diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..26d3352 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/.idea/diy_protocols.iml b/.idea/diy_protocols.iml new file mode 100644 index 0000000..d0876a7 --- /dev/null +++ b/.idea/diy_protocols.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..a971a2c --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..e902a04 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/bacterial_genome.R b/bacterial_genome.R new file mode 100644 index 0000000..9039270 --- /dev/null +++ b/bacterial_genome.R @@ -0,0 +1,410 @@ +library(ggplot2) +library(stringr) +##### +#This part of scripts produces the plots of alignment data in a directory "../report/images_assembly/" with format ".png". Only genome-aligned reverse reads were taken here. +#For every sample was created a set of reads of equal capacity (min between all sample reads, estimated manually). +##### + +#Uploading data in R + +temp<-read.csv("custom_1_mapq_table.txt", sep=";", header=TRUE) +temp<-subset(temp, Read==2) +temp<-temp[sample(nrow(temp), 190000),] +temp<-cbind(temp, "Sample"=1) +table_set<-temp +samples<-c(2, 4:7, 9) +for (i in samples) { + filename<-paste("custom_", i, "_mapq_table.txt", sep='') + temp<-read.csv(filename, sep=";", header=TRUE) + temp<-subset(temp, Read==2) + temp<-temp[sample(nrow(temp), 190000),] + temp<-cbind(temp, "Sample"=i) + table_set<-rbind(table_set, temp) +} +rm(temp) +table_set$Sample<-as.factor(table_set$Sample) + +#GC-content + +ggplot(data = table_set, aes(x = GC)) + + geom_density(stat = "density", fill = "pink", alpha = 0.5) + + facet_wrap(~Sample, nrow = 3) + + geom_vline(xintercept = 50, linetype="dotted") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/gc.png", width = 2250, height = 1500, units = "px") + +#Mismatches per cycle of sequence + +df2<-data.frame("Cycles"=0, "Sample"=1) +for (i in c(1, samples)) { + cyc <-subset(table_set, Sample == i)[,4] + cyc <- as.numeric(unlist(strsplit(cyc,","))) + df2<-rbind(df2, cbind("Cycles" = cyc, "Sample" =i)) +} +df2<-df2[-1,] +df2$Sample<-as.factor(df2$Sample) +rm (cyc, i) + +ggplot(df2, aes(x = Cycles)) + + geom_density(stat = "count", fill = "blue", alpha = 0.5) + + #geom_line(stat = "count", fill = "blue", alpha = 0.5) + + facet_wrap(~Sample, nrow = 3) + + ggtitle("Mismatches per cycle") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/mpc.png", width = 2250, height = 1500, units = "px") +ggplot(df2, aes(x = Cycles)) + + geom_density(stat = "density", fill = "blue", alpha = 0.5) + + facet_wrap(~Sample, nrow = 3) + + ggtitle("Mismatches per cycle") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/mpc_density.png", width = 2250, height = 1500, units = "px") +rm (df2) + +#Mismatches depending on GC-content + +mgc2<-cbind(table_set[, c("Count_SNPs", "Sample")], "GC" = round(table_set$GC)) + +ggplot(mgc2, aes(x=GC, y=Count_SNPs)) + + stat_summary(fun = sum, geom="line", linewidth = 0.5) + + facet_wrap(~Sample, nrow = 3) + + ggtitle("Mismatches/GC") + + geom_vline(xintercept = 50, linetype="dotted") + + theme_bw()+ + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/mgc.png", width = 2250, height = 1500, units = "px") +ggplot(mgc2, aes(x=GC, y=Count_SNPs)) + + stat_summary(fun = mean, geom="line", size = 0.5) + + facet_wrap(~Sample, nrow = 3) + + ggtitle("Mismatches/GC") + + geom_vline(xintercept = 50, linetype="dotted") + + theme_bw()+ + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/mgc_mean.png", width = 2250, height = 1500, units = "px") + +rm (mgc2) + +#Frequency of different types of mismatches + +mtype2 <- apply(table_set[, c(12:23)][table_set$Sample==1,], 2, sum) +mtype2 <- data.frame("Sum" = mtype2) +mtype2$type<-as.factor(colnames(table_set[, c(12:23)])) +mtype2<-cbind(mtype2, "Sample" = 1) + +for (i in samples) { + temp <- apply(table_set[, c(12:23)][table_set$Sample==i,], 2, sum) + temp <- data.frame("Sum" = temp) + temp$type<-as.factor(colnames(table_set[, c(12:23)])) + temp<-cbind(temp, "Sample" = i) + mtype2<-rbind(mtype2, temp) +} +rm(temp) + +ggplot(mtype2) + + geom_line(aes(x = type, y = Sum, group = Sample)) + + geom_point(aes(x = type, y = Sum)) + + #facet_wrap(~Sample, nrow = 3, scales = "free_y") + + facet_wrap(~Sample, nrow = 3, ) + + ggtitle("Mismatch type") + + theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1)) +ggsave("../report/images_assembly/mt.png", width = 2250, height = 1500, units = "px") + +mtype2_2 <- apply(table_set[table_set$Sample==1, c(12:23)], 2, mean) +mtype2_2 <- data.frame("mean" = mtype2_2) +mtype2_2$type<-as.factor(colnames(table_set[, c(12:23)])) +mtype2_2<-cbind(mtype2_2, "Sample" = 1) +for (i in samples) { + temp <- apply(table_set[table_set$Sample==i, c(12:23)], 2, mean) + temp <- data.frame("mean" = temp) + temp$type<-as.factor(colnames(table_set[, c(12:23)])) + temp<-cbind(temp, "Sample" = i) + mtype2_2<-rbind(mtype2_2, temp) +} +mtype2_2$Sample<-as.factor(mtype2_2$Sample) + +ggplot(mtype2_2) + + geom_line(aes(x = type, y = mean, group = Sample)) + + geom_point(aes(x = type, y = mean)) + + #facet_wrap(~Sample, nrow = 3, scales = "free_y") + + facet_wrap(~Sample, nrow = 3) + + ggtitle("Mismatch type") + + theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1)) +ggsave("../report/images_assembly/mt_mean.png", width = 2250, height = 1500, units = "px") + +rm (mtype2, mtype2_2) + +#Number of deletions per sample + +delets2 <- table_set[table_set$Sample==1,][,10] +delets2 <- unlist(strsplit(delets2,"")) +delets2<- delets2[delets2 %in% c(letters, LETTERS)] +delets2<-cbind("Nucleotide"=delets2, "Sample" = 1) +delets2<-data.frame(delets2) +delets2$Nucleotide <- as.factor(delets2$Nucleotide) +for (i in samples) { + temp <- table_set[table_set$Sample==i,][,10] + temp <- unlist(strsplit(temp,"")) + temp<- temp[temp %in% c(letters, LETTERS)] + temp<-cbind("Nucleotide"=temp, "Sample" = i) + temp<-data.frame(temp) + temp$Nucleotide <- as.factor(temp$Nucleotide) + delets2<-rbind(temp, delets2) +} +delets2$Sample<-as.factor(delets2$Sample) +rm (temp) + +ggplot(delets2) + + geom_bar(aes(x = Nucleotide), width = 0.25, fill = "orange") + + facet_wrap(~Sample, nrow = 3, scales = "free_y") + + #ylim(0, 1500) + + ggtitle ("Deletions") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/delets.png", width = 2250, height = 1500, units = "px") + +rm (delets2) + +#Number of insertions per sample + +inserts2 <- table_set[table_set$Sample==1,][,11] +inserts2 <- unlist(strsplit(inserts2,"")) +inserts2<- inserts2[inserts2 %in% c(letters, LETTERS)] +inserts2<-cbind("Nucleotide"=inserts2, "Sample" = 1) +inserts2<-data.frame(inserts2) +inserts2$Nucleotide <- as.factor(inserts2$Nucleotide) +for (i in samples) { + temp <- table_set[table_set$Sample==i,][,11] + temp <- unlist(strsplit(temp,"")) + temp<- temp[temp %in% c(letters, LETTERS)] + temp<-cbind("Nucleotide"=temp, "Sample" = i) + temp<-data.frame(temp) + temp$Nucleotide <- as.factor(temp$Nucleotide) + inserts2<-rbind(temp, inserts2) +} +inserts2$Sample<-as.factor(inserts2$Sample) + +ggplot(inserts2) + + ggtitle("Insertions") + + geom_bar(aes(x = Nucleotide), width = 0.25, fill = "orange") + + facet_wrap(~Sample, nrow = 2, scales = "free_y") + + #ylim(0, 2000) + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/inserts.png", width = 2250, height = 1500, units = "px") + +rm (inserts2) + +#Mean, min and max values of insertions per sample + +insert_mean2<-subset(table_set, Sample == 1)[,7] +insert_mean2<-cbind("Sample"=1, t(summary(insert_mean2))) +for (i in samples) { + temp<-subset(table_set, Mean_length_insert!=0 & Sample == i)[,7] + temp<-cbind("Sample"=i, t(summary(temp))) + insert_mean2<-rbind(insert_mean2, temp) +} + +ggplot(data.frame(insert_mean2), aes(x = factor(Sample), y = Mean), size = 0.5) + + geom_pointrange(aes(ymin = 0, ymax = Max.)) + + #scale_y_continuous(breaks = c(0:12), limits = c(0, 13)) + + geom_point(aes(x = factor(Sample), y = 0), shape = 1) + + geom_point(aes(x = factor(Sample), y = Max.), shape = 1) + + ggtitle("Insert mean length") + + scale_x_discrete(name = "Sample") + + #scale_y_continuous(name = "Length", breaks = seq(0, 18, 1)) + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/iml.png", width = 2250, height = 1500, units = "px") + +rm (insert_mean2) + +#Mean, min and max values of deletions per sample + +delete_mean2<-subset(table_set, Sample == 1)[,6] +delete_mean2<-cbind("Sample"=1, t(summary(delete_mean2))) +for (i in samples) { + temp<-subset(table_set, Mean_length_del!=0 & Sample == i)[,6] + temp<-cbind("Sample"=i, t(summary(temp))) + delete_mean2<-rbind(delete_mean2, temp) +} +ggplot(data.frame(delete_mean2), aes(x = factor(Sample), y = Mean), size = 0.5) + + geom_pointrange(aes(ymin = 0, ymax = Max.)) + + #scale_y_continuous(breaks = c(0:22), limits = c(0, 22)) + + geom_point(aes(x = factor(Sample), y = 0), shape = 1) + + geom_point(aes(x = factor(Sample), y = Max.), shape = 1) + + ggtitle("Delete mean length") + + scale_x_discrete(name = "Sample") + + #scale_y_continuous(name = "Length", breaks = seq(0, 12, 1)) + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/dml.png", width = 2250, height = 1500, units = "px") + +rm (delete_mean2) + +#Number of mismatches per sample + +ggplot(table_set, aes(x=Sample, y=Count_SNPs, group = 1)) + + stat_summary(fun = sum, geom="bar", width = 0.7, fill = "white", color = "black", alpha = 0.7) + + ggtitle("Mismatches per sample") + + #geom_vline(xintercept = 50, linetype="dotted") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/mps_mean.png", width = 2250, height = 1500, units = "px") + + +ggplot(table_set, aes(x=Sample, y=Count_SNPs, group = 1)) + + stat_summary(fun = mean, geom="point") + + stat_summary(fun = mean, geom="line") + + ggtitle("Mismatches per sample") + + #geom_vline(xintercept = 50, linetype="dotted") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/mps.png", width = 2250, height = 1500, units = "px") + +#Number of deletions per cycle of sequence + +numbers_only <- function(x) !grepl("\\D", x) + +dels2 <- table_set[table_set$Sample==1,][,10] +dels2 <- unlist(strsplit(dels2, split="|",fixed = TRUE)) +dels2 <- unlist(strsplit(dels2,",")) +dels2<- dels2[numbers_only(dels2)] +dels2<-cbind("Num"=dels2, "Sample" = 1) +dels2<-data.frame(dels2) +for (i in samples) { + temp <- table_set[table_set$Sample==i,][,10] + temp <- unlist(strsplit(temp, split="|",fixed = TRUE)) + temp <- unlist(strsplit(temp,",")) + temp<- temp[numbers_only(temp)] + temp<-cbind("Num"=temp, "Sample" = i) + temp<-data.frame(temp) + dels2<-rbind(temp, dels2) +} +dels2$Num<-as.numeric(dels2$Num) +ggplot(dels2, aes(x = Num)) + + #geom_density(stat = "count", alpha = 0.5) + + stat_count(geom="line", position="identity") + + stat_count(geom="point", position="identity", size = 1) + + facet_wrap(~Sample, nrow = 3, scales = "free_y") + + #ylim(0, 1500) + + ggtitle ("Deletions per cycle") + + theme_bw()+ + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/dpc.png", width = 2250, height = 1500, units = "px") + +rm (dels2, temp) + +#Number of insertions per cycle of sequence + +ins2 <- table_set[table_set$Sample==1,][,11] +ins2 <- unlist(strsplit(ins2, split="|",fixed = TRUE)) +ins2 <- unlist(strsplit(ins2,",")) +ins2<- ins2[numbers_only(ins2)] +ins2<-cbind("Num"=ins2, "Sample" = 1) +ins2<-data.frame(ins2) +for (i in samples) { + temp <- table_set[table_set$Sample==i,][,11] + temp <- unlist(strsplit(temp, split="|",fixed = TRUE)) + temp <- unlist(strsplit(temp,",")) + temp<- temp[numbers_only(temp)] + temp<-cbind("Num"=temp, "Sample" = i) + temp<-data.frame(temp) + ins2<-rbind(temp, ins2) +} +ins2$Num<-as.numeric(ins2$Num) +ggplot(ins2, aes(x = Num)) + + stat_count(geom="line", position="identity") + + stat_count(geom="point", position="identity", size = 1) + + facet_wrap(~Sample, nrow = 3, scales = "free_y") + + #ylim(0, 1500) + + ggtitle ("Insertions per cycle") + + scale_x_continuous(breaks=seq(0, 150, 25))+ + theme_bw()+ + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/ipc.png", width = 2250, height = 1500, units = "px") +rm (ins2, temp) + +#Number of deletions per sample + +parse_delets2 <- table_set[table_set$Sample==1,][,10] +parse_delets2 <- unlist(strsplit(parse_delets2, split="|",fixed = TRUE)) +parse_delets2<-cbind("Num"=parse_delets2, "Sample" = 1) +parse_delets2<-data.frame(parse_delets2) +for (i in samples) { + temp <- table_set[table_set$Sample==i,][,10] + temp <- unlist(strsplit(temp,split="|",fixed = TRUE)) + temp<-cbind("Num"=temp, "Sample" = i) + temp<-data.frame(temp) + parse_delets2<-rbind(temp, parse_delets2) +} +ggplot(parse_delets2, aes(x=Sample, group = 1)) + + stat_count(geom="bar", width = 0.7, fill = "white", color = "black", alpha = 0.7) + + ggtitle("Deletions per sample") + + #geom_vline(xintercept = 50, linetype="dotted") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/dps.png", width = 2250, height = 1500, units = "px") +rm (parse_delets2, temp) + +#Number of insertions per sample + +parse_inserts2 <- table_set[table_set$Sample==1,][,11] +parse_inserts2 <- unlist(strsplit(parse_inserts2, split="|",fixed = TRUE)) +parse_inserts2<-cbind("Num"=parse_inserts2, "Sample" = 1) +parse_inserts2<-data.frame(parse_inserts2) +for (i in samples) { + temp <- table_set[table_set$Sample==i,][,11] + temp <- unlist(strsplit(temp,split="|",fixed = TRUE)) + temp<-cbind("Num"=temp, "Sample" = i) + temp<-data.frame(temp) + parse_inserts2<-rbind(temp, parse_inserts2) +} +ggplot(parse_inserts2, aes(x=Sample, group = 1)) + + stat_count(geom="bar", width = 0.7, fill = "white", color = "black", alpha = 0.7) + + ggtitle("Insertions per sample") + + #geom_vline(xintercept = 50, linetype="dotted") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_assembly/ips.png", width = 2250, height = 1500, units = "px") +rm (parse_inserts2, temp) + +#Frequency of insertion motifs per sample + +insert_motifs2 <- table_set[table_set$Sample==1,][,11] +insert_motifs2 <- unlist(strsplit(insert_motifs2, split="|",fixed = TRUE)) +insert_motifs2 <- unlist(strsplit(insert_motifs2,",")) +insert_motifs2 <- insert_motifs2[!numbers_only(insert_motifs2)] +insert_motifs2 <- cbind("Num"=insert_motifs2, "Sample" = 1) +insert_motifs2 <- data.frame(insert_motifs2) +for (i in samples) { + temp <- table_set[table_set$Sample==i,][,11] + temp <- unlist(strsplit(temp, split="|",fixed = TRUE)) + temp <- unlist(strsplit(temp,",")) + temp <- temp[!numbers_only(temp)] + temp <- cbind("Num"=temp, "Sample" = i) + temp <- data.frame(temp) + insert_motifs2 <- rbind(temp, insert_motifs2) +} +ggplot(insert_motifs2, aes(x = Num)) + + stat_count(geom='bar', width = 0.7, fill = "white", color = "black", alpha = 0.7) + + facet_wrap(~Sample, nrow=4, scales = "free_y") + + ggtitle("Insertion motifs per sample") + + theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1)) +ggsave("../report/images_assembly/imps.png", width = 2250, height = 2500, units = "px") +rm (temp, insert_motifs2) + +#Frequency of deletion motifs per sample + +delete_motifs2 <- table_set[table_set$Sample==1,][,10] +delete_motifs2 <- unlist(strsplit(delete_motifs2, split="|",fixed = TRUE)) +delete_motifs2 <- unlist(strsplit(delete_motifs2,",")) +delete_motifs2 <- delete_motifs2[!numbers_only(delete_motifs2)] +delete_motifs2 <- cbind("Num"=delete_motifs2, "Sample" = 1) +delete_motifs2 <- data.frame(delete_motifs2) +for (i in samples) { + temp <- table_set[table_set$Sample==i,][,10] + temp <- unlist(strsplit(temp, split="|",fixed = TRUE)) + temp <- unlist(strsplit(temp,",")) + temp <- temp[!numbers_only(temp)] + temp <- cbind("Num"=temp, "Sample" = i) + temp <- data.frame(temp) + delete_motifs2 <- rbind(temp, delete_motifs2) +} +ggplot(delete_motifs2, aes(x = Num)) + + stat_count(geom='bar', width = 0.7, fill = "white", color = "black", alpha = 0.7) + + facet_wrap(~Sample, nrow=7, scales = "free_y") + + ggtitle("Deletions motifs per sample") + + theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1)) +ggsave("../report/images_assembly/dmps.png", width = 2250, height = 3500, units = "px") +rm (delete_motifs2, temp) \ No newline at end of file diff --git a/plasmid.R b/plasmid.R new file mode 100644 index 0000000..e8b68e2 --- /dev/null +++ b/plasmid.R @@ -0,0 +1,403 @@ +library(ggplot2) +library(stringr) +##### +#This part of scripts produces the plots of alignment data. Only plasmid-aligned reverse reads were taken here. +#For every sample was created a set of reads of equal capacity (min between all sample reads). +##### + +#Uploading data in R + +t1<-read.csv("../custom_1_90_mapq.txt", sep=";") +t2<-read.csv("../custom_2_87_mapq.txt", sep=";") +t4<-read.csv("../custom_4_96_mapq.txt", sep=";") +t5<-read.csv("../custom_5_92_mapq.txt", sep=";") +t6<-read.csv("../custom_6_108_mapq.txt", sep=";") +t7<-read.csv("../custom_7_97_mapq.txt", sep=";") +tpol<-read.csv("../custom_pol_93_mapq.txt", sep=";") + +tp<-cbind(t1, "Sample" =1) +tp<-rbind(tp, cbind(t2, "Sample" =2)) +tp<-rbind(tp, cbind(t4, "Sample" =4)) +tp<-rbind(tp, cbind(t5, "Sample" =5)) +tp<-rbind(tp, cbind(t6, "Sample" =6)) +tp<-rbind(tp, cbind(t7, "Sample" =7)) +tp<-rbind(tp, cbind(tpol, "Sample" =9)) + +samples<-c(2, 4:7, 9) + +sample2<-subset(tp, Read==2&Sample==1) +tp2<-sample2[sample(nrow(sample2), 20000),] +for (i in samples) { + sample2<-subset(tp, Read==2&Sample==i) + tp2<-rbind(tp2, sample2[sample(nrow(sample2), 20000),]) +} +rm(sample2, t1, t2, t4, t5, t6, t7, tpol) + +#GC-content + +ggplot(data = tp2, aes(x = GC)) + + geom_density(stat = "density", fill = "pink", alpha = 0.5) + + facet_wrap(~Sample, nrow = 3) + + geom_vline(xintercept = 50, linetype="dotted") + + xlim(25, 75) + + ylim(0, 0.15) + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/gc.png", width = 2250, height = 1500, units = "px") + +#Mismatches per cycle of sequence + +df2<-data.frame("Cycles"=0, "Sample"=1) +for (i in c(1:2, 4:7, 9)) { + cyc <-subset(tp2, Sample == i)[,4] + cyc <- as.numeric(unlist(strsplit(cyc,","))) + df2<-rbind(df2, cbind("Cycles" = cyc, "Sample" =i)) +} +df2<-df2[-1,] +df2$Sample<-as.factor(df2$Sample) +rm (cyc, i) +ggplot(df2, aes(x = Cycles)) + + geom_density(stat = "count", fill = "blue", alpha = 0.5) + + #geom_line(stat = "count", fill = "blue", alpha = 0.5) + + facet_wrap(~Sample, nrow = 3) + + ggtitle("Mismatches per cycle") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/mpc.png", width = 2250, height = 1500, units = "px") +ggplot(df2, aes(x = Cycles)) + + geom_density(stat = "density", fill = "blue", alpha = 0.5) + + facet_wrap(~Sample, nrow = 3) + + ggtitle("Mismatches per cycle") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/mpc_density.png", width = 2250, height = 1500, units = "px") +rm (df2) + +#Mismatches depending on GC-content + +mgc2<-cbind(tp2[, c("Count_SNPs", "Sample")], "GC" = round(tp2$GC)) +ggplot(mgc2, aes(x=GC, y=Count_SNPs)) + + stat_summary(fun = sum, geom="line", linewidth = 0.5) + + facet_wrap(~Sample, nrow = 3) + + ggtitle("Mismatches/GC") + + geom_vline(xintercept = 50, linetype="dotted") + + theme_bw()+ + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/mgc.png", width = 2250, height = 1500, units = "px") +ggplot(mgc2, aes(x=GC, y=Count_SNPs)) + + stat_summary(fun = mean, geom="line", size = 0.5) + + facet_wrap(~Sample, nrow = 3) + + ggtitle("Mismatches/GC") + + geom_vline(xintercept = 50, linetype="dotted") + + theme_bw()+ + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/mgc_mean.png", width = 2250, height = 1500, units = "px") +rm (mgc2) + +#Frequency of different types of mismatches + +mtype2 <- apply(tp2[, c(12:23)][tp2$Sample==1,], 2, sum) +mtype2 <- data.frame("Sum" = mtype2) +mtype2$type<-as.factor(colnames(tp2[, c(12:23)])) +mtype2<-cbind(mtype2, "Sample" = 1) +for (i in samples) { + temp <- apply(tp2[, c(12:23)][tp2$Sample==i,], 2, sum) + temp <- data.frame("Sum" = temp) + temp$type<-as.factor(colnames(tp2[, c(12:23)])) + temp<-cbind(temp, "Sample" = i) + mtype2<-rbind(mtype2, temp) +} +ggplot(mtype2) + + geom_line(aes(x = type, y = Sum, group = Sample)) + + geom_point(aes(x = type, y = Sum)) + + #facet_wrap(~Sample, nrow = 3, scales = "free_y") + + facet_wrap(~Sample, nrow = 3, ) + + ggtitle("Mismatch type") + + theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1)) +ggsave("../report/images_read2/mt.png", width = 2250, height = 1500, units = "px") + + +mtype2_2 <- apply(tp2[tp2$Sample==1, c(12:23)], 2, mean) +mtype2_2 <- data.frame("mean" = mtype2_2) +mtype2_2$type<-as.factor(colnames(tp2[, c(12:23)])) +mtype2_2<-cbind(mtype2_2, "Sample" = 1) +for (i in samples) { + temp <- apply(tp2[tp2$Sample==i, c(12:23)], 2, mean) + temp <- data.frame("mean" = temp) + temp$type<-as.factor(colnames(tp2[, c(12:23)])) + temp<-cbind(temp, "Sample" = i) + mtype2_2<-rbind(mtype2_2, temp) +} +ggplot(mtype2_2) + + geom_line(aes(x = type, y = mean, group = Sample)) + + geom_point(aes(x = type, y = mean)) + + #facet_wrap(~Sample, nrow = 3, scales = "free_y") + + facet_wrap(~Sample, nrow = 3) + + ggtitle("Mismatch type") + + theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1)) +ggsave("../report/images_read2/mt_mean.png", width = 2250, height = 1500, units = "px") +rm(mtype2, mtype2_2) + +#Number of deletions per sample + +delets2 <- tp2[tp2$Sample==1,][,10] +delets2 <- unlist(strsplit(delets2,"")) +delets2<- delets2[delets2 %in% c(letters, LETTERS)] +delets2<-cbind("Nucleotide"=delets2, "Sample" = 1) +delets2<-data.frame(delets2) +delets2$Nucleotide <- as.factor(delets2$Nucleotide) +for (i in samples) { + temp <- tp2[tp2$Sample==i,][,10] + temp <- unlist(strsplit(temp,"")) + temp<- temp[temp %in% c(letters, LETTERS)] + temp<-cbind("Nucleotide"=temp, "Sample" = i) + temp<-data.frame(temp) + temp$Nucleotide <- as.factor(temp$Nucleotide) + delets2<-rbind(temp, delets2) +} +ggplot(delets2) + + geom_bar(aes(x = Nucleotide), width = 0.25, fill = "orange") + + facet_wrap(~Sample, nrow = 3, scales = "free_y") + + #ylim(0, 1500) + + ggtitle ("Deletes") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/delets.png", width = 2250, height = 1500, units = "px") +rm (delets2) + +#Number of insertions per sample + +inserts2 <- tp2[tp2$Sample==1,][,11] +inserts2 <- unlist(strsplit(inserts2,"")) +inserts2<- inserts2[inserts2 %in% c(letters, LETTERS)] +inserts2<-cbind("Nucleotide"=inserts2, "Sample" = 1) +inserts2<-data.frame(inserts2) +inserts2$Nucleotide <- as.factor(inserts2$Nucleotide) +for (i in samples) { + temp <- tp2[tp2$Sample==i,][,11] + temp <- unlist(strsplit(temp,"")) + temp<- temp[temp %in% c(letters, LETTERS)] + temp<-cbind("Nucleotide"=temp, "Sample" = i) + temp<-data.frame(temp) + temp$Nucleotide <- as.factor(temp$Nucleotide) + inserts2<-rbind(temp, inserts2) +} +ggplot(inserts2) + + ggtitle("Inserts") + + geom_bar(aes(x = Nucleotide), width = 0.25, fill = "orange") + + facet_wrap(~Sample, nrow = 2, scales = "free_y") + + #ylim(0, 2000) + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/inserts.png", width = 2250, height = 1500, units = "px") +rm (inserts2) + +#Mean, min and max values of insertions per sample + +insert_mean2<-subset(tp2, Sample == 1)[,7] +insert_mean2<-cbind("Sample"=1, t(summary(insert_mean2))) +for (i in samples) { + temp<-subset(tp2, Mean_length_insert!=0 & Sample == i)[,7] + temp<-cbind("Sample"=i, t(summary(temp))) + insert_mean2<-rbind(insert_mean2, temp) +} +ggplot(data.frame(insert_mean2), aes(x = factor(Sample), y = Mean), size = 0.5) + + geom_pointrange(aes(ymin = 0, ymax = Max.)) + + scale_y_continuous(breaks = c(0:12), limits = c(0, 13)) + + geom_point(aes(x = factor(Sample), y = 0), shape = 1) + + geom_point(aes(x = factor(Sample), y = Max.), shape = 1) + + ggtitle("Insert mean length") + + scale_x_discrete(name = "Sample") + + scale_y_continuous(name = "Length", breaks = seq(0, 18, 1)) + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/iml.png", width = 2250, height = 1500, units = "px") +rm (insert_mean2) + +#Mean, min and max values of deletions per sample + +delete_mean2<-subset(tp2, Sample == 1)[,6] +delete_mean2<-cbind("Sample"=1, t(summary(delete_mean2))) +for (i in samples) { + temp<-subset(tp2, Mean_length_del!=0 & Sample == i)[,6] + temp<-cbind("Sample"=i, t(summary(temp))) + delete_mean2<-rbind(delete_mean2, temp) +} +ggplot(data.frame(delete_mean2), aes(x = factor(Sample), y = Mean), size = 0.5) + + geom_pointrange(aes(ymin = 0, ymax = Max.)) + + scale_y_continuous(breaks = c(0:22), limits = c(0, 22)) + + geom_point(aes(x = factor(Sample), y = 0), shape = 1) + + geom_point(aes(x = factor(Sample), y = Max.), shape = 1) + + ggtitle("Delete mean length") + + scale_x_discrete(name = "Sample") + + scale_y_continuous(name = "Length", breaks = seq(0, 12, 1)) + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/dml.png", width = 2250, height = 1500, units = "px") +rm(delete_mean2) + +#Number of mismatches per sample + +ggplot(tp2, aes(x=Sample, y=Count_SNPs, group = 1)) + + stat_summary(fun = mean, geom="bar", width = 0.7, fill = "white", color = "black", alpha = 0.7) + + ggtitle("Mismatches per sample") + + #geom_vline(xintercept = 50, linetype="dotted") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/mps_mean.png", width = 2250, height = 1500, units = "px") + + +ggplot(tp2, aes(x=Sample, y=Count_SNPs, group = 1)) + + stat_summary(fun = sum, geom="point") + + stat_summary(fun = sum, geom="line") + + ggtitle("Mismatches per sample") + + #geom_vline(xintercept = 50, linetype="dotted") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/mps.png", width = 2250, height = 1500, units = "px") + +#Number of deletions per cycle of sequence + +numbers_only <- function(x) !grepl("\\D", x) + +dels2 <- tp2[tp2$Sample==1,][,10] +dels2 <- unlist(strsplit(dels2, split="|",fixed = TRUE)) +dels2 <- unlist(strsplit(dels2,",")) +dels2<- dels2[numbers_only(dels2)] +dels2<-cbind("Num"=dels2, "Sample" = 1) +dels2<-data.frame(dels2) +for (i in samples) { + temp <- tp2[tp2$Sample==i,][,10] + temp <- unlist(strsplit(temp, split="|",fixed = TRUE)) + temp <- unlist(strsplit(temp,",")) + temp<- temp[numbers_only(temp)] + temp<-cbind("Num"=temp, "Sample" = i) + temp<-data.frame(temp) + dels2<-rbind(temp, dels2) +} +dels2$Num<-as.numeric(dels2$Num) +ggplot(dels2, aes(x = Num)) + + #geom_density(stat = "count", alpha = 0.5) + + stat_count(geom="line", position="identity") + + stat_count(geom="point", position="identity", size = 1) + + facet_wrap(~Sample, nrow = 3, scales = "free_y") + + #ylim(0, 1500) + + ggtitle ("Deletes per cycle") + + theme_bw()+ + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/dpc.png", width = 2250, height = 1500, units = "px") +rm(dels2) + +#Number of insertions per cycle of sequence + +ins2 <- tp2[tp2$Sample==1,][,11] +ins2 <- unlist(strsplit(ins2, split="|",fixed = TRUE)) +ins2 <- unlist(strsplit(ins2,",")) +ins2<- ins2[numbers_only(ins2)] +ins2<-cbind("Num"=ins2, "Sample" = 1) +ins2<-data.frame(ins2) +for (i in samples) { + temp <- tp2[tp2$Sample==i,][,11] + temp <- unlist(strsplit(temp, split="|",fixed = TRUE)) + temp <- unlist(strsplit(temp,",")) + temp<- temp[numbers_only(temp)] + temp<-cbind("Num"=temp, "Sample" = i) + temp<-data.frame(temp) + ins2<-rbind(temp, ins2) +} +ins2$Num<-as.numeric(ins2$Num) +ggplot(ins2, aes(x = Num)) + + stat_count(geom="line", position="identity") + + stat_count(geom="point", position="identity", size = 1) + + facet_wrap(~Sample, nrow = 3, scales = "free_y") + + #ylim(0, 1500) + + ggtitle ("Inserts per cycle") + + scale_x_continuous(breaks=seq(0, 150, 25))+ + scale_y_continuous(drop=FALSE) + theme_bw()+ + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/ipc.png", width = 2250, height = 1500, units = "px") +rm (ins2) + +#Number of deletions per sample + +parse_delets2 <- tp2[tp2$Sample==1,][,10] +parse_delets2 <- unlist(strsplit(parse_delets2, split="|",fixed = TRUE)) +parse_delets2<-cbind("Num"=parse_delets2, "Sample" = 1) +parse_delets2<-data.frame(parse_delets2) +for (i in samples) { + temp <- tp2[tp2$Sample==i,][,10] + temp <- unlist(strsplit(temp,split="|",fixed = TRUE)) + temp<-cbind("Num"=temp, "Sample" = i) + temp<-data.frame(temp) + parse_delets2<-rbind(temp, parse_delets2) +} +ggplot(parse_delets2, aes(x=Sample, group = 1)) + + stat_count(geom="bar", width = 0.7, fill = "white", color = "black", alpha = 0.7) + + ggtitle("Deletes per sample") + + #geom_vline(xintercept = 50, linetype="dotted") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/dps.png", width = 2250, height = 1500, units = "px") +rm (parse_delets2) + +#Number of insertions per sample + +parse_inserts2 <- tp2[tp2$Sample==1,][,11] +parse_inserts2 <- unlist(strsplit(parse_inserts2, split="|",fixed = TRUE)) +parse_inserts2<-cbind("Num"=parse_inserts2, "Sample" = 1) +parse_inserts2<-data.frame(parse_inserts2) +for (i in samples) { + temp <- tp2[tp2$Sample==i,][,11] + temp <- unlist(strsplit(temp,split="|",fixed = TRUE)) + temp<-cbind("Num"=temp, "Sample" = i) + temp<-data.frame(temp) + parse_inserts2<-rbind(temp, parse_inserts2) +} +ggplot(parse_inserts2, aes(x=Sample, group = 1)) + + stat_count(geom="bar", width = 0.7, fill = "white", color = "black", alpha = 0.7) + + ggtitle("Inserts per sample") + + #geom_vline(xintercept = 50, linetype="dotted") + + theme(plot.title = element_text(hjust = 0.5)) +ggsave("../report/images_read2/ips.png", width = 2250, height = 1500, units = "px") +rm (parse_inserts2, temp) + +#Frequency of insertion motifs per sample + +insert_motifs2 <- tp2[tp2$Sample==1,][,11] +insert_motifs2 <- unlist(strsplit(insert_motifs2, split="|",fixed = TRUE)) +insert_motifs2 <- unlist(strsplit(insert_motifs2,",")) +insert_motifs2<- insert_motifs2[!numbers_only(insert_motifs2)] +insert_motifs2<-cbind("Num"=insert_motifs2, "Sample" = 1) +insert_motifs2<-data.frame(insert_motifs2) +for (i in samples) { + temp <- tp2[tp2$Sample==i,][,11] + temp <- unlist(strsplit(temp, split="|",fixed = TRUE)) + temp <- unlist(strsplit(temp,",")) + temp<- temp[!numbers_only(temp)] + temp<-cbind("Num"=temp, "Sample" = i) + temp<-data.frame(temp) + insert_motifs2<-rbind(temp, insert_motifs2) +} +ggplot(insert_motifs2, aes(x = Num)) + + stat_count(geom='bar', width = 0.7, fill = "white", color = "black", alpha = 0.7) + + facet_wrap(~Sample, nrow=4, scales = "free_y") + + ggtitle("Insert motifs per sample") + + theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1)) +ggsave("../report/images_read2/imps.png", width = 2250, height = 2500, units = "px") +rm (insert_motifs2, temp) + +#Frequency of deletion motifs per sample + +delete_motifs2 <- tp2[tp2$Sample==1,][,10] +delete_motifs2 <- unlist(strsplit(delete_motifs2, split="|",fixed = TRUE)) +delete_motifs2 <- unlist(strsplit(delete_motifs2,",")) +delete_motifs2<- delete_motifs2[!numbers_only(delete_motifs2)] +delete_motifs2<-cbind("Num"=delete_motifs2, "Sample" = 1) +delete_motifs2<-data.frame(delete_motifs2) +for (i in samples) { + temp <- tp2[tp2$Sample==i,][,10] + temp <- unlist(strsplit(temp, split="|",fixed = TRUE)) + temp <- unlist(strsplit(temp,",")) + temp<- temp[!numbers_only(temp)] + temp<-cbind("Num"=temp, "Sample" = i) + temp<-data.frame(temp) + delete_motifs2<-rbind(temp, delete_motifs2) +} +ggplot(delete_motifs2, aes(x = Num)) + + stat_count(geom='bar', width = 0.7, fill = "white", color = "black", alpha = 0.7) + + facet_wrap(~Sample, nrow=4, scales = "free_y") + + ggtitle("Deletes motifs per sample") + + theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1)) +ggsave("../report/images_read2/dmps.png", width = 2250, height = 2500, units = "px") +rm (delete_motifs2, temp) \ No newline at end of file