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gene_body_coverage.R
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# >> gene body coverage << #
# load libraries ----
library(here)
source(here("Rscripts/load_libraries.R"))
# functions & defs ----
filter_bed_files <- function(input_file){
strand_f <- str_split_fixed(str_split_fixed(string = input_file, pattern = "\\.coverage", n = 2)[,1], "\\.", 2)[,2]
suppressMessages(vroom(input_file,col_names = c("seqid","TSS", "TTS", "gene","rel_pos", "counts"), num_threads = 8, progress = F)) %>%
as_tibble() %>%
mutate(strand = strand_f) %>%
group_by(gene) %>%
mutate(perc_pos = ifelse(strand == "plus", round(scales::rescale(rel_pos, to=c(0,100)), digits = 0),
round(scales::rescale(rel_pos, to=c(100,0)), digits= 0))) %>%
dplyr::select(-strand)
}
merge_bed_files <- function(input_plus, input_minus){
# Suppress summarise info
options(dplyr.summarise.inform = FALSE)
rbind(input_plus, input_minus) %>%
group_by(gene) %>%
mutate(perc_coverage = scales::rescale(counts, to = c(0,100))) %>%
group_by(perc_pos, gene) %>%
summarise(C_mean = mean(perc_coverage),
C_max = max(perc_coverage),
C_min = min(perc_coverage)) %>%
group_by(perc_pos) %>%
summarise(C_mean_sum = mean(C_mean),
C_m_sum = min(C_mean)) %>%
mutate(median_C = median(C_mean_sum),
IQR = IQR(C_mean_sum),
QCoV = IQR/median_C)
}
modify_coverage_files <- function(folder, minimum_wanted_seqs = 10, output = c("normal", "genesizes")){
coverage_files <- list.files(folder, recursive = T, pattern = ".coverage$")
plus_c_files <- coverage_files[which(1:length(coverage_files) %% 2 == 0)]
minus_c_files <- coverage_files[which(1:length(coverage_files) %% 2 == 1)]
dataset_names <- str_split_fixed(str_split_fixed(str_split_fixed(plus_c_files, "\\/", n = 3)[,3],"_fu",2)[,1], "\\.", 2)[,1]
coverage_frame <- data.table()
for(i in seq_along(plus_c_files)){
# files
f <- folder
tic("normalise coverage files")
print(paste0("file number ", i, " of ", length(plus_c_files)))
if(output == "normal"){
# coverage to normalised coverage
p_t <- filter_bed_files(paste0(f,plus_c_files[i])) %>%
group_by(gene) %>% dplyr::filter(min(counts) >= minimum_wanted_seqs) %>% ungroup()
m_t <- filter_bed_files(paste0(f,minus_c_files[i])) %>%
group_by(gene) %>% dplyr::filter(min(counts) >= minimum_wanted_seqs) %>% ungroup()
a_t <- merge_bed_files(p_t, m_t) %>%
mutate(dataset = dataset_names[i])
coverage_frame <- rbind(coverage_frame, a_t)
}else{
# coverage to normalised coverage
p_t <- filter_bed_files_size(paste0(f,plus_c_files[i])) %>%
group_by(gene) %>% dplyr::filter(min(counts) >= minimum_wanted_seqs) %>% ungroup()
m_t <- filter_bed_files_size(paste0(f,minus_c_files[i])) %>%
group_by(gene) %>% dplyr::filter(min(counts) >= minimum_wanted_seqs) %>% ungroup()
a_t <- merge_bed_files_sizes(p_t, m_t) %>%
mutate(dataset = dataset_names[i])
coverage_frame <- rbind(coverage_frame, a_t)
}
toc()
}
return(coverage_frame)
}
keep_highest_site <- function(inputdf, selected_end, merge_w = 20, cov_min = 3){
inputdf %>%
distinct({{selected_end}}, .keep_all = T) %>%
arrange({{selected_end}}) %>%
mutate(index = lag({{selected_end}}, default = 1) + as.integer(merge_w),
index1 = cumsum(ifelse(index >= {{selected_end}}, 0, 1))+1) %>%
dplyr::group_by(index1) %>%
dplyr::filter(cov == max(cov),
cov >= cov_min) %>%
ungroup() %>%
group_by(gene) %>%
dplyr::slice(which.max(cov)) %>%
ungroup() %>%
dplyr::select(id_name, {{selected_end}}, strand)
}
filter_bed_files_size <- function(input_file, min_wanted = 10){
strand_f <- str_split_fixed(str_split_fixed(string = input_file, pattern = "\\.coverage", n = 2)[,1], "\\.", 2)[,2]
suppressMessages(vroom(input_file,col_names = c("seqid","TSS", "TTS", "gene","rel_pos", "counts"), num_threads = 8, progress = F)) %>%
as_tibble() %>%
mutate(strand = strand_f) %>%
group_by(gene) %>%
mutate(perc_pos = ifelse(strand == "plus", round(scales::rescale(rel_pos, to=c(0,100)), digits = 0),
round(scales::rescale(rel_pos, to=c(100,0)), digits= 0))) %>%
dplyr::select(-strand) %>%
group_by(gene) %>%
dplyr::filter(min(counts) >= min_wanted) %>%
ungroup() %>%
left_join(ecoli_gff %>% dplyr::select(id_name, width) %>% dplyr::rename(gene = id_name), by = c("gene")) %>%
mutate(read_group = ifelse(width <= 500, "sub500",
ifelse(width > 500 & width <= 1000, "sub1000",
ifelse(width > 1000 & width <= 1500, "sub1500",
ifelse(width > 1500 & width <= 2000, "sub1500",
ifelse(width > 2000, "big2000",NA))))))
}
merge_bed_files_sizes <- function(input_plus, input_minus){
# Suppress summarise info
options(dplyr.summarise.inform = FALSE)
f <- rbind(input_plus, input_minus) %>%
group_by(gene) %>%
mutate(perc_coverage = scales::rescale(counts, to = c(0,100))) %>%
group_by(perc_pos, gene,read_group) %>%
summarise(C_mean = mean(perc_coverage),
C_max = max(perc_coverage),
C_min = min(perc_coverage)) %>%
group_by(perc_pos,read_group) %>%
summarise(C_mean_sum = mean(C_mean),
C_m_sum = min(C_mean)) %>%
ungroup() %>%
group_by(read_group) %>%
mutate(median_C = median(C_mean_sum),
IQR = IQR(C_mean_sum),
QCoV = IQR/median_C)
gene_per_group <- rbind(input_plus, input_minus) %>%
distinct(gene, read_group) %>%
group_by(read_group) %>%
summarise(n = n())
f2 <- left_join(f, gene_per_group, by = "read_group")
return(f2)
}
# load & tidy data ----
## prepare 5´-3´end tables for coverage calculations ====
### primary 5´end ####
dir <- here()
tss <- vroom(file = paste0(dir,"/tables/tss_tables/tss_data_untrimmed.tsv"), num_threads = 8, progress = F) %>%
mutate(mode = str_sub(sample, 1,3),
TSS = ifelse(mode == "RNA" & strand == "+", TSS - 12,
ifelse(mode == "RNA" & strand == "-", TSS + 12,TSS))) %>%
dplyr::filter(TSS_type == "primary", type == "CDS") %>%
keep_highest_site(inputdf = .,selected_end = TSS)
### primary 3´end ####
tts <- vroom(file = paste0(dir,"/tables/tts_tables/tts_data_trimmed.tsv"), num_threads = 8, progress = F) %>%
mutate(mode = str_sub(sample, 1,3)) %>%
dplyr::filter(TTS_type == "primary", type == "CDS") %>%
keep_highest_site(inputdf = .,selected_end = TTS)
### find gens with annotated primary 5´and 3´end ####
w <- left_join(tss, tts, by = c("id_name", "strand")) %>%
dplyr::filter(!is.na(TSS), !is.na(TTS)) %>%
mutate(seqnames = ecoli_gff$seqid[1]) %>%
dplyr::select(seqnames, TSS, TTS, id_name, strand)
### write to bed-like file which can be used with bedtools coverage
fwrite(w %>% dplyr::filter(strand == "+") %>% dplyr::select(-strand),
paste0(dir,"/tables/transcript_tables/transcripts.plus.bedgraph"), sep = "\t", col.names = F, quote = F)
fwrite(w %>% dplyr::filter(strand == "-") %>% dplyr::select(-strand),
paste0(dir,"/tables/transcript_tables/transcripts.minus.bedgraph"), sep = "\t", col.names = F, quote = F)
## read in files from bedtools coverage ====
### full-length > polyA-trimmed > polyA & SSP adapter trimmed > clipping removed > stranded ####
cov_trimmed <- modify_coverage_files(folder = paste0(dir, "/data/coverage_data/coverage_data_pychopper_auto_cutadapt_SSP_clipped_stranded/"),
output = "normal")
### notrimming > stranded ####
cov_untrimmed <- modify_coverage_files(folder = paste0(dir,"/data/coverage_data/coverage_data_notrimming_stranded/"),
output = "normal")
### merge datasets ####
cov_sets <- rbind(cov_untrimmed %>% mutate(method = "untrimmed"),
cov_trimmed %>% mutate(method = "trimmed")) %>%
dplyr::left_join(old_new, by = c("dataset" = "old_name")) %>%
mutate(sample = new_name) %>%
dplyr::select(-new_name, -dataset) %>%
dplyr::filter(!is.na(sample))
## calculate QCoV sizes
cov_trimmed_sizes <- modify_coverage_files(folder = paste0(dir, "/data/coverage_data/coverage_data_pychopper_auto_cutadapt_SSP_clipped_stranded/"),
output = "genesizes")
# PLOTS ----
## reorder levels ====
cov_sets$sample <- factor(cov_sets$sample,
levels = (bc_to_sample$sample[c(1,10,11,8,9,2,4,3,5,6,7)]))
cov_sets$method <- factor(cov_sets$method,
levels = rev(c("untrimmed", "trimmed")))
cov_trimmed_sizes$sample <- factor(cov_trimmed_sizes$sample,
levels = rev(bc_to_sample$sample[c(1,10,11,8,9,2,4,3,5,6,7)]))
cov_trimmed_sizes$read_group <- factor(cov_trimmed_sizes$read_group,
levels = (c("sub500",
"sub1000",
"sub1500",
"big2000")))
## plotting ====
### Gene body coverage - Fig. 4A ####
ggplot(data = cov_sets ,
aes(x = perc_pos, y = C_mean_sum)) +
geom_line(size = 1.2,aes(linetype = method), color = "black") +
facet_grid(cols = vars(sample)) +
scale_x_continuous(limits = c(0,100), expand = c(0,0)) +
scale_y_continuous(limits = c(0,100), expand = c(0,0)) +
geom_ribbon(aes(fill = method, ymin = 0, ymax = C_mean_sum), alpha = 0.5, color = NA) +
scale_fill_manual(values = rev(c("#AD9D86","#A1CEC2"))) +
theme_Publication_white() +
theme(panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "black")) +
ylab("Mean gene body coverage (%)") +
xlab("Relative 5´to 3´gene body position (%)")
### Gene body coverage - Fig. 4C ####
cov_sets %>%
group_by(sample,method) %>%
summarise(QCoV = max(QCoV)) %>%
mutate(mode = str_sub(sample, 1,3),
sample = factor(sample, levels = rev(bc_to_sample$sample[c(1,10,11,8,9,2,4,3,5,6,7)]))) %>%
ggplot() +
geom_bar(aes(y = sample, x = QCoV, group = method, fill = method),position = position_dodge(),
stat = "identity", color = "black") +
theme_Publication_white() +
theme(panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "black", linetype = "dashed")) +
scale_x_continuous(limits = c(0.0,0.3, expand = c(0,0))) +
scale_fill_manual(values = c("#AD9D86","#A1CEC2")) +
ylab("") +
xlab("QCoV")
### Cov5 - Fig. 4D ####
cov_sets %>%
dplyr::filter(perc_pos <= 10, method == "trimmed") %>%
dplyr::group_by(sample) %>%
summarise(prime5 = mean(C_mean_sum, na.rm = T)/median_C*100) %>%
distinct(sample, .keep_all = T) %>%
mutate(mode = str_sub(sample, 1,3),
sample = factor(sample, levels = rev(bc_to_sample$sample[c(1,10,11,8,9,2,4,3,5,6,7)]))) %>%
ggplot() +
geom_bar(aes(y = sample, x = prime5 - 100, fill = mode),
stat = "identity", color = "black") +
scale_x_continuous(limits = c(-45,45), expand = c(0,0)) +
theme_Publication_white() +
scale_fill_manual(values = cbf1[c(2,5,3)]) +
theme(panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "black", linetype = "dashed")) +
ylab("") +
xlab("CoV5")
### Cov3 - Fig. 4E ####
cov_sets %>%
dplyr::filter(perc_pos >= 90, method == "trimmed") %>%
dplyr::group_by(sample) %>%
summarise(prime3 = mean(C_mean_sum, na.rm = T)/median_C*100) %>%
distinct(sample, .keep_all = T) %>%
mutate(mode = str_sub(sample, 1,3),
sample = factor(sample, levels = rev(bc_to_sample$sample[c(1,10,11,8,9,2,4,3,5,6,7)]))) %>%
ggplot() +
geom_bar(aes(y = sample, x = prime3 - 100, fill = mode),
stat = "identity", color = "black") +
scale_x_continuous(limits = c(-45,45), expand = c(0,0)) +
theme_Publication_white() +
scale_fill_manual(values = cbf1[c(2,5,3)]) +
theme(panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "black", linetype = "dashed")) +
ylab("") +
xlab("CoV3")
### QCoV gene size - Fig. 4F ####
ggplot(data = cov_trimmed_sizes %>% mutate(mode = str_sub(sample,1,3)) %>% distinct(sample, read_group, QCoV,n, .keep_all =T),
aes(x = QCoV, y = sample, fill = mode, factor = read_group)) +
geom_bar(stat = "identity", position = position_dodge(), color = "black",
aes(alpha = read_group),size = 0.5) +
scale_alpha_manual(values = rev(c(0,0.33,0.66,1))) +
scale_x_continuous(limits = c(0.0,1.5),expand = c(0,0)) +
theme_Publication_white() +
scale_fill_manual(values = cbf1[c(2,5,3)])