/
transposons_plots.R
executable file
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transposons_plots.R
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require(ggplot2)
require(plyr)
require(VennDiagram)
require(gridExtra)
require(gdata)
require(scales)
require(splitstackshape)
# Help function to create a venn plot
venn_3_samples <- function(sample1 = sample1, sample2 = sample2, sample3 = sample3, name1 = name1, name2 = name2, name3 = name3, clone_name = clone_name, save_ids = "FALSE"){
sample1 <- unique(sample1[complete.cases(sample1)])
sample2 <- unique(sample2[complete.cases(sample2)])
sample3 <- unique(sample3[complete.cases(sample3)])
inter_12_full <- unique(intersect(sample1, sample2))
inter_13_full <- unique(intersect(sample1, sample3))
inter_23_full <- unique(intersect(sample2, sample3))
inter_123_full <- unique(intersect(inter_12_full, sample3))
inter_12 <- unique(inter_12_full[!inter_12_full %in% inter_123_full])
inter_13 <- unique(inter_13_full[!inter_13_full %in% inter_123_full])
inter_23 <- unique(inter_23_full[!inter_23_full %in% inter_123_full])
unic_s1 <- unique(sample1[!sample1 %in% c(inter_12, inter_13, inter_123_full)])
unic_s2 <- unique(sample2[!sample2 %in% c(inter_12, inter_23, inter_123_full)])
unic_s3 <- unique(sample3[!sample3 %in% c(inter_13, inter_23, inter_123_full)])
# write files with the Ids of the sites in each subset of the venn plots
if(save_ids == "TRUE"){
write.table(inter_123_full,
paste(clone_name, name1, "vs", name2, "vs", name3, "intersection_transposons.txt", sep = "_"), row.names = F, col.names = F, quote = F)
write.table(inter_12,
paste(clone_name, "only", name1, "vs", name2, "intersection_transposons.txt", sep = "_"),
row.names = F, col.names = F, quote = F)
write.table(inter_13,
paste(clone_name, "only", name1, "vs", name3, "intersection_transposons.txt", sep = "_"),
row.names = F, col.names = F, quote = F)
write.table(inter_23,
paste(clone_name, "only", name2, "vs", name3, "intersection_transposons.txt", sep = "_"),
row.names = F, col.names = F, quote = F)
write.table(inter_12_full,
paste(clone_name, "all", name1, "vs", name2, "intersection_transposons.txt", sep = "_"),
row.names = F, col.names = F, quote = F)
write.table(inter_13_full,
paste(clone_name, "all", name1, "vs", name3, "intersection_transposons.txt", sep = "_"),
row.names = F, col.names = F, quote = F)
write.table(inter_23_full,
paste(clone_name, "all", name2, "vs", name3, "intersection_transposons.txt", sep = "_"),
row.names = F, col.names = F, quote = F)
write.table(unic_s1,
paste(clone_name, name1, "exclusive_transposons.txt", sep = "_"),
row.names = F, col.names = F, quote = F)
write.table(unic_s2,
paste(clone_name, name2, "exclusive_transposons.txt", sep = "_"),
row.names = F, col.names = F, quote = F)
write.table(unic_s3,
paste(clone_name, name3, "exclusive_transposons.txt", sep = "_"),
row.names = F, col.names = F, quote = F)
}else if(save_ids == "FALSE"){
}else{
print("Invalid save_ids option!")
}
grid.newpage();
venn.plot <- draw.triple.venn(
area1 = length(sample1),
area2 = length(sample2),
area3 = length(sample3),
n12 = length(inter_12_full),
n13 = length(inter_13_full),
n23 = length(inter_23_full),
n123 = length(inter_123_full),
cross.area = length(inter),
alpha = 0.5,
category = c(deparse(name1), deparse(name2), deparse(name3)),
fill = c("darkgreen", "green", "#8B4513"),
lty = "blank",
cex = 4,
cat.cex = 2.5,
cat.dist = 0.055,
ext.pos = 0,
ext.dist = -0.05,
ext.length = 0.85,
ext.line.lwd = 2,
ext.line.lty = "dashed",
scaled = T,
print.mode = c("raw", "percent"),
rotation.degree = 0)
}
transposons <- read.table(snakemake@input[[1]], sep = "\t")
transposon_in_genes <- read.table(snakemake@input[[2]], sep = "\t")
transposons <- transposons[, c(4, 10)]
transposon_in_genes <- transposon_in_genes[, c(4, 10)]
marks_in_transp <- c(unique(as.character(transposons$V4)),
unique(as.character(transposon_in_genes$V4)))
# Reads the MSD-Methylated sites
DM_marks <- read.table(snakemake@input[[3]],
header = T,
na.strings = "NA",
colClasses = "character")
# Defines the Methylated TEs of each sample
transposons_all <- data.frame()
for(i in 1:length(names(DM_marks))){
if(i == 1){
query <- DM_marks[DM_marks[, i] %in% marks_in_transp, i]
transposons_group <- transposons[transposons$V4 %in% query,][2]
colnames(transposons_group) <- paste(names(DM_marks)[i])
transposons_all <- cbind(transposons_group)
}else{
query <- DM_marks[DM_marks[,i] %in% marks_in_transp, i]
transposons_group <- transposons[transposons$V4 %in% query,][2]
colnames(transposons_group) <- paste(names(DM_marks)[i])
transposons_all <- cbindX(transposons_all, transposons_group)
}
}
# Writes a file with the TEs of each sample
write.table(transposons_all,
snakemake@output[[1]],
sep = "\t",
col.names = T,
row.names = F,
quote = F)
# Venn plots
A <- venn_3_samples(sample1 = transposons_all[,1],
sample2 = transposons_all[,2],
sample3 = transposons_all[,3],
name1 = colnames(transposons_all)[1],
name2 = colnames(transposons_all)[2],
name3 = colnames(transposons_all)[3],
clone_name = "BRASUZ1",
save_ids = "TRUE")
dev.off()
## Export as a svg file
svg(filename = snakemake@output[[2]], width = 12, height = 12, pointsize = 12)
grid.arrange(grobTree(A), ncol = 1, top = textGrob("BRASUZ1", gp = gpar(fontsize = 30, font = 8)))
dev.off()
# Select the subset of TEs of the venn plots to annotation
sample1 = transposons_all[, 1]
sample2 = transposons_all[, 2]
sample3 = transposons_all[, 3]
sample1 <- unique(as.character(sample1[complete.cases(sample1)]))
sample2 <- unique(as.character(sample2[complete.cases(sample2)]))
sample3 <- unique(as.character(sample3[complete.cases(sample3)]))
inter_12_full <- unique(intersect(sample1, sample2))
inter_13_full <- unique(intersect(sample1, sample3))
inter_23_full <- unique(intersect(sample2, sample3))
inter_123_full <- unique(intersect(inter_12_full, sample3))
inter_12 <- unique(inter_12_full[!inter_12_full %in% inter_123_full])
inter_13 <- unique(inter_13_full[!inter_13_full %in% inter_123_full])
inter_23 <- unique(inter_23_full[!inter_23_full %in% inter_123_full])
unic_s1 <- unique(sample1[!sample1 %in% c(inter_12, inter_13, inter_123_full)])
unic_s2 <- unique(sample2[!sample2 %in% c(inter_12, inter_23, inter_123_full)])
unic_s3 <- unique(sample3[!sample3 %in% c(inter_13, inter_23, inter_123_full)])
venn_subset_transp <- cbindX(as.data.frame(inter_123_full),
as.data.frame(unic_s1),
as.data.frame(unic_s2),
as.data.frame(unic_s3))
transp_genome <- read.table(snakemake@input[[4]], sep = "\t")[,4]
transposons_all <- cbindX(as.data.frame(sample1),
as.data.frame(sample2),
as.data.frame(sample3),
venn_subset_transp)
transposons_all <- cbindX(transposons_all, as.data.frame(transp_genome))
colnames(transposons_all) <- c("Adult leaves",
"Juvenile leaves",
"Xylem",
"Intersection",
"Unique in Adult",
"Unique in Juvenile",
"Unique in Xylem",
"Genome")
# Verify the TEs that are not possible to classify correctly.
TE_not_class <- read.table(snakemake@input[[4]], sep = "\t")
TE_not_class <- cSplit(indt = TE_not_class, splitCols = "V4", sep = ",", drop = F)
TE_not_class <- TE_not_class[is.na(TE_not_class$V4_02) == FALSE, ]
TE_not_class <- as.character(TE_not_class$V4)
# Defines the groups to be used in the plot
use_intersections <- snakemake@params[["use_intersections"]]
if(use_intersections == TRUE){
transp_class_all <- data.frame()
for(i in 1:length(names(transposons_all))){
transp_subset <- unique(as.character(transposons_all[, i]))
# check TEs with overlaps
transp_unclass <- unique(as.character(transp_subset[transp_subset %in% TE_not_class]))
# check if in the regions with more than one TE, the TEs are of differently classified
transp_unclass_final <- character()
for( t in 1:length(transp_unclass)){
sum_of_class <- (length(grep("DMX", transp_unclass[t])) +
length(grep("DTX", transp_unclass[t])) +
length(grep("DHX", transp_unclass[t])) +
length(grep("DXX-MtTE", transp_unclass[t])) +
length(grep("DXX_Blc", transp_unclass[t])) +
length(grep("RYX", transp_unclass[t])) +
length(grep("RtX", transp_unclass[t])) +
length(grep("RLX", transp_unclass[t])) +
length(grep("RXX-LARD", transp_unclass[t])) +
length(grep("RXX-TRtM", transp_unclass[t])) +
length(grep("RSX", transp_unclass[t])) +
length(grep("RXX_Blc", transp_unclass[t])))
if(sum_of_class > 1){
transp_unclass_final <- c(transp_unclass_final, transp_unclass[t])
}
}
unknow_TE <- length(transp_unclass_final)
transp_subset <- transp_subset[!transp_subset %in% transp_unclass_final]
#MAVERICK | DMX
maverick <- length(grep("DMX", transp_subset))
#CACTA | DTX
cacta <- length(grep("DTX", transp_subset))
#HELITRON | DHX
helitron <- length(grep("DHX", transp_subset))
#MITE | DXX-MITE
mite <- length(grep("DXX-MITE", transp_subset))
#DNA_general | DXX_Blc
dna_general <- length(grep("DXX_Blc", transp_subset))
#DIRS/VIPER | RYX
dirs_viper <- length(grep("RYX", transp_subset))
#LINE | RIX
line <- length(grep("RIX", transp_subset))
#LTR | RLX
ltr <- length(grep("RLX", transp_subset))
#LARD | RXX-LARD
lard <- length(grep("RXX-LARD", transp_subset))
#TRIM | RXX-TRIM
trim <- length(grep("RXX-TRIM", transp_subset))
#SINE | RSX
sine <- length(grep("RSX", transp_subset))
#general | RXX_Blc
rna_general <- length(grep("RXX_Blc", transp_subset))
category <- c("MAVERICK",
"CACTA",
"HELITRON",
"MITE",
"DNA_general",
"DIRS/VIPER",
"LINE",
"LTR",
"LARD",
"TRIM",
"SINE",
"RNA_general",
"Nested TEs")
len <- c(maverick,
cacta,
helitron,
mite,
dna_general,
dirs_viper,
line,ltr,
lard,
trim,
sine,
rna_general,
unknow_TE)
transp_class <- data.frame(rep(names(transposons_all)[i], length(category)), category, len)
transp_class_all <- rbind(transp_class_all ,transp_class)
}
colnames(transp_class_all) <- c("samples", "classif", "quantif")
transp_class_all <- transp_class_all[!transp_class_all$quantif == "0",]
group_plot <-character()
for(i in 1:nrow(transp_class_all)){
if(paste(transp_class_all$samples[i]) == "Genome"){
group_plot <- append(group_plot,"E. grandis")
}else{
group_plot <- append(group_plot,"Samples")
}
}
transp_class_all <- cbind(group_plot, transp_class_all)
write.table(transp_class_all, snakemake@output[[3]], quote = F, row.names = F, col.names = F, sep = "\t")
## Defines the colors of the bars.
fill <- c("#000000",
"#E69F00",
"#56B4E9",
"#009E73",
"#F0E442",
"#0072B2",
"#D55E00",
"#CC79A7",
"#2C7417",
"#CBE75F",
"#A877EA",
"#6BF4ED",
"#AC6AC9")
transp_plot <- ggplot(transp_class_all, aes(x = samples, y = quantif, fill = classif)) +
geom_bar(stat = "identity", alpha = 0.9, width = 0.5) +
scale_x_discrete(name="")+
scale_y_continuous(breaks = scales::pretty_breaks(n = 10))+
facet_wrap( ~ group_plot, ncol=2, scales="free")+
ylab("Number of transposons")+
theme_bw()+
theme(legend.text=element_text(size=14), axis.title.y=element_text(size = 20, vjust=2), axis.title.x=element_text(size=14,vjust=0), axis.text.x = element_text(size = 15), axis.text.y = element_text(size = 15), legend.title = element_blank(), strip.text.x = element_text(size = 15))+
scale_fill_manual(values=fill)
transp_plot_perc <- ggplot(transp_class_all, aes(x = samples, y = quantif, fill = classif)) +
geom_bar(position = "fill", stat = "identity", alpha = 0.9, width = 0.5) +
scale_x_discrete(name="")+
scale_y_continuous(labels = percent_format())+
facet_wrap( ~ group_plot, ncol=2, scales="free")+
ylab("Number of transposons")+
theme_bw()+
theme(legend.text=element_text(size=14), axis.title.y=element_text(size = 20, vjust=2), axis.title.x=element_text(size=14,vjust=0), axis.text.x = element_text(size = 15), axis.text.y = element_text(size = 15), legend.title = element_blank(), strip.text.x = element_text(size = 15))+
scale_fill_manual(values=fill)
# Get the ggplot grob
transp_plot_grob = ggplotGrob(transp_plot)
transp_plot_perc_grob = ggplotGrob(transp_plot_perc)
# Builds and save the bar plot.
svg(snakemake@output[[4]], width=10, height=7)
grid.newpage()
grid.draw(transp_plot_grob)
dev.off()
# Builds and save the bar plot.
svg(snakemake@output[[5]], width=10, height=7)
grid.newpage()
grid.draw(transp_plot_perc_grob)
dev.off()
}else if(use_intersections == FALSE){
transposons_all <- transposons_all[, colnames(transposons_all) %in% c("Adult leaves", "Juvenile leaves", "Xylem", "Genome") ]
transp_class_all <- data.frame()
for(i in 1:length(names(transposons_all))){
transp_subset <- unique(as.character(transposons_all[, i]))
# check TEs with overlaps
transp_unclass <- unique(as.character(transp_subset[transp_subset %in% TE_not_class]))
transp_unclass_final <- character()
for( t in 1:length(transp_unclass)){
sum_of_class <- (length(grep("DMX", transp_unclass[t])) +
length(grep("DTX", transp_unclass[t])) +
length(grep("DHX", transp_unclass[t])) +
length(grep("DXX-MtTE", transp_unclass[t])) +
length(grep("DXX_Blc", transp_unclass[t])) +
length(grep("RYX", transp_unclass[t])) +
length(grep("RtX", transp_unclass[t])) +
length(grep("RLX", transp_unclass[t])) +
length(grep("RXX-LARD", transp_unclass[t])) +
length(grep("RXX-TRtM", transp_unclass[t])) +
length(grep("RSX", transp_unclass[t])) +
length(grep("RXX_Blc", transp_unclass[t])))
if(sum_of_class > 1){
transp_unclass_final <- c(transp_unclass_final, transp_unclass[t])
}
}
unknow_TE <- length(transp_unclass_final)
transp_subset <- transp_subset[!transp_subset %in% transp_unclass_final]
#MAVERICK | DMX
maverick <- length(grep("DMX", transp_subset))
#CACTA | DTX
cacta <- length(grep("DTX", transp_subset))
#HELITRON | DHX
helitron <- length(grep("DHX", transp_subset))
#MITE | DXX-MITE
mite <- length(grep("DXX-MITE", transp_subset))
#DNA_general | DXX_Blc
dna_general <- length(grep("DXX_Blc", transp_subset))
#DIRS/VIPER | RYX
dirs_viper <- length(grep("RYX", transp_subset))
#LINE | RIX
line <- length(grep("RIX", transp_subset))
#LTR | RLX
ltr <- length(grep("RLX", transp_subset))
#LARD | RXX-LARD
lard <- length(grep("RXX-LARD", transp_subset))
#TRIM | RXX-TRIM
trim <- length(grep("RXX-TRIM", transp_subset))
#SINE | RSX
sine <- length(grep("RSX", transp_subset))
#general | RXX_Blc
rna_general <- length(grep("RXX_Blc", transp_subset))
category <- c("MAVERICK",
"CACTA",
"HELITRON",
"MITE",
"DNA_general",
"DIRS/VIPER",
"LINE",
"LTR",
"LARD",
"TRIM",
"SINE",
"RNA_general",
"Nested TEs")
len <- c(maverick,
cacta,
helitron,
mite,
dna_general,
dirs_viper,
line,ltr,
lard,
trim,
sine,
rna_general,
unknow_TE)
transp_class <- data.frame(rep(names(transposons_all)[i], length(category)), category, len)
transp_class_all <- rbind(transp_class_all ,transp_class)
}
colnames(transp_class_all) <- c("samples", "classif", "quantif")
transp_class_all <- transp_class_all[!transp_class_all$quantif == "0",]
group_plot <-character()
for(i in 1:nrow(transp_class_all)){
if(paste(transp_class_all$samples[i]) == "Genome"){
group_plot <- append(group_plot, "E. grandis")
}else{
group_plot <- append(group_plot, "Samples")
}
}
transp_class_all <- cbind(group_plot, transp_class_all)
write.table(transp_class_all, snakemake@output[[3]], quote = F, row.names = F, col.names = F, sep = "\t")
## Defines the colors of the bars.
fill <- c("#000000",
"#E69F00",
"#56B4E9",
"#009E73",
"#F0E442",
"#0072B2",
"#D55E00",
"#CC79A7",
"#2C7417",
"#CBE75F",
"#A877EA",
"#6BF4ED",
"#AC6AC9")
# labels of the grids
levels(transp_class_all$group_plot) <- c("E. grandis" = "E. grandis - all TEs", "Samples" = "Samples - Methylated TEs")
transp_plot <- ggplot(transp_class_all, aes(x = samples, y = quantif, fill = classif)) +
geom_bar(stat = "identity", alpha = 0.9, width = 0.4) +
scale_x_discrete(name="")+
scale_y_continuous(breaks = scales::pretty_breaks(n = 10))+
facet_wrap( ~ group_plot, ncol = 2, scales = "free") +
ylab("Number of transposons")+
theme_bw()+
theme(legend.text=element_text(size=14),
axis.title.y = element_text(size = 20, vjust = 2),
axis.title.x=element_text(size = 14, vjust = 0),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15),
legend.title = element_blank(),
strip.text.x = element_text(size = 15))+
scale_fill_manual(values = fill)
# Get the ggplot grob
transp_plot_grob = ggplotGrob(transp_plot)
# changes the dimension of the the second box
transp_plot_grob$widths[9] = 2*transp_plot_grob$widths[9]
# Builds and save the bar plot.
svg(snakemake@output[[4]], width=10, height=7)
# Draw the plot
grid.newpage()
grid.draw(transp_plot_grob)
dev.off()
transp_plot_perc <- ggplot(transp_class_all, aes(x = samples, y = quantif, fill = classif)) +
geom_bar(position = "fill", stat = "identity", alpha = 0.9, width = 0.5) +
scale_x_discrete(name="")+
scale_y_continuous(labels = percent_format())+
facet_wrap( ~ group_plot, ncol=2, scales="free")+
ylab("Relative frequency")+
theme_bw()+
theme(legend.text=element_text(size=14), axis.title.y=element_text(size = 20, vjust=2), axis.title.x=element_text(size=14,vjust=0), axis.text.x = element_text(size = 15), axis.text.y = element_text(size = 15), legend.title = element_blank(), strip.text.x = element_text(size = 15))+
scale_fill_manual(values=fill)
# Get the ggplot grob
transp_plot_perc_grob = ggplotGrob(transp_plot_perc)
# changes the dimension of the the second box
transp_plot_perc_grob$widths[9] = 2*transp_plot_perc_grob$widths[9]
# Builds and save the bar plot.
svg(snakemake@output[[5]], width=10, height=7)
# Draw the plot
grid.newpage()
grid.draw(transp_plot_perc_grob)
dev.off()
}