410 lines
15 KiB
R
410 lines
15 KiB
R
library(ggplot2)
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library(stringr)
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#####
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#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.
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#For every sample was created a set of reads of equal capacity (min between all sample reads, estimated manually).
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#####
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#Uploading data in R
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temp<-read.csv("custom_1_mapq_table.txt", sep=";", header=TRUE)
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temp<-subset(temp, Read==2)
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temp<-temp[sample(nrow(temp), 190000),]
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temp<-cbind(temp, "Sample"=1)
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table_set<-temp
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samples<-c(2, 4:7, 9)
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for (i in samples) {
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filename<-paste("custom_", i, "_mapq_table.txt", sep='')
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temp<-read.csv(filename, sep=";", header=TRUE)
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temp<-subset(temp, Read==2)
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temp<-temp[sample(nrow(temp), 190000),]
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temp<-cbind(temp, "Sample"=i)
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table_set<-rbind(table_set, temp)
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}
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rm(temp)
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table_set$Sample<-as.factor(table_set$Sample)
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#GC-content
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ggplot(data = table_set, aes(x = GC)) +
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geom_density(stat = "density", fill = "pink", alpha = 0.5) +
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facet_wrap(~Sample, nrow = 3) +
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geom_vline(xintercept = 50, linetype="dotted") +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/gc.png", width = 2250, height = 1500, units = "px")
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#Mismatches per cycle of sequence
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df2<-data.frame("Cycles"=0, "Sample"=1)
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for (i in c(1, samples)) {
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cyc <-subset(table_set, Sample == i)[,4]
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cyc <- as.numeric(unlist(strsplit(cyc,",")))
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df2<-rbind(df2, cbind("Cycles" = cyc, "Sample" =i))
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}
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df2<-df2[-1,]
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df2$Sample<-as.factor(df2$Sample)
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rm (cyc, i)
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ggplot(df2, aes(x = Cycles)) +
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geom_density(stat = "count", fill = "blue", alpha = 0.5) +
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#geom_line(stat = "count", fill = "blue", alpha = 0.5) +
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facet_wrap(~Sample, nrow = 3) +
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ggtitle("Mismatches per cycle") +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/mpc.png", width = 2250, height = 1500, units = "px")
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ggplot(df2, aes(x = Cycles)) +
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geom_density(stat = "density", fill = "blue", alpha = 0.5) +
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facet_wrap(~Sample, nrow = 3) +
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ggtitle("Mismatches per cycle") +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/mpc_density.png", width = 2250, height = 1500, units = "px")
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rm (df2)
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#Mismatches depending on GC-content
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mgc2<-cbind(table_set[, c("Count_SNPs", "Sample")], "GC" = round(table_set$GC))
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ggplot(mgc2, aes(x=GC, y=Count_SNPs)) +
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stat_summary(fun = sum, geom="line", linewidth = 0.5) +
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facet_wrap(~Sample, nrow = 3) +
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ggtitle("Mismatches/GC") +
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geom_vline(xintercept = 50, linetype="dotted") +
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theme_bw()+
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/mgc.png", width = 2250, height = 1500, units = "px")
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ggplot(mgc2, aes(x=GC, y=Count_SNPs)) +
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stat_summary(fun = mean, geom="line", size = 0.5) +
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facet_wrap(~Sample, nrow = 3) +
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ggtitle("Mismatches/GC") +
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geom_vline(xintercept = 50, linetype="dotted") +
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theme_bw()+
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/mgc_mean.png", width = 2250, height = 1500, units = "px")
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rm (mgc2)
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#Frequency of different types of mismatches
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mtype2 <- apply(table_set[, c(12:23)][table_set$Sample==1,], 2, sum)
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mtype2 <- data.frame("Sum" = mtype2)
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mtype2$type<-as.factor(colnames(table_set[, c(12:23)]))
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mtype2<-cbind(mtype2, "Sample" = 1)
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for (i in samples) {
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temp <- apply(table_set[, c(12:23)][table_set$Sample==i,], 2, sum)
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temp <- data.frame("Sum" = temp)
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temp$type<-as.factor(colnames(table_set[, c(12:23)]))
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temp<-cbind(temp, "Sample" = i)
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mtype2<-rbind(mtype2, temp)
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}
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rm(temp)
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ggplot(mtype2) +
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geom_line(aes(x = type, y = Sum, group = Sample)) +
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geom_point(aes(x = type, y = Sum)) +
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#facet_wrap(~Sample, nrow = 3, scales = "free_y") +
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facet_wrap(~Sample, nrow = 3, ) +
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ggtitle("Mismatch type") +
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theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1))
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ggsave("../report/images_assembly/mt.png", width = 2250, height = 1500, units = "px")
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mtype2_2 <- apply(table_set[table_set$Sample==1, c(12:23)], 2, mean)
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mtype2_2 <- data.frame("mean" = mtype2_2)
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mtype2_2$type<-as.factor(colnames(table_set[, c(12:23)]))
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mtype2_2<-cbind(mtype2_2, "Sample" = 1)
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for (i in samples) {
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temp <- apply(table_set[table_set$Sample==i, c(12:23)], 2, mean)
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temp <- data.frame("mean" = temp)
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temp$type<-as.factor(colnames(table_set[, c(12:23)]))
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temp<-cbind(temp, "Sample" = i)
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mtype2_2<-rbind(mtype2_2, temp)
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}
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mtype2_2$Sample<-as.factor(mtype2_2$Sample)
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ggplot(mtype2_2) +
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geom_line(aes(x = type, y = mean, group = Sample)) +
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geom_point(aes(x = type, y = mean)) +
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#facet_wrap(~Sample, nrow = 3, scales = "free_y") +
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facet_wrap(~Sample, nrow = 3) +
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ggtitle("Mismatch type") +
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theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1))
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ggsave("../report/images_assembly/mt_mean.png", width = 2250, height = 1500, units = "px")
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rm (mtype2, mtype2_2)
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#Number of deletions per sample
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delets2 <- table_set[table_set$Sample==1,][,10]
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delets2 <- unlist(strsplit(delets2,""))
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delets2<- delets2[delets2 %in% c(letters, LETTERS)]
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delets2<-cbind("Nucleotide"=delets2, "Sample" = 1)
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delets2<-data.frame(delets2)
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delets2$Nucleotide <- as.factor(delets2$Nucleotide)
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for (i in samples) {
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temp <- table_set[table_set$Sample==i,][,10]
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temp <- unlist(strsplit(temp,""))
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temp<- temp[temp %in% c(letters, LETTERS)]
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temp<-cbind("Nucleotide"=temp, "Sample" = i)
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temp<-data.frame(temp)
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temp$Nucleotide <- as.factor(temp$Nucleotide)
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delets2<-rbind(temp, delets2)
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}
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delets2$Sample<-as.factor(delets2$Sample)
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rm (temp)
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ggplot(delets2) +
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geom_bar(aes(x = Nucleotide), width = 0.25, fill = "orange") +
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facet_wrap(~Sample, nrow = 3, scales = "free_y") +
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#ylim(0, 1500) +
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ggtitle ("Deletions") +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/delets.png", width = 2250, height = 1500, units = "px")
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rm (delets2)
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#Number of insertions per sample
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inserts2 <- table_set[table_set$Sample==1,][,11]
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inserts2 <- unlist(strsplit(inserts2,""))
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inserts2<- inserts2[inserts2 %in% c(letters, LETTERS)]
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inserts2<-cbind("Nucleotide"=inserts2, "Sample" = 1)
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inserts2<-data.frame(inserts2)
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inserts2$Nucleotide <- as.factor(inserts2$Nucleotide)
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for (i in samples) {
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temp <- table_set[table_set$Sample==i,][,11]
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temp <- unlist(strsplit(temp,""))
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temp<- temp[temp %in% c(letters, LETTERS)]
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temp<-cbind("Nucleotide"=temp, "Sample" = i)
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temp<-data.frame(temp)
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temp$Nucleotide <- as.factor(temp$Nucleotide)
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inserts2<-rbind(temp, inserts2)
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}
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inserts2$Sample<-as.factor(inserts2$Sample)
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ggplot(inserts2) +
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ggtitle("Insertions") +
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geom_bar(aes(x = Nucleotide), width = 0.25, fill = "orange") +
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facet_wrap(~Sample, nrow = 2, scales = "free_y") +
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#ylim(0, 2000) +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/inserts.png", width = 2250, height = 1500, units = "px")
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rm (inserts2)
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#Mean, min and max values of insertions per sample
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insert_mean2<-subset(table_set, Sample == 1)[,7]
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insert_mean2<-cbind("Sample"=1, t(summary(insert_mean2)))
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for (i in samples) {
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temp<-subset(table_set, Mean_length_insert!=0 & Sample == i)[,7]
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temp<-cbind("Sample"=i, t(summary(temp)))
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insert_mean2<-rbind(insert_mean2, temp)
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}
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ggplot(data.frame(insert_mean2), aes(x = factor(Sample), y = Mean), size = 0.5) +
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geom_pointrange(aes(ymin = 0, ymax = Max.)) +
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#scale_y_continuous(breaks = c(0:12), limits = c(0, 13)) +
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geom_point(aes(x = factor(Sample), y = 0), shape = 1) +
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geom_point(aes(x = factor(Sample), y = Max.), shape = 1) +
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ggtitle("Insert mean length") +
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scale_x_discrete(name = "Sample") +
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#scale_y_continuous(name = "Length", breaks = seq(0, 18, 1)) +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/iml.png", width = 2250, height = 1500, units = "px")
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rm (insert_mean2)
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#Mean, min and max values of deletions per sample
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delete_mean2<-subset(table_set, Sample == 1)[,6]
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delete_mean2<-cbind("Sample"=1, t(summary(delete_mean2)))
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for (i in samples) {
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temp<-subset(table_set, Mean_length_del!=0 & Sample == i)[,6]
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temp<-cbind("Sample"=i, t(summary(temp)))
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delete_mean2<-rbind(delete_mean2, temp)
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}
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ggplot(data.frame(delete_mean2), aes(x = factor(Sample), y = Mean), size = 0.5) +
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geom_pointrange(aes(ymin = 0, ymax = Max.)) +
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#scale_y_continuous(breaks = c(0:22), limits = c(0, 22)) +
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geom_point(aes(x = factor(Sample), y = 0), shape = 1) +
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geom_point(aes(x = factor(Sample), y = Max.), shape = 1) +
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ggtitle("Delete mean length") +
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scale_x_discrete(name = "Sample") +
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#scale_y_continuous(name = "Length", breaks = seq(0, 12, 1)) +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/dml.png", width = 2250, height = 1500, units = "px")
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rm (delete_mean2)
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#Number of mismatches per sample
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ggplot(table_set, aes(x=Sample, y=Count_SNPs, group = 1)) +
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stat_summary(fun = sum, geom="bar", width = 0.7, fill = "white", color = "black", alpha = 0.7) +
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ggtitle("Mismatches per sample") +
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#geom_vline(xintercept = 50, linetype="dotted") +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/mps_mean.png", width = 2250, height = 1500, units = "px")
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ggplot(table_set, aes(x=Sample, y=Count_SNPs, group = 1)) +
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stat_summary(fun = mean, geom="point") +
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stat_summary(fun = mean, geom="line") +
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ggtitle("Mismatches per sample") +
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#geom_vline(xintercept = 50, linetype="dotted") +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/mps.png", width = 2250, height = 1500, units = "px")
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#Number of deletions per cycle of sequence
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numbers_only <- function(x) !grepl("\\D", x)
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dels2 <- table_set[table_set$Sample==1,][,10]
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dels2 <- unlist(strsplit(dels2, split="|",fixed = TRUE))
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dels2 <- unlist(strsplit(dels2,","))
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dels2<- dels2[numbers_only(dels2)]
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dels2<-cbind("Num"=dels2, "Sample" = 1)
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dels2<-data.frame(dels2)
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for (i in samples) {
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temp <- table_set[table_set$Sample==i,][,10]
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temp <- unlist(strsplit(temp, split="|",fixed = TRUE))
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temp <- unlist(strsplit(temp,","))
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temp<- temp[numbers_only(temp)]
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temp<-cbind("Num"=temp, "Sample" = i)
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temp<-data.frame(temp)
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dels2<-rbind(temp, dels2)
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}
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dels2$Num<-as.numeric(dels2$Num)
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ggplot(dels2, aes(x = Num)) +
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#geom_density(stat = "count", alpha = 0.5) +
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stat_count(geom="line", position="identity") +
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stat_count(geom="point", position="identity", size = 1) +
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facet_wrap(~Sample, nrow = 3, scales = "free_y") +
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#ylim(0, 1500) +
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ggtitle ("Deletions per cycle") +
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theme_bw()+
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/dpc.png", width = 2250, height = 1500, units = "px")
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rm (dels2, temp)
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#Number of insertions per cycle of sequence
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ins2 <- table_set[table_set$Sample==1,][,11]
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ins2 <- unlist(strsplit(ins2, split="|",fixed = TRUE))
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ins2 <- unlist(strsplit(ins2,","))
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ins2<- ins2[numbers_only(ins2)]
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ins2<-cbind("Num"=ins2, "Sample" = 1)
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ins2<-data.frame(ins2)
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for (i in samples) {
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temp <- table_set[table_set$Sample==i,][,11]
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temp <- unlist(strsplit(temp, split="|",fixed = TRUE))
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temp <- unlist(strsplit(temp,","))
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temp<- temp[numbers_only(temp)]
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temp<-cbind("Num"=temp, "Sample" = i)
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temp<-data.frame(temp)
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ins2<-rbind(temp, ins2)
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}
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ins2$Num<-as.numeric(ins2$Num)
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ggplot(ins2, aes(x = Num)) +
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stat_count(geom="line", position="identity") +
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stat_count(geom="point", position="identity", size = 1) +
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facet_wrap(~Sample, nrow = 3, scales = "free_y") +
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#ylim(0, 1500) +
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ggtitle ("Insertions per cycle") +
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scale_x_continuous(breaks=seq(0, 150, 25))+
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theme_bw()+
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/ipc.png", width = 2250, height = 1500, units = "px")
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rm (ins2, temp)
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#Number of deletions per sample
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parse_delets2 <- table_set[table_set$Sample==1,][,10]
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parse_delets2 <- unlist(strsplit(parse_delets2, split="|",fixed = TRUE))
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parse_delets2<-cbind("Num"=parse_delets2, "Sample" = 1)
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parse_delets2<-data.frame(parse_delets2)
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for (i in samples) {
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temp <- table_set[table_set$Sample==i,][,10]
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temp <- unlist(strsplit(temp,split="|",fixed = TRUE))
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temp<-cbind("Num"=temp, "Sample" = i)
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temp<-data.frame(temp)
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parse_delets2<-rbind(temp, parse_delets2)
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}
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ggplot(parse_delets2, aes(x=Sample, group = 1)) +
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stat_count(geom="bar", width = 0.7, fill = "white", color = "black", alpha = 0.7) +
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ggtitle("Deletions per sample") +
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#geom_vline(xintercept = 50, linetype="dotted") +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/dps.png", width = 2250, height = 1500, units = "px")
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rm (parse_delets2, temp)
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#Number of insertions per sample
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parse_inserts2 <- table_set[table_set$Sample==1,][,11]
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parse_inserts2 <- unlist(strsplit(parse_inserts2, split="|",fixed = TRUE))
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parse_inserts2<-cbind("Num"=parse_inserts2, "Sample" = 1)
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parse_inserts2<-data.frame(parse_inserts2)
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for (i in samples) {
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temp <- table_set[table_set$Sample==i,][,11]
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temp <- unlist(strsplit(temp,split="|",fixed = TRUE))
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temp<-cbind("Num"=temp, "Sample" = i)
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temp<-data.frame(temp)
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parse_inserts2<-rbind(temp, parse_inserts2)
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}
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ggplot(parse_inserts2, aes(x=Sample, group = 1)) +
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stat_count(geom="bar", width = 0.7, fill = "white", color = "black", alpha = 0.7) +
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ggtitle("Insertions per sample") +
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#geom_vline(xintercept = 50, linetype="dotted") +
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theme(plot.title = element_text(hjust = 0.5))
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ggsave("../report/images_assembly/ips.png", width = 2250, height = 1500, units = "px")
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rm (parse_inserts2, temp)
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#Frequency of insertion motifs per sample
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insert_motifs2 <- table_set[table_set$Sample==1,][,11]
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insert_motifs2 <- unlist(strsplit(insert_motifs2, split="|",fixed = TRUE))
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insert_motifs2 <- unlist(strsplit(insert_motifs2,","))
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insert_motifs2 <- insert_motifs2[!numbers_only(insert_motifs2)]
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insert_motifs2 <- cbind("Num"=insert_motifs2, "Sample" = 1)
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insert_motifs2 <- data.frame(insert_motifs2)
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for (i in samples) {
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temp <- table_set[table_set$Sample==i,][,11]
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temp <- unlist(strsplit(temp, split="|",fixed = TRUE))
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temp <- unlist(strsplit(temp,","))
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temp <- temp[!numbers_only(temp)]
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temp <- cbind("Num"=temp, "Sample" = i)
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temp <- data.frame(temp)
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insert_motifs2 <- rbind(temp, insert_motifs2)
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}
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ggplot(insert_motifs2, aes(x = Num)) +
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stat_count(geom='bar', width = 0.7, fill = "white", color = "black", alpha = 0.7) +
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facet_wrap(~Sample, nrow=4, scales = "free_y") +
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ggtitle("Insertion motifs per sample") +
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theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1))
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ggsave("../report/images_assembly/imps.png", width = 2250, height = 2500, units = "px")
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rm (temp, insert_motifs2)
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|
|
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#Frequency of deletion motifs per sample
|
|
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delete_motifs2 <- table_set[table_set$Sample==1,][,10]
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delete_motifs2 <- unlist(strsplit(delete_motifs2, split="|",fixed = TRUE))
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delete_motifs2 <- unlist(strsplit(delete_motifs2,","))
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delete_motifs2 <- delete_motifs2[!numbers_only(delete_motifs2)]
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delete_motifs2 <- cbind("Num"=delete_motifs2, "Sample" = 1)
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delete_motifs2 <- data.frame(delete_motifs2)
|
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for (i in samples) {
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temp <- table_set[table_set$Sample==i,][,10]
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temp <- unlist(strsplit(temp, split="|",fixed = TRUE))
|
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temp <- unlist(strsplit(temp,","))
|
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temp <- temp[!numbers_only(temp)]
|
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temp <- cbind("Num"=temp, "Sample" = i)
|
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temp <- data.frame(temp)
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delete_motifs2 <- rbind(temp, delete_motifs2)
|
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}
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ggplot(delete_motifs2, aes(x = Num)) +
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stat_count(geom='bar', width = 0.7, fill = "white", color = "black", alpha = 0.7) +
|
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facet_wrap(~Sample, nrow=7, scales = "free_y") +
|
|
ggtitle("Deletions motifs per sample") +
|
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theme(plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 90, hjust = 1))
|
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ggsave("../report/images_assembly/dmps.png", width = 2250, height = 3500, units = "px")
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rm (delete_motifs2, temp) |