dat$ft Y2 <- cdat$ft ft.outpblmup <- c(ft.outpblmup, mean(Y)/mean(Y2)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.ft.outpblmup <- c(p.ft.outpblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.ft.outpblmup.c <- c(p.ft.outpblmup.c,p2$p.value) } if(j ==4){Y <- dat$length Y2 <- cdat$length length.outpblmup <- c(length.outpblmup, mean(Y)/mean(Y2)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.length.outpblmup <- c(p.length.outpblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.length.outpblmup.c <- c(p.length.outpblmup.c,p2$p.value) } if(j ==5){Y <- dat$l1 Y2 <- cdat$l1 l1.outpblmup <- c(l1.outpblmup, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l1.outpblmup <- c(p.l1.outpblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l1.outpblmup.c <- c(p.l1.outpblmup.c,p2$p.value) } if(j ==6){Y <- dat$l2 Y2 <- cdat$l2 l2.outpblmup <- c(l2.outpblmup, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l2.outpblmup <- c(p.l2.outpblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l2.outpblmup.c <- c(p.l2.outpblmup.c,p2$p.value) } if(j ==7){Y <- dat$l3 Y2 <- cdat$l3 l3.outpblmup <- c(l3.outpblmup, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l3.outpblmup <- c(p.l3.outpblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l3.outpblmup.c <- c(p.l3.outpblmup.c,p2$p.value) } if(j ==8){Y <- dat$stacks Y2 <- cdat$stacks stacks.outpblmup <- c(stacks.outpblmup, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.stacks.outpblmup <- c(p.stacks.outpblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.stacks.outpblmup.c <- c(p.stacks.outpblmup.c,p2$p.value) } } } if(k == 3){ dat <- blm.down.plus[with(blm.down.plus, abs(delta-i) < width/2), ] cdat <- plus[with(plus, abs(delta-i) < width/2), ] ctl <- cdat.down.plus[with(cdat.down.plus, abs(delta-i) < width/2), ] for(j in 1:8){ if(j ==1){Y <- dat$fa Y2 <- cdat$fa fa.outpblmdown <- c(fa.outpblmdown, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fa.outpblmdown <- c(p.fa.outpblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.fa.outpblmdown.c <- c(p.fa.outpblmdown.c,p2$p.value) } if(j ==2){Y <- dat$fc Y2 <- cdat$fc fc.outpblmdown <- c(fc.outpblmdown, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fc.outpblmdown <- c(p.fc.outpblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.fc.outpblmdown.c <- c(p.fc.outpblmdown.c,p2$p.value) } if(j ==3){Y <- dat$ft Y2 <- cdat$ft ft.outpblmdown <- c(ft.outpblmdown, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.ft.outpblmdown <- c(p.ft.outpblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.ft.outpblmdown.c <- c(p.ft.outpblmdown.c,p2$p.value) } if(j ==4){Y <- dat$length Y2 <- cdat$length length.outpblmdown <- c(length.outpblmdown, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.length.outpblmdown <- c(p.length.outpblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.length.outpblmdown.c <- c(p.length.outpblmdown.c,p2$p.value) } if(j ==5){Y <- dat$l1 Y2 <- cdat$l1 l1.outpblmdown <- c(l1.outpblmdown, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l1.outpblmdown <- c(p.l1.outpblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l1.outpblmdown.c <- c(p.l1.outpblmdown.c,p2$p.value) } if(j ==6){Y <- dat$l2 Y2 <- cdat$l2 l2.outpblmdown <- c(l2.outpblmdown, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l2.outpblmdown <- c(p.l2.outpblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l2.outpblmdown.c <- c(p.l2.outpblmdown.c,p2$p.value) } if(j ==7){Y <- dat$l3 Y2 <- cdat$l3 l3.outpblmdown <- c(l3.outpblmdown, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l3.outpblmdown <- c(p.l3.outpblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l3.outpblmdown.c <- c(p.l3.outpblmdown.c,p2$p.value) } if(j ==8){Y <- dat$stacks Y2 <- cdat$stacks stacks.outpblmdown <- c(stacks.outpblmdown, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.stacks.outpblmdown <- c(p.stacks.outpblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.stacks.outpblmdown.c <- c(p.stacks.outpblmdown.c,p2$p.value) } } } if(k == 4){ dat <- minus[with(minus, abs(delta-i) < width/2), ] cdat <- all[with(all, abs(delta-i) < width/2), ] for(j in 1:8){ if(j ==1){Y <- dat$fa Y2 <- cdat$fa fa.outm <- c(fa.outm, mean(Y)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fa.outm <- c(p.fa.outm, p$p.value) } if(j ==2){Y <- dat$fc Y2 <- cdat$fc fc.outm <- c(fc.outm, mean(Y)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fc.outm <- c(p.fc.outm, p$p.value) } if(j ==3){Y <- dat$ft Y2 <- cdat$ft ft.outm <- c(ft.outm, mean(Y)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.ft.outm <- c(p.ft.outm, p$p.value) } if(j ==4){Y <- dat$length Y2 <- cdat$length length.outm <- c(length.outm, mean(Y)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.length.outm <- c(p.length.outm, p$p.value) } if(j ==5){Y <- dat$l1 Y2 <- cdat$l1 l1.outm <- c(l1.outm, mean(Y, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l1.outm <- c(p.l1.outm, p$p.value) } if(j ==6){Y <- dat$l2 Y2 <- cdat$l2 l2.outm <- c(l2.outm, mean(Y, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l2.outm <- c(p.l2.outm, p$p.value) } if(j ==7){Y <- dat$l3 Y2 <- cdat$l3 l3.outm <- c(l3.outm, mean(Y, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l3.outm <- c(p.l3.outm, p$p.value) } if(j ==8){Y <- dat$stacks Y2 <- cdat$stacks stacks.outm <- c(stacks.outm, mean(Y)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.stacks.outm <- c(p.stacks.outm, p$p.value) } } } if(k == 5){ dat <- blm.up.minus[with(blm.up.minus, abs(delta-i) < width/2), ] cdat <- minus[with(minus, abs(delta-i) < width/2), ] ctl <- cdat.up.minus[with(cdat.up.minus, abs(delta-i) < width/2), ] for(j in 1:8){ if(j ==1){Y <- dat$fa Y2 <- cdat$fa fa.outmblmup <- c(fa.outmblmup, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fa.outmblmup <- c(p.fa.outmblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.fa.outmblmup.c <- c(p.fa.outmblmup.c,p2$p.value) } if(j ==2){Y <- dat$fc Y2 <- cdat$fc fc.outmblmup <- c(fc.outmblmup, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fc.outmblmup <- c(p.fc.outmblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.fc.outmblmup.c <- c(p.fc.outmblmup.c,p2$p.value) } if(j ==3){Y <- dat$ft Y2 <- cdat$ft ft.outmblmup <- c(ft.outmblmup, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.ft.outmblmup <- c(p.ft.outmblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.ft.outmblmup.c <- c(p.ft.outmblmup.c,p2$p.value) } if(j ==4){Y <- dat$length Y2 <- cdat$length length.outmblmup <- c(length.outmblmup, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.length.outmblmup <- c(p.length.outmblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.length.outmblmup.c <- c(p.length.outmblmup.c,p2$p.value) } if(j ==5){Y <- dat$l1 Y2 <- cdat$l1 l1.outmblmup <- c(l1.outmblmup, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l1.outmblmup <- c(p.l1.outmblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l1.outmblmup.c <- c(p.l1.outmblmup.c,p2$p.value) } if(j ==6){Y <- dat$l2 Y2 <- cdat$l2 l2.outmblmup <- c(l2.outmblmup, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l2.outmblmup <- c(p.l2.outmblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l2.outmblmup.c <- c(p.l2.outmblmup.c,p2$p.value) } if(j ==7){Y <- dat$l3 Y2 <- cdat$l3 l3.outmblmup <- c(l3.outmblmup, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l3.outmblmup <- c(p.l3.outmblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l3.outmblmup.c <- c(p.l3.outmblmup.c,p2$p.value) } if(j ==8){Y <- dat$stacks Y2 <- cdat$stacks stacks.outmblmup <- c(stacks.outmblmup, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.stacks.outmblmup <- c(p.stacks.outmblmup, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.stacks.outmblmup.c <- c(p.stacks.outmblmup.c,p2$p.value) } } } if(k == 6){ dat <- blm.down.minus[with(blm.down.minus, abs(delta-i) < width/2), ] cdat <- minus[with(minus, abs(delta-i) < width/2), ] ctl <- cdat.down.minus[with(cdat.down.minus, abs(delta-i) < width/2), ] for(j in 1:8){ if(j ==1){Y <- dat$fa Y2 <- cdat$fa fa.outmblmdown <- c(fa.outmblmdown, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fa.outmblmdown <- c(p.fa.outmblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.fa.outmblmdown.c <- c(p.fa.outmblmdown.c,p2$p.value) } if(j ==2){Y <- dat$fc Y2 <- cdat$fc fc.outmblmdown <- c(fc.outmblmdown, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.fc.outmblmdown <- c(p.fc.outmblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.fc.outmblmdown.c <- c(p.fc.outmblmdown.c,p2$p.value) } if(j ==3){Y <- dat$ft Y2 <- cdat$ft ft.outmblmdown <- c(ft.outmblmdown, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.ft.outmblmdown <- c(p.ft.outmblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.ft.outmblmdown.c <- c(p.ft.outmblmdown.c,p2$p.value) } if(j ==4){Y <- dat$length Y2 <- cdat$length length.outmblmdown <- c(length.outmblmdown, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.length.outmblmdown <- c(p.length.outmblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.length.outmblmdown.c <- c(p.length.outmblmdown.c,p2$p.value) } if(j ==5){Y <- dat$l1 Y2 <- cdat$l1 l1.outmblmdown <- c(l1.outmblmdown, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l1.outmblmdown <- c(p.l1.outmblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l1.outmblmdown.c <- c(p.l1.outmblmdown.c,p2$p.value) } if(j ==6){Y <- dat$l2 Y2 <- cdat$l2 l2.outmblmdown <- c(l2.outmblmdown, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l2.outmblmdown <- c(p.l2.outmblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l2.outmblmdown.c <- c(p.l2.outmblmdown.c,p2$p.value) } if(j ==7){Y <- dat$l3 Y2 <- cdat$l3 l3.outmblmdown <- c(l3.outmblmdown, mean(Y, na.rm=TRUE)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.l3.outmblmdown <- c(p.l3.outmblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.l3.outmblmdown.c <- c(p.l3.outmblmdown.c,p2$p.value) } if(j ==8){Y <- dat$stacks Y2 <- cdat$stacks stacks.outmblmdown <- c(stacks.outmblmdown, mean(Y)/mean(Y2, na.rm=TRUE)) testY <- data.frame(Y, rep(2, length(Y))) colnames(testY) <- c("variable","classifier") ctlY <- data.frame(Y2, rep(1,length(Y2))) colnames(ctlY) <- c("variable","classifier") anova.df <- rbind(ctlY, testY) p <- oneway.test(variable ~ classifier, data=anova.df) p.stacks.outmblmdown <- c(p.stacks.outmblmdown, p$p.value) if(j == 1){name <- ctl$fa} if(j == 2){name <- ctl$fc} if(j == 3){name <- ctl$ft} if(j == 4){name <- ctl$length} if(j == 5){name <- ctl$l1} if(j == 6){name <- ctl$l2} if(j == 7){name <- ctl$l3} if(j == 8){name <- ctl$stacks} ctl2Y <- data.frame(name, rep(2, length(name))) colnames(ctl2Y) <- c("variable", "classifier") anova2.df <- rbind(ctlY, ctl2Y) p2 <- oneway.test(variable ~ classifier, data=anova2.df) p.stacks.outmblmdown.c <- c(p.stacks.outmblmdown.c,p2$p.value) } } } print(i) } print("k==........................") print(k) print("h=............................................................") print(h) print("...............") } } # write output to file showing enrichment ratios and p-values as a function of distance output.df <- data.frame(map.points,fa.outpblmup,p.fa.outpblmup,fc.outpblmup,p.fc.outpblmup,ft.outpblmup,p.ft.outpblmup,l1.outpblmup,p.l1.outpblmup, l2.outpblmup,p.l2.outpblmup,l3.outpblmup,p.l3.outpblmup,stacks.outpblmup,p.stacks.outpblmup,length.outpblmup,p.length.outpblmup, fa.outpblmdown,p.fa.outpblmdown,fc.outpblmdown,p.fc.outpblmdown,ft.outpblmdown,p.ft.outpblmdown,l1.outpblmdown,p.l1.outpblmdown, l2.outpblmdown,p.l2.outpblmdown,l3.outpblmdown,p.l3.outpblmdown,stacks.outpblmdown,p.stacks.outpblmdown,length.outpblmdown,p.length.outpblmdown, fa.outmblmup,p.fa.outmblmup,fc.outmblmup,p.fc.outmblmup,ft.outmblmup,p.ft.outmblmup,l1.outmblmup,p.l1.outmblmup, l2.outmblmup,p.l2.outmblmup,l3.outmblmup,p.l3.outmblmup,stacks.outmblmup,p.stacks.outmblmup,length.outmblmup,p.length.outmblmup, fa.outmblmdown,p.fa.outmblmdown,fc.outmblmdown,p.fc.outmblmdown,ft.outmblmdown,p.ft.outmblmdown,l1.outmblmdown,p.l1.outmblmdown, l2.outmblmdown,p.l2.outmblmdown,l3.outmblmdown,p.l3.outmblmdown,stacks.outmblmdown,p.stacks.outmblmdown,length.outmblmdown,p.length.outmblmdown) write.table(output.df, file="blm ratios and p-values map.csv", sep=",", col.names=colnames(output.df), row.names=FALSE) ctl.df <- data.frame(p.fa.outpblmup.c,p.fc.outpblmup.c,p.ft.outpblmup.c,p.l1.outpblmup.c,p.l2.outpblmup.c,p.l3.outpblmup.c,p.stacks.outpblmup.c,p.length.outpblmup.c, p.fa.outpblmdown.c,p.fc.outpblmdown.c,p.ft.outpblmdown.c,p.l1.outpblmdown.c,p.l2.outpblmdown.c,p.l3.outpblmdown.c,p.stacks.outpblmdown.c,p.length.outpblmdown.c, p.fa.outmblmup.c,p.fc.outmblmup.c,p.ft.outmblmup.c,p.l1.outmblmup.c,p.l2.outmblmup.c,p.l3.outmblmup.c,p.stacks.outmblmup.c,p.length.outmblmup.c, p.fa.outmblmdown.c,p.fc.outmblmdown.c,p.ft.outmblmdown.c,p.l1.outmblmdown.c,p.l2.outmblmdown.c,p.l3.outmblmdown.c,p.stacks.outmblmdown.c,p.length.outmblmdown.c) output2.df <- output.df[,seq(from=3,to=65, by=2)] sink("dataset stats and FDR.txt") for(i in 1:length(ctl.df)){ col <- ctl.df[,i] col2 <- sort(col, decreasing=FALSE) col2.fd <- col2[col2 < 0.01] av.fd <- length(col2.fd)/100 dat1 <- output2.df[,i] dat1.disc <- dat1[dat1 < 0.01] fdr <- av.fd/length(dat1.disc) print(names(output2.df)[i]) print("fd #") print(av.fd) print("disc #") print(length(dat1.disc)) print("fdr") print(fdr) print("") print("") } sink() plim <- NULL ylim <- NULL lwidth <- 4 type <- "l" pch1 <- 16 pch2 <- 17 col1 <- "red" col2 <- "blue" lty1 <- 1 lty2 <- 2 line <- 0.01 line.lty <- 3 caxis <- 1.5 clegend <- 1.5 clab <- 1.5 cmain <- 2.5 las <- 0 las2 <- 2 margin1 <- c(2,3,3,0) margin2 <- c(3,3,2,0) # bottom left top right pdf("features.pdf") par(oma=c(4,2,1,1)) par(mfrow=c(4,4)) # Fa p-value par(mar=margin1) plot(map.points, p.fa.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="Fa", log="y", ylim=c(1E-36,1),lty=lty1,pch=pch1, xaxt="n",yaxt="n", cex.main=cmain) abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=line, lty=3, lwd=lwidth, col="black") points(map.points, p.fa.outpblmup, col=col1, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, p.fa.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, p.fa.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, p.fa.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="Fa", cex=clegend, bty="n") box(lwd=2) # Fc p-value par(mar=margin1) plot(map.points, p.fc.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="Fc", log="y", ylim=c(1E-15,1),lty=lty1,pch=pch1, xaxt="n", yaxt="n", cex.main=cmain) abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=line, lty=3, lwd=lwidth, col="black") points(map.points, p.fc.outpblmup, col=col1, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, p.fc.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, p.fc.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, p.fc.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="Fc", cex=clegend, bty="n") box(lwd=2) # Ft p-value par(mar=margin1) plot(map.points, p.ft.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="Ft", log="y", ylim=c(1E-100,1),lty=lty1,pch=pch1, xaxt="n",yaxt="n", cex.main=cmain) abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=line, lty=3, lwd=lwidth, col="black") points(map.points, p.ft.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, p.ft.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, p.ft.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, p.ft.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="Ft", cex=clegend, bty="n") box(lwd=2) # stacks p-value par(mar=margin1) plot(map.points, p.stacks.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="Stacks", log="y", ylim=c(1E-11,1),lty=lty1,pch=pch1, xaxt="n",yaxt="n", cex.main=cmain) abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=line, lty=3, lwd=lwidth, col="black") points(map.points, p.stacks.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, p.stacks.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, p.stacks.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, p.stacks.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="Stacks", cex=clegend, bty="n") box(lwd=2) # Fa ratio par(mar=margin2) plot(map.points, fa.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="", ylim=c(0.75,1.2),lty=lty1,pch=pch1, xaxt="n", yaxt="n") abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=1, lty=1, lwd=lwidth, col="black") points(map.points, fa.outpblmup, col=col1, type=type, lwd=lwidth, ylim=ylim,lty=lty1,pch=pch1) points(map.points, fa.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, fa.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, fa.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="Fa", cex=clegend, bty="n") box(lwd=2) # Fc ratio par(mar=margin2) plot(map.points, fc.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="", ylim=c(0.75,1.2),lty=lty1,pch=pch1, xaxt="n",yaxt="n") abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=1, lty=1, lwd=lwidth, col="black") points(map.points, fc.outpblmup, col=col1, type=type, lwd=lwidth, ylim=ylim,lty=lty1,pch=pch1) points(map.points, fc.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, fc.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, fc.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="Fc", cex=clegend, bty="n") box(lwd=2) # Ft ratio par(mar=margin2) plot(map.points, ft.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="", ylim=c(0.75,1.2),lty=lty1,pch=pch1, xaxt="n",yaxt="n") abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=1, lty=1, lwd=lwidth, col="black") points(map.points, ft.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, ft.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, ft.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, ft.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="Ft", cex=clegend, bty="n") box(lwd=2) # stacks ratio par(mar=margin2) plot(map.points, stacks.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="", ylim=c(0.93,1.02),lty=lty1,pch=pch1, xaxt="n",yaxt="n") abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=1, lty=1, lwd=lwidth, col="black") points(map.points, stacks.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, stacks.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, stacks.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, stacks.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="Stacks", cex=clegend, bty="n") box(lwd=2) # L1 p-value par(mar=margin1) plot(map.points, p.l1.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="L1", log="y", ylim=c(1E-5,1),lty=lty1,pch=pch1, xaxt="n",yaxt="n", cex.main=cmain) abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=line, lty=3, lwd=lwidth, col="black") points(map.points, p.l1.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, p.l1.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, p.l1.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, p.l1.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="L1", cex=clegend, bty="n") box(lwd=2) # L2 p-value par(mar=margin1) plot(map.points, p.l2.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="L2", log="y", ylim=c(1E-5,1),lty=lty1,pch=pch1, xaxt="n",yaxt="n", cex.main=cmain) abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=line, lty=3, lwd=lwidth, col="black") points(map.points, p.l2.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, p.l2.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, p.l2.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, p.l2.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="L2", cex=clegend, bty="n") box(lwd=2) # L3 p-value par(mar=margin1) plot(map.points, p.l3.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="L3", log="y", ylim=c(1E-5,1),lty=lty1,pch=pch1, xaxt="n",yaxt="n", cex.main=cmain) abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=line, lty=3, lwd=lwidth, col="black") points(map.points, p.l3.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, p.l3.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, p.l3.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, p.l3.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="L3", cex=clegend, bty="n") box(lwd=2) # length p-value par(mar=margin1) plot(map.points, p.length.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="Length", log="y", ylim=c(1E-10,1),lty=lty1,pch=pch1, xaxt="n",yaxt="n",cex.main=cmain) abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=line, lty=3, lwd=lwidth, col="black") points(map.points, p.length.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, p.length.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, p.length.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, p.length.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) par(font=2) #legend("bottomleft", legend="Length", cex=clegend, bty="n") box(lwd=2) # L1 ratio par(mar=margin2) plot(map.points, l1.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="", ylim=c(0.8,1.2),lty=lty1,pch=pch1, xaxt="n",yaxt="n") abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=1, lty=1, lwd=lwidth, col="black") points(map.points, l1.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, l1.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, l1.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, l1.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) axis(side=1,at=seq(from=-1600, to=1600, by=800),font=2,cex.axis=caxis,las=las2) par(font=2) #legend("bottomleft", legend="L1", cex=clegend, bty="n") box(lwd=2) # L2 ratio par(mar=margin2) plot(map.points, l2.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="", ylim=c(0.8,1.2),lty=lty1,pch=pch1, xaxt="n",yaxt="n") abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=1, lty=1, lwd=lwidth, col="black") points(map.points, l2.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, l2.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, l2.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, l2.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) axis(side=1,at=seq(from=-1600, to=1600, by=800),font=2,cex.axis=caxis,las=las2) par(font=2) #legend("bottomleft", legend="L2", cex=clegend, bty="n") box(lwd=2) # L3 ratio par(mar=margin2) plot(map.points, l3.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="", ylim=c(0.8,1.2),lty=lty1,pch=pch1, xaxt="n",yaxt="n") abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=1, lty=1, lwd=lwidth, col="black") points(map.points, l3.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, l3.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, l3.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, l3.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) axis(side=1,at=seq(from=-1600, to=1600, by=800),font=2,cex.axis=caxis,las=las2) par(font=2) #legend("bottomleft", legend="L3", cex=clegend, bty="n") box(lwd=2) # length ratio par(mar=margin2) plot(map.points, length.outpblmup, col=col1, type=type, lwd=lwidth, xlab="", ylab="", main="", ylim=c(0.9,1.05),lty=lty1,pch=pch1, xaxt="n",yaxt="n") abline(v=seq(from=-2000,to=2000, by=200), col="azure3", lwd=1) abline(v=0, col="black") abline(h=1, lty=1, lwd=lwidth, col="black") points(map.points, length.outpblmup, col=col1, type=type, lwd=lwidth, lty=lty1,pch=pch1) points(map.points, length.outmblmup, col=col1, type=type, lwd=lwidth,lty=lty2,pch=pch2) points(map.points, length.outpblmdown, col=col2, type=type, lwd=lwidth,lty=lty1,pch=pch1) points(map.points, length.outmblmdown, col=col2, type=type, lwd=lwidth,lty=lty2,pch=pch2) axis(side=2,font=2,cex.axis=caxis,las=las) axis(side=1,at=seq(from=-1600, to=1600, by=800),font=2,cex.axis=caxis,las=las2) par(font=2) #legend("bottomleft", legend="Length", cex=clegend, bty="n") box(lwd=2) mtext(side=2, text=" Ratio P-Value Ratio P-Value", cex=clab, font=2, line=0, adj=0, outer=TRUE) mtext(side=1, text=" Distance Downstream of TSS (bp)", cex=clab, font=2, line=2.5, adj=0, outer=TRUE) dev.off() par(mfrow=c(1,1))