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manuscript_figures.R
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library(tidyverse)
library(GenomicRanges)
library(SummarizedExperiment)
library(biomaRt)
library(DBI)
library(doParallel)
library(ggridges)
####################################################
## CONNECT TO THE SPLICING DATABASE ################
####################################################
# source("/home/sruiz/PROJECTS/splicing-accuracy-manuscript/manuscript_figures.R")
base_folder <- here::here()
## CONNECT TO THE DATABASE ------------------------------
supporting_reads <- 1
gtf_version <- 105
main_project <- paste0("splicing_",supporting_reads,"read")
database_path <- paste0(base_folder, "/database/", main_project, "/", gtf_version, "/", main_project, ".sqlite")
con <- dbConnect(RSQLite::SQLite(), database_path)
tables <- dbListTables(con)
## SET PATHS TO FOLDERS
args <-
list(
dependencies_folder = file.path(here::here(), "dependencies"),
results_folder = file.path(here::here(), "results", main_project, gtf_version, "_paper_review", "results"),
figures_folder = file.path(here::here(), "results", main_project, gtf_version, "_paper_review", "figures"),
data_folder = file.path(here::here(), "results", main_project, gtf_version, "_paper_review", "data")
)
dir.create(file.path(args$results_folder), recursive = TRUE, showWarnings = F)
dir.create(file.path(args$figures_folder), recursive = TRUE, showWarnings = F)
dir.create(file.path(args$data_folder), recursive = TRUE, showWarnings = F)
## QUERY MASTER TABLES
query = paste0("SELECT * FROM 'metadata'")
df_metadata <- dbGetQuery(con, query) %>% distinct(sample_id, .keep_all = T) %>% as_tibble()
all_projects <- df_metadata$SRA_project %>% unique
df_metadata$cluster %>% unique
# df_metadata %>% dplyr::count(cluster) %>% print(n=100)
query <- paste0("SELECT * FROM 'intron'")
master_introns <- dbGetQuery(con, query) %>% as_tibble()
query <- paste0("SELECT * FROM 'novel'")
master_novel_junctions <- dbGetQuery(con, query) %>% as_tibble()
query <- paste0("SELECT * FROM 'transcript'")
master_transcripts <- dbGetQuery(con, query) %>% as_tibble()
query <- paste0("SELECT * FROM 'gene'")
master_genes <- dbGetQuery(con, query) %>% as_tibble()
custom_ggtheme <- theme(text = element_text(size = 7, colour = "black"),
axis.ticks = element_line(colour = "black", linewidth = 2),
axis.text = element_text(size = 7, colour = "black"),
axis.line = element_line(colour = "black"),
axis.title = element_text(size = 7, colour = "black"),
axis.text.y = element_text(size = 7, colour = "black"),
axis.text.x = element_text(size = 7, colour = "black", hjust = 0.5, vjust = 0.5),
strip.text = element_text(size = 7, colour = "black"),
legend.text = element_text(size = "7", colour = "black"),
legend.title = element_blank(),
legend.position = "top")
########################################
## FUNCTIONS - produce figures for the
## paper
########################################
## SECTION 1 - GENERAL TESTS --------------------------------------
## 1. Novel donor and acceptor junctions are commonly detected and exceed the number of unique annotated introns by an average of 11-fold
stats_results_section_1 <- function() {
## We found that 268,988 (82.8%) annotated introns had at least a single associated novel donor or acceptor junction,
## with only 55,968 annotated introns appearing to be precisely spliced across all the samples and tissues studied.
master_introns %>% distinct(ref_junID) %>% nrow()
master_introns %>% dplyr::count(misspliced)
## Collectively, we detected 3,865,268 unique novel junctions, equating to 14 novel junctions per annotated intron.
master_novel_junctions %>% nrow()
master_novel_junctions %>% dplyr::count(novel_type)
(master_novel_junctions %>% nrow()) / (master_introns %>% filter(misspliced==1) %>% nrow())
## originating from 32,026 genes and 201,541 transcripts, and covering 13,949 different human samples and 40 human tissues
master_genes %>% distinct(gene_id) %>% nrow()
master_transcripts %>% nrow()
## Novel junctions exceed in X fold to annotated introns
(master_novel_junctions %>% distinct(novel_junID) %>% nrow()) / (master_introns %>% distinct(ref_junID) %>% nrow())
## Percentage of mis-spliced introns
(((master_introns %>%
dplyr::count(misspliced) %>%
filter(misspliced == 1) %>%
pull(n)) * 100 )) /
((master_introns %>% distinct(ref_junID) %>% nrow()) %>%
round(digits = 1))
## Collectively, we detected X novel junctions
master_novel_junctions %>% distinct(novel_junID) %>% nrow()
## equating to 14 novel junctions per an annotated junction.
((master_novel_junctions %>% distinct(novel_junID) %>% nrow()) / (master_introns %>%
dplyr::count(misspliced) %>%
filter(misspliced == 1) %>%
pull(n))) %>%
round()
}
## SECTION 2 - MAIN FIGURES ---------------------------------------
## 2. Over 98% of novel donor and acceptor junctions are likely to be generated through splicing errors
main_figure2 <- function () {
results_file <- file.path(args$data_folder, "figure2_a.csv")
if (file.exists(results_file)) {
df_reclassification_rates_tidy <- read_csv(file = results_file, col_names = T, show_col_types = F)
} else {
reclassification_file <- file.path(args$results_folder, "/reclassification_rates.rds")
if (!file.exists(reclassification_file)) {
df_reclassification_rates <- map_df(all_projects, function(project_id) {
# project_id <- all_projects[1]
print(paste0(Sys.time(), " - ", project_id))
all_clusters <- df_metadata %>% filter(SRA_project == project_id) %>% distinct(cluster) %>% pull()
versions <- c("97")
dates <- c("26-May-2019")
map_df(all_clusters, function(cluster) {
# cluster <- all_clusters[1]
print(paste0(cluster))
map_df(versions, function(version) {
# version <- versions[1]
print(paste0("v", version))
if ( file.exists(file.path(base_folder, "results", main_project, version, project_id, "base_data",
paste0(project_id, "_", cluster, "_all_split_reads.rds"))) ) {
## ENSEMBL v97
df_ensembl97 <- readRDS(file = file.path(base_folder, "results", main_project, version, project_id, "base_data",
paste0(project_id, "_", cluster, "_all_split_reads.rds")) ) %>% as_tibble()
df_ensembl97_introns <- df_ensembl97 %>% filter(type == "annotated")
df_ensembl97_novel <- df_ensembl97 %>% filter(type %in% c("novel_donor", "novel_acceptor"))
## ENSEMBL v105
df_ensembl105 <- readRDS(file = file.path(base_folder, "results", main_project, "105", project_id, "base_data",
paste0(project_id, "_", cluster, "_all_split_reads.rds")) ) %>% as_tibble()
df_ensembl105_introns <- df_ensembl105 %>% filter(type == "annotated")
df_ensembl105_novel <- df_ensembl105 %>% filter(type %in% c("novel_donor", "novel_acceptor"))
rm(df_ensembl97)
rm(df_ensembl105)
## NOVEL JUNCTIONS THAT KEPT ANNOTATION CATEGORY
kept_annotation <- df_ensembl97_novel %>%
filter(junID %in% df_ensembl105_novel$junID) %>%
distinct(junID, .keep_all = T)
kept_annotation %>% nrow() %>% print()
## NOVEL JUNCTIONS THAT ENTERED ANNOTATION
in_annotation <- df_ensembl97_novel %>%
filter(junID %in% df_ensembl105_introns$junID) %>%
distinct(junID, .keep_all = T)
in_annotation %>% nrow() %>% print()
label <- paste0("Ensembl_v", version, " (", dates, ")")
# (in_annotation %>% nrow()) / (in_annotation %>% nrow() +
# keep_annotation %>% nrow()) * 100
return(data.frame(tissue = cluster,
reclassification_rates = label,
kept_annotation = kept_annotation %>% nrow() / df_ensembl97_novel %>% nrow(),
in_annotation = in_annotation %>% nrow() / df_ensembl97_novel %>% nrow() ))
} else {
return(NULL)
}
})
})
})
saveRDS(object = df_reclassification_rates, file = reclassification_file)
} else {
df_reclassification_rates <- readRDS(file = reclassification_file)
}
## We found that across all tissues, on average only 0.008 [0.005,0.012] of junctions defined as novel donor or acceptor
## junctions using v97 were reclassified as annotated introns in v105, and thus part of a transcript structure (Fig.2a)
## Stats - all tissues
df_reclassification_rates %>% pull(in_annotation) %>% min
df_reclassification_rates %>% pull(in_annotation) %>% max
df_reclassification_rates %>% pull(in_annotation) %>% mean
## Interestingly, we noted that the highest re-classification rates were observed amongst human brain tissues,
## on average 0.009 [0.008,0.012].
## Stats - only brain
df_reclassification_rates %>% filter(str_detect(tissue, pattern = "Brain")) %>% pull(in_annotation) %>% mean
df_reclassification_rates %>% filter(str_detect(tissue, pattern = "Brain")) %>% pull(in_annotation) %>% max
df_reclassification_rates %>% filter(str_detect(tissue, pattern = "Brain")) %>% pull(in_annotation) %>% min
## Prepare the dataframe before plotting it
df_reclassification_rates_tidy <- df_reclassification_rates %>%
dplyr::select(kept_annotation, in_annotation, tissue) %>%
tidyr::gather(key = "type", value = "prop", -tissue )
df_reclassification_rates_tidy$type = factor(df_reclassification_rates_tidy$type, levels = c( "kept_annotation", "in_annotation" ) )
df_reclassification_rates_tidy = df_reclassification_rates_tidy %>%
ungroup() %>% arrange(type , prop) %>% mutate(tissue = fct_inorder(tissue))
write.csv(x = df_reclassification_rates_tidy,
file = file.path(args$data_folder, "figure2_a.csv"), row.names = F)
}
###################################
## RECLASSIFICATION RATES BAR PLOT
##################################
#
#
# df_reclassification_rates_tidy <- df_reclassification_rates_tidy %>%
# filter(!(tissue %in% c("Brain - Cortex", "Brain - Cerebellum"))) %>%
# arrange(type, desc(prop)) %>%
# mutate(tissue = as.factor(x = tissue))
#
# # Create factor column with decreasing order TRUE
# df_reclassification_rates_tidy$tissue <- factor(df_reclassification_rates_tidy$tissue,
# levels = df_reclassification_rates_tidy$tissue[order(df_reclassification_rates_tidy$prop, decreasing = TRUE)])
#
#
df_reclassification_rates_tidy <- df_reclassification_rates_tidy %>%
arrange(type, desc(prop)) %>% # First by 'type', then by 'prop' descending
mutate(tissue = factor(tissue, levels = unique(tissue))) # Reorder tissue
df_reclassification_rates_tidy$type = factor(df_reclassification_rates_tidy$type, levels = c("kept_annotation", "in_annotation"))
ggplot(data = df_reclassification_rates_tidy,
aes(x = tissue, y = prop, fill = type)) +
geom_bar(stat = "identity", position = "stack") +
ggforce::facet_zoom(ylim = c(0,0.013), zoom.size = 3) +
ylab("Ratio of unique novel junctions") +
xlab("") +
scale_fill_manual(values = c( "#a6a6a6", "#1a1a1a" ),
breaks = c( "kept_annotation", "in_annotation" ),
labels = c( "Novel junctions mantaining category", "Novel junctions entering annotation" )) +
theme_light() +
custom_ggtheme +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
guides(fill = guide_legend(title = NULL, ncol = 2, nrow = 1)) %>%
return()
ggplot2::ggsave(file.path(args$figures_folder, "main_figure2.png"), width = 180, height = 100, units = "mm", dpi = 300)
ggplot2::ggsave(file.path(args$figures_folder, "main_figure2.svg"), width = 180, height = 100, units = "mm", dpi = 300)
}
main_figure2_b <- function() {
##################################################
## Proportion of unique junctions per GTEx tissue
##################################################
results_file <- file.path(args$data_folder, "figure2_b.csv")
if ( file.exists(results_file) ) {
df_proportions_violin <- read_csv(file = results_file, col_names = T)
} else {
file_name <- file.path(args$results_folder, "/unique_donor_acceptor_jxn.rds")
if ( !file.exists(file_name) ) {
df_proportions <- map_df(all_projects, function(project_id) {
# project_id <- all_projects[1]
print(paste0(Sys.time(), " - ", project_id))
all_clusters <- df_metadata %>%
filter(SRA_project == project_id) %>%
distinct(cluster) %>%
pull()
map_df(all_clusters, function(cluster_id) {
# cluster_id <- all_clusters[1]
## Print the tissue
print(paste0(cluster_id))
samples <- df_metadata %>%
dplyr::count(cluster) %>%
filter(cluster == cluster_id) %>%
pull(n)
####################
## GET THE INTRONS
####################
query <- paste0("SELECT DISTINCT ref_junID FROM '", cluster_id, "_", project_id, "_nevermisspliced'")
introns <- dbGetQuery(con, query) %>% as_tibble()
query <- paste0("SELECT DISTINCT ref_junID FROM '", cluster_id, "_", project_id, "_misspliced'")
introns <- rbind(introns, dbGetQuery(con, query) %>% as_tibble())
###########################
## GET THE NOVEL JUNCTIONS
###########################
query <- paste0("SELECT DISTINCT novel_junID FROM '", cluster_id, "_", project_id, "_misspliced'")
novel_junctions <- dbGetQuery(con, query) %>% as_tibble() %>%
inner_join(y = master_novel_junctions,
by = "novel_junID")
introns %>% head()
novel_junctions %>% head()
######################################
## Calculate proportion of unique jxn
######################################
annotated_junc <- introns %>% distinct(ref_junID) %>% nrow()
donor_junc <- novel_junctions %>% filter(novel_type == "novel_donor") %>% distinct(novel_junID) %>% nrow()
acceptor_junc <- novel_junctions %>% filter(novel_type == "novel_acceptor") %>% distinct(novel_junID) %>% nrow()
annotated_prop <- annotated_junc/(annotated_junc + donor_junc + acceptor_junc)
donor_prop <- donor_junc/(annotated_junc + donor_junc + acceptor_junc)
acceptor_prop <- acceptor_junc/(annotated_junc + donor_junc + acceptor_junc)
## Return the data.frame
return(data.frame(tissue = cluster_id,
annotated_junc = annotated_junc ,
donor_junc = donor_junc,
acceptor_junc = acceptor_junc,
annotated_prop = annotated_prop,
donor_prop = donor_prop,
acceptor_prop = acceptor_prop,
samples = samples))
})
})
## Save results --------------------------------------------------------------------------
saveRDS(object = df_proportions, file = file_name)
write.csv(x = df_proportions, file = paste0(args$results_folder, "unique_donor_acceptor_jxn.csv"), row.names = F)
} else {
df_proportions <- readRDS(file = file.path(args$results_folder, "unique_donor_acceptor_jxn.rds"))
}
###################
## STATS
###################
## We found that novel donor and acceptor junctions consistently accounted for
## the majority of unique junctions detected (X%, range),
(df_proportions %>%
pull(donor_prop) %>%
mean() + df_proportions %>%
pull(acceptor_prop) %>%
mean()) * 100
df_proportions %>%
mutate(novel_prop = (donor_prop + acceptor_prop) * 100) %>%
arrange(desc(novel_prop))
## While we detected an average of 241,044 unique annotated junctions across all tissues,
## unique novel donor and acceptor junctions averaged 249,366 and 360,400 respectively.
df_proportions %>%
pull(annotated_junc) %>%
mean
df_proportions %>%
pull(donor_junc) %>%
mean
df_proportions %>%
pull(acceptor_junc) %>%
mean
## Prepare data to plot
df_proportions <- df_proportions %>%
dplyr::select(tissue,
donor = donor_prop,
acceptor = acceptor_prop,
annotated_intron = annotated_prop) %>%
tidyr::gather(key = "type", value = "prop", -tissue )
df_proportions_violin <- df_proportions %>%
filter(type != "annotated_intron") %>%
mutate(prop = prop * 100)
df_proportions_violin$type = factor(df_proportions_violin$type,
levels = c("donor","acceptor"))
## Save source data
write_csv(x = df_proportions_violin, file = file.path(args$data_folder, "figure2_b.csv"), col_names = T)
}
######################
## VIOLIN PLOT
######################
## PLOT
junx_violin_plot <- ggplot(df_proportions_violin, aes(type, prop, fill = type)) +
geom_violin(trim = FALSE) +
geom_point(size = 0.5, colour = "#333333") +
geom_line( aes(group = tissue), colour = "#333333",linewidth = .2 ) +
theme_light() +
ylab("% unique junctions") +
xlab("") +
theme(axis.line = element_line(colour = "black"),
text = element_text(colour = "black", size = 12),
legend.position = "top") +
scale_x_discrete( breaks = c( "acceptor", "donor"),# "annotated_intron"),
labels = c( "Novel Acceptor", "Novel Donor")) +
scale_fill_manual(values = c( "#35B779FF", "#8d03b0"),
breaks = c( "donor", "acceptor"),
labels = c( "Novel Donor", "Novel Acceptor")) +
guides(fill = guide_legend(title = NULL,
ncol = 2,
nrow = 1)) +
custom_ggtheme +
theme(legend.position = "right")
junx_violin_plot
## CALL THE FUNCTION 'get_unique_donor_acceptor_reads' TO OBTAIN THE CUMULATIVE NUMBER OF READS
## NEEDED TO CREATE A GGARANGE PLOT WITH A AND B GRAPHS
reads_violin_plot <- main_figure2_c()
ggpubr::ggarrange(junx_violin_plot, reads_violin_plot, common.legend = T, labels = c("b", "c"), align = "h", ncol = 2, nrow = 1)
file_name <- paste0(args$figures_folder, "/main_figure2bc")
ggplot2::ggsave(filename = paste0(file_name,".png"), width = 180, height = 90, units = "mm", dpi = 300)
ggplot2::ggsave(filename = paste0(file_name,".svg"), width = 180, height = 90, units = "mm", dpi = 300)
}
main_figure2_c <- function() {
results_file <- file.path(args$data_folder, "figure2_c.csv")
if ( file.exists(results_file) ) {
df_mean_counts_violin <- read_csv(file = results_file, col_names = T)
} else {
file_name <- file.path(args$results_folder, "unique_donor_acceptor_reads.rds")
if ( !file.exists(file_name) ) {
df_mean_counts <- map_df(all_projects, function(project_id) {
print(paste0(Sys.time(), " - ", project_id))
## GET THE CLUSTERS
all_clusters <- df_metadata %>%
filter(SRA_project == project_id) %>%
distinct(cluster) %>%
pull()
map_df(all_clusters, function(cluster_id) {
# project_id <- "BRAIN"
# cluster_id <- "Brain - Frontal Cortex (BA9)"
print(cluster_id)
samples <- df_metadata %>%
dplyr::count(cluster) %>%
filter(cluster == cluster_id) %>%
pull(n)
####################
## GET THE INTRONS
####################
query <- paste0("SELECT DISTINCT ref_junID, ref_sum_counts, ref_n_individuals FROM '",
cluster_id, "_", project_id, "_nevermisspliced'")
introns <- dbGetQuery(con, query) %>% as_tibble()
query <- paste0("SELECT DISTINCT ref_junID, ref_sum_counts, ref_n_individuals FROM '",
cluster_id, "_", project_id, "_misspliced'")
introns <- rbind(introns, dbGetQuery(con, query) %>% as_tibble())
###########################
## GET THE NOVEL JUNCTIONS
###########################
query <- paste0("SELECT novel_junID, novel_sum_counts, novel_n_individuals
FROM '", cluster_id, "_", project_id, "_misspliced'")
novel_junctions <- dbGetQuery(con, query) %>% as_tibble()
novel_junctions <- novel_junctions %>%
left_join(y = master_novel_junctions %>% dplyr::select(novel_junID, novel_type),
by = "novel_junID") %>%
as_tibble()
###########################
## GET THE PROPORTIONS
###########################
annotated <- introns %>%
dplyr::distinct(ref_junID, .keep_all = T) %>%
pull(ref_sum_counts) %>%
sum()
acceptor <- novel_junctions %>%
filter(novel_type == "novel_acceptor") %>%
dplyr::distinct(novel_junID, .keep_all = T) %>%
pull(novel_sum_counts) %>%
sum()
donor <- novel_junctions %>%
filter(novel_type == "novel_donor") %>%
dplyr::distinct(novel_junID, .keep_all = T) %>%
pull(novel_sum_counts) %>%
sum()
annotated_p = annotated * 100 / (annotated + acceptor + donor)
acceptor_p = acceptor * 100 / (annotated + acceptor + donor)
donor_p = donor * 100 / (annotated + acceptor + donor)
return(data.frame(tissue = cluster_id,
samples = samples,
type = c("annotated","acceptor", "donor"),
prop_counts = c(annotated_p, acceptor_p, donor_p),
sum_counts = c(annotated, acceptor, donor)))
})
})
# ## Save data
saveRDS(object = df_mean_counts, file = file_name)
} else {
df_mean_counts <- readRDS(file = file_name) %>% as_tibble()
}
###################
## SOME STATS
###################
## we found that novel donor and acceptor junctions together accounted
## for only 0.3 – 0.4% of all junction reads whereas annotated junctions
## accounted for 99.4% - X% of junction reads across all tissues evaluated
df_mean_counts %>%
filter(type == "annotated") %>%
pull(prop_counts ) %>%
sort()
df_mean_counts %>%
filter(type != "annotated") %>%
group_by(tissue) %>%
mutate(sum_prop_counts = prop_counts %>% sum()) %>%
ungroup() %>%
distinct(tissue, .keep_all = T) %>%
pull(sum_prop_counts) %>%
sort()
df_mean_counts %>%
filter(tissue == "Brain - Frontal Cortex (BA9)")
########################
## PREPARE DATA TO PLOT
########################
df_mean_counts_violin <- df_mean_counts %>%
filter(type != "annotated")
df_mean_counts_violin$type = factor(df_mean_counts_violin$type,
levels = c("donor", "acceptor"))
## Save source data
write_csv(x = df_mean_counts_violin, file = file.path(args$data_folder, "figure2_c.csv"), col_names = T)
}
######################
## VIOLIN PLOT
######################
## Plot
reads_violin_plot <- ggplot(df_mean_counts_violin, aes(type, prop_counts, fill = type)) +
geom_violin(trim = FALSE) +
geom_point(size = 0.5, colour = "#333333") +
geom_line( aes(group = tissue), colour = "#333333",linewidth = .2 ) +
theme_light() +
ylab("% cumulative read counts") +
xlab("") +
theme(axis.line = element_line(colour = "black"),
text = element_text(colour = "black", size = 12),
legend.position = "top") +
scale_x_discrete( breaks = c( "acceptor", "donor"),# "annotated_intron"),
labels = c( "Novel Acceptor", "Novel Donor")) +
scale_fill_manual(values = c( "#35B779FF", "#8d03b0"),
breaks = c( "donor", "acceptor"),
labels = c( "Novel Donor", "Novel Acceptor")) +
guides(fill = guide_legend(title = NULL, ncol = 2, nrow = 1))+
custom_ggtheme +
theme(legend.position = "right")
reads_violin_plot %>%
return()
}
## 4. High sequence similarity between novel splice sites and their annotated pairs explains splicing errors
main_figure3_ab <- function() {
file_name <- file.path(args$results_folder, "df_mes.rds")
df_mes <- if ( file.exists(file_name) ) {
print("Loading MES results...")
readRDS(file = file_name) %>% distinct(ref_junID, novel_junID, .keep_all = T) %>% as_tibble()
} else {
###########################
## GET THE MES
###########################
## Get the MES scores corresponding to the mis-spliced MASTER introns
introns <- master_introns %>%
filter(misspliced == 1) %>%
dplyr::select(ref_junID, ref_mes5ss, ref_mes3ss)
## Get the MASTER novel junctions
novel_junctions <- master_novel_junctions %>%
dplyr::select(ref_junID, novel_junID, novel_type, novel_mes5ss, novel_mes3ss) %>%
as_tibble()
###########################
## MERGE THE DATA AND RETURN
###########################
df_mes <- novel_junctions %>%
left_join(y = introns, by = "ref_junID") %>%
mutate(diff_ss5score = ref_mes5ss - novel_mes5ss,
diff_ss3score = ref_mes3ss - novel_mes3ss)
saveRDS(object = df_mes %>% distinct(ref_junID, novel_junID, .keep_all = T) %>% as_tibble(),
file = file_name)
}
##########################
## DATA MAIN FIGURE
##########################
df_delta_5ss <- df_mes %>%
filter(novel_type == "novel_donor") %>%
dplyr::select(diff_ss5score) %>%
tidyr::gather(key = "type", value = "MES")
df_delta_3ss <- df_mes %>%
filter(novel_type == "novel_acceptor") %>%
dplyr::select(diff_ss3score) %>%
tidyr::gather(key = "type", value = "MES")
# Save source data
write_csv(x = df_delta_5ss, file = file.path(args$data_folder, "figure3_a.csv"), col_names = T)
write_csv(x = df_delta_3ss, file = file.path(args$data_folder, "figure3_b.csv"), col_names = T)
##################################
## PLOT MAIN FIGURE 3 (a and b)
##################################
generate_plot <- function(df, breaks_label, label_text) {
colour <- if (breaks_label == "diff_ss5score") { "#35B779FF" } else { "#8d03b0"}
ggplot(data = df) +
geom_density(mapping = aes(x = MES, fill = type)) +
geom_vline(xintercept = 0) +
xlab("Delta MES") +
xlim(c(-40, 65)) +
ylim(c(0, 0.11)) +
theme_light() +
scale_color_viridis_d() +
scale_fill_manual(values = colour, breaks = breaks_label, labels = label_text) +
custom_ggtheme +
guides(fill = guide_legend(title = NULL, ncol = 2, nrow = 1))
}
## Plot
deltaplot5ss <- generate_plot(df = df_delta_5ss, breaks_label = "diff_ss5score", label_text = "Delta MES Donor")
deltaplot3ss <- generate_plot(df = df_delta_3ss, breaks_label = "diff_ss3score", label_text = "Delta MES Acceptor")
ggpubr::ggarrange(deltaplot5ss, deltaplot3ss, labels = c("a", "b"), ncol = 2, nrow = 1)
figure_name = file.path(args$figures_folder, "main_figure3_ab")
ggplot2::ggsave(filename = paste0(figure_name, ".png"), width = 180, height = 50, units = "mm", dpi = 300)
ggplot2::ggsave(filename = paste0(figure_name, ".svg"), width = 180, height = 50, units = "mm", dpi = 300)
##########################
## STATS FOR THE PAPER
##########################
## "[...] As would be expected, we found that the majority of novel 5’ and 3’ splice sites were weaker than the corresponding annotated site with 82.5% of novel 5’..."
((df_mes %>%
filter(novel_type == "novel_donor") %>%
dplyr::select(intron_MES = ref_mes5ss, novel_donor_MES = novel_mes5ss) %>%
mutate(MES_diff = intron_MES - novel_donor_MES) %>%
pull(MES_diff) %>%
sign() %>%
table() %>%
as.data.frame() %>%
filter(. != -1) %>%
pull(Freq) %>%
sum) * 100) / ( master_novel_junctions %>%
dplyr::count(novel_type) %>%
filter(novel_type == "novel_donor") %>%
pull(n) )
## "[...] and 85.2% of novel 3’ sites having positive delta MES scores [...]"
((df_mes %>%
filter(novel_type == "novel_acceptor") %>%
dplyr::select(intron_MES = ref_mes5ss, novel_acceptor_MES = novel_mes3ss) %>%
mutate(MES_diff = intron_MES - novel_acceptor_MES) %>%
pull(MES_diff) %>%
sign() %>%
table() %>% as.data.frame() %>%
filter(. != -1) %>%
pull(Freq) %>%
sum) * 100) / ( master_novel_junctions %>%
dplyr::count(novel_type) %>%
filter(novel_type == "novel_acceptor") %>%
pull(n) )
## "[...] Interestingly, this analysis demonstrated that novel 5’ splice sites had a modal delta value which was very close to zero "
df_mes %>%
filter(novel_type == "novel_donor") %>%
dplyr::select(intron_MES = ref_mes5ss, novel_donor_MES = novel_mes5ss) %>%
mutate(MES_diff = intron_MES - novel_donor_MES) %>%
pull(MES_diff) %>%
get_mode()
df_mes %>%
filter(novel_type == "novel_donor") %>%
dplyr::select(intron_MES = ref_mes5ss, novel_donor_MES = novel_mes5ss) %>%
mutate(MES_diff = intron_MES - novel_donor_MES) %>%
pull(MES_diff) %>%
summary()
## "[...] The modal delta value for novel 3’ splice sites was higher "
df_mes %>%
filter(novel_type == "novel_acceptor") %>%
dplyr::select(intron_MES = ref_mes3ss, novel_acceptor_MES = novel_mes3ss) %>%
mutate(MES_diff = intron_MES - novel_acceptor_MES) %>%
pull(MES_diff) %>%
get_mode()
df_mes %>%
filter(novel_type == "novel_acceptor") %>%
dplyr::select(intron_MES = ref_mes3ss, novel_acceptor_MES = novel_mes3ss) %>%
mutate(MES_diff = intron_MES - novel_acceptor_MES) %>%
pull(MES_diff) %>%
summary()
df_delta_3ss$MES %>% min
df_delta_3ss$MES %>% max
}
main_figure3_cd <- function() {
limit_bp <- 30
subsample = F
results_file1 <- file.path(args$data_folder, "figure3_c.csv")
results_file2 <- file.path(args$data_folder, "figure3_d.csv")
if (file.exists(results_file1) && file.exists(results_file2)) {
df_figure3_c <- read_csv(file = results_file1, col_names = T, show_col_types = F)
df_figure3_d <- read_csv(file = results_file2, col_names = T, show_col_types = F)
} else {
###############################
## GET DATA FOR FRONTAL CORTEX
###############################
project_id <- "BRAIN"
cluster_id <- "Brain - Frontal Cortex (BA9)"
query <- paste0("SELECT tissue.novel_junID, tissue.ref_junID, tissue.ref_sum_counts, tissue.ref_n_individuals, novel.novel_type, novel.distance
FROM '", cluster_id, "_", project_id, "_misspliced' AS tissue
INNER JOIN 'novel' ON novel.novel_junID = tissue.novel_junID")
db_misspliced_introns <- dbGetQuery(con, query) %>% as_tibble()
###############################
## PREPARE DATA BEFORE PLOT
###############################
df_novel_tidy <- db_misspliced_introns %>%
inner_join(master_introns %>% dplyr::select(ref_junID, protein_coding), by = "ref_junID") %>%
filter(protein_coding %in% c(0,100)) %>%
mutate(type_PC = ifelse(protein_coding == 100, "protein coding (PC)", "non PC"),
novel_type = str_replace(string = novel_type, pattern = "_", replacement = " "),
novel_type = str_to_title(novel_type))
df_novel_tidy$novel_type = factor(df_novel_tidy$novel_type, levels = c("Novel Donor", "Novel Acceptor"))
df_novel_tidy$type_PC = factor(df_novel_tidy$type_PC, levels = c("protein coding (PC)", "non PC"))
if (subsample) {
set.seed(100)
df_novel_tidy_w_expression <- df_novel_tidy %>%
group_by(type_PC) %>%
distinct(ref_junID, .keep_all = T) %>%
mutate(mean_expression = log10(ref_sum_counts/ref_n_individuals)) %>%
ungroup()
## Subsampling introns to control by similarity in mean read coverage
m.out <- MatchIt::matchit(type_PC ~ mean_expression,
data = df_novel_tidy_w_expression %>% dplyr::select(-distance),
distance = df_novel_tidy_w_expression$mean_expression,
method = "nearest",
caliper = c(mean_expression = 0.005),
std.caliper = FALSE)
data_subsample <- MatchIt::match.data(m.out)
## Only consider matched annotated introns (i.e. introns with similar expression levels in protein-coding and non-coding transcripts)
df_novel_tidy <- df_novel_tidy %>% dplyr::select(ref_junID, distance) %>%
inner_join(y = data_subsample %>% dplyr::select(-c(distance, weights)), by = "ref_junID")
}
df_novel_tidy %>% distinct(ref_junID, .keep_all = T) %>% filter(abs(distance) <= limit_bp) %>% dplyr::count(type_PC)
df_novel_tidy %>% distinct(novel_junID, .keep_all = T) %>% filter(abs(distance) <= limit_bp) %>% dplyr::count(type_PC)
df_figure3_c = rbind(df_novel_tidy %>% filter(type_PC == "protein coding (PC)", novel_type == "Novel Donor"),
df_novel_tidy %>% filter(type_PC == "protein coding (PC)", novel_type == "Novel Acceptor"))
df_figure3_d = rbind(df_novel_tidy %>% filter(type_PC == "non PC", novel_type == "Novel Donor"),
df_novel_tidy %>% filter(type_PC == "non PC", novel_type == "Novel Acceptor"))
# Save source data
write_csv(x = df_figure3_c, file = file.path(args$data_folder, "figure3_c.csv"), col_names = T)
# Save source data
write_csv(x = df_figure3_d, file = file.path(args$data_folder, "figure3_d.csv"), col_names = T)
}
#####################################
## FIGURES EXON AND INTRON DRAWS
#####################################
distance_rectangle_donor <- ggplot() +
geom_rect(aes(xmin = 0, xmax = limit_bp, ymin = 49, ymax = 51),
fill = "grey", alpha = 1, color = "black") +
geom_text(aes(x = 15, y = 80), size = 2.5, label = "intron") +
geom_rect(aes(xmin = (limit_bp * -1), xmax = 0, ymin = 1, ymax = 100),
fill = "grey", color = "black") +
geom_text(aes(x = -15, y = 55), size = 2.5, label = "exon") +
theme_void()
distance_rectangle_acceptor <- ggplot() +
geom_rect(aes(xmin = 0, xmax = limit_bp, ymin = 1, ymax = 100),
fill = "grey", color = "black") +
geom_text(aes(x = 15, y = 55), size = 2.5, label = "exon") +
geom_rect(aes(xmin = (limit_bp * -1), xmax = 0, ymin = 49, ymax = 51),
fill = "grey", alpha = 1, color = "black") +