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manuscript_AD_figures.R
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manuscript_AD_figures.R
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library(tidyverse)
library(GenomicRanges)
library(DBI)
library(doParallel)
####################################################
## CONNECT TO THE SPLICING DATABASE ################
####################################################
## source("/home/sruiz/PROJECTS/splicing-accuracy-manuscript/manuscript_AD_figures.R")
## CONNECT TO THE DATABASE ------------------------------
base_folder <- here::here()
# base_folder <- "/mnt/PROJECTS/splicing-accuracy-manuscript"
gtf_version <- 105
main_project_identifier <- "SRP100948"
supportive_reads <- 1
data_subsample <- F
#project_name <- paste0(main_project_identifier, "_", supportive_reads, "read_subsample", data_subsample) #SRP100948_
project_name <- paste0(main_project_identifier, "_", supportive_reads, "read")
args <-
list(
project_id = main_project_identifier,
case_type = "AD",
control_type = "control",
database_folder = file.path(base_folder, "database", project_name, gtf_version),
results_folder = file.path(base_folder, "results", project_name, gtf_version, "_paper_review", "results"),
figures_folder = file.path(base_folder, "results", project_name, gtf_version, "_paper_review", "figures"),
data_folder = file.path(base_folder, "results", project_name, 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$data_folder), recursive = TRUE, showWarnings = F)
database_path <- file.path(args$database_folder, "/", paste0(project_name, ".sqlite"))
con <- dbConnect(RSQLite::SQLite(), database_path)
dbListTables(con)
## 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$SRA_project %>% unique
query <- paste0("SELECT * FROM 'intron'")
master_introns <- dbGetQuery(con, query) %>% as_tibble()
query <- paste0("SELECT * FROM 'novel'")
master_novel_junctions <- dbGetQuery(con, query) %>% as_tibble()
query <- paste0("SELECT * FROM 'transcript'")
master_transcripts <- dbGetQuery(con, query) %>% as_tibble()
query <- paste0("SELECT * FROM 'gene'")
master_gene <- dbGetQuery(con, query) %>% as_tibble()
all_projects <- df_metadata$SRA_project %>% unique
all_clusters <- df_metadata$cluster %>% unique()
## UTILS
get_mode <- function(data) {
uniqv <- unique(data)
uniqv[which.max(tabulate(match(data, uniqv)))]
}
custom_ggtheme <- theme(text = element_text(size = 9, family="Arial", colour = "black"),
axis.ticks = element_line(colour = "black"),
axis.text = element_text(size = 9, family="Arial", colour = "black"),
axis.line = element_line(colour = "black"),
axis.title = element_text(size = 9, family="Arial", colour = "black"),
axis.text.y = element_text(size = 9, family="Arial", colour = "black"),
axis.text.x = element_text(size = 9, family="Arial", colour = "black", hjust = 0.5, vjust = 0.5),
strip.text = element_text(size = 9, family="Arial", colour = "black"),
legend.text = element_text(size = 8, family="Arial", colour = "black"),
legend.position = "top",
legend.box = "vertical")
########################################
## FUNCTIONS
########################################
## SECTION 1 - PAPER FIGURES -----------------------------------------------------------------
## Main figures
#' Title
#' Visualise differences in number of unique novel junctions from common introns from AD vs control samples
#' Only using common, subsampled introns
#' @return
#' @export
#'
#' @examples
main_figure8_a <- function() {
## Load common introns
common_introns_subsample <- readRDS(file = file.path(args$results_folder, "/common_subsampled_introns_seed1000.rds"))
common_introns_subsample %>% dplyr::count(sample_type)
df_unique_junctions <- map_df(c(args$case_type,args$control_type), function(cluster_id) {
# cluster_id <- all_clusters[1]
print(cluster_id)
####################
## GET THE INTRONS
####################
query <- paste0("SELECT DISTINCT ref_junID FROM '", cluster_id, "_", args$project_id, "_nevermisspliced'")
introns <- dbGetQuery(con, query) %>% as_tibble()
query <- paste0("SELECT DISTINCT ref_junID FROM '", cluster_id, "_", args$project_id, "_misspliced'")
introns <- rbind(introns, dbGetQuery(con, query) %>% as_tibble())
## Keep only common, subsampled introns
introns <- introns %>% filter(ref_junID %in% (common_introns_subsample %>% filter(sample_type == cluster_id) %>% pull(ref_junID)))
############################
## GET THE NOVEL JUNCTIONS
############################
query <- paste0("SELECT * FROM '", cluster_id, "_", args$project_id, "_misspliced'")
novel_junctions <- dbGetQuery(con, query) %>% as_tibble()
novel_junctions <- novel_junctions %>%
inner_join(y = master_novel_junctions %>% dplyr::select(novel_junID,novel_type), by = "novel_junID") %>%
filter(ref_junID %in% (common_introns_subsample %>% filter(sample_type == cluster_id) %>% pull(ref_junID)))
###########################
## GET THE PROPORTIONS
###########################
annotated_junc <- introns %>% distinct(ref_junID) %>% nrow()
donor_junc <- novel_junctions %>% filter(novel_type == "novel_donor") %>% distinct(novel_junID) %>% nrow()
acceptor_junc <- novel_junctions %>% filter(novel_type == "novel_acceptor") %>% distinct(novel_junID) %>% nrow()
annotated_prop <- annotated_junc/(annotated_junc + donor_junc + acceptor_junc)
donor_prop <- donor_junc/(annotated_junc + donor_junc + acceptor_junc)
acceptor_prop <- acceptor_junc/(annotated_junc + donor_junc + acceptor_junc)
## Return the data.frame
return(data.frame(cluster = cluster_id,
annotated_junc = annotated_junc ,
donor_junc = donor_junc,
acceptor_junc = acceptor_junc,
annotated_prop = annotated_prop,
donor_prop = donor_prop,
acceptor_prop = acceptor_prop))
})
df_unique_junctions_tidy <- df_unique_junctions %>%
dplyr::select(cluster, donor = donor_prop, acceptor = acceptor_prop, annotated_intron = annotated_prop) %>%
tidyr::gather(key = "type", value = "prop", -cluster ) %>%
mutate(prop = prop * 100)
## Save source data
write_csv(x = df_unique_junctions_tidy, file = file.path(args$data_folder, "figure8_a.csv"), col_names = T)
######################
## BAR PLOT
######################
ggplot(data = df_unique_junctions_tidy %>%
mutate(type = factor(type, levels = c("donor", "acceptor", "annotated_intron")),
prop_label = paste0(round(x = prop, digits = 2), "%")),
aes(x = cluster, y = prop, fill = type)) +
geom_bar(stat="identity", position = "stack")+
geom_text(aes(label=prop_label), vjust=2, position = "stack", color="white", size = 3, fontface = "bold") +
theme_light() +
ylab("% unique junctions") +
xlab("") +
theme(axis.line = element_line(colour = "black"),
legend.position = "top") +
scale_fill_manual(values = c( "#999999","#64037d", "#35B779FF"),
breaks = c("annotated_intron","acceptor", "donor" ),
label = c("Annotated","Acceptor", "Donor" )) +
guides(fill = guide_legend(title = "", ncol = 3, nrow = 1 )) +
custom_ggtheme +
theme( plot.margin = margin(t = 5, r = 5, l = 5, b = -5),
legend.box.margin=margin(b = -10, t = -5))#+
ggplot2::ggsave(file.path(args$figures_folder, "figure8_a.png"), width = 80, height = 70, units = "mm", dpi = 300)
ggplot2::ggsave(file.path(args$figures_folder, "figure8_a.svg"), width = 80, height = 70, units = "mm", dpi = 300)
}
#' Title
#' Visualise differences in the cummulative number of novel reads in common introns from AD vs control samples
#' Only using common, subsampled introns
#' @return
#' @export
#'
#' @examples
main_figure8_b <- function() {
## Load common introns
common_introns_subsample <- readRDS(file = file.path(args$results_folder, "/common_subsampled_introns_seed1000.rds"))
df_mean_counts <- map_df(all_clusters, function(cluster_id) {
# cluster_id <- all_clusters[1]
print(cluster_id)
####################
## GET EXPRESSION LEVELS FROM THE ANNOTATED INTRONS
####################
query <- paste0("SELECT DISTINCT ref_junID, ref_sum_counts, ref_n_individuals FROM '", cluster_id, "_", args$project_id, "_nevermisspliced'")
introns <- dbGetQuery(con, query) %>% as_tibble()
query <- paste0("SELECT DISTINCT ref_junID, ref_sum_counts, ref_n_individuals FROM '", cluster_id, "_", args$project_id, "_misspliced'")
introns <- rbind(introns, dbGetQuery(con, query) %>% as_tibble())
## Only common, subsampled introns
introns <- introns %>%
filter(ref_junID %in% (common_introns_subsample %>% filter(sample_type == cluster_id) %>% pull(ref_junID)))
###########################
## GET THE NOVEL JUNCTIONS
###########################
query <- paste0("SELECT ref_junID, novel_junID, novel_sum_counts, novel_n_individuals FROM '", cluster_id, "_", args$project_id, "_misspliced'")
novel_junctions <- dbGetQuery(con, query) %>% as_tibble()
novel_junctions <- novel_junctions %>%
inner_join(y = master_novel_junctions %>% dplyr::select(novel_junID, novel_type) %>% as_tibble(), by = "novel_junID") %>%
as_tibble() %>%
## Only NOVEL READS FROM common, subsampled introns
filter(ref_junID %in% introns$ref_junID)
###########################
## GET THE PROPORTIONS
###########################
annotated <- introns %>% dplyr::distinct(ref_junID, .keep_all = T) %>% pull(ref_sum_counts) %>% sum()
acceptor <- novel_junctions %>%
filter(novel_type == "novel_acceptor") %>%
dplyr::distinct(novel_junID, .keep_all = T) %>%
pull(novel_sum_counts) %>%
sum()
donor <- novel_junctions %>%
filter(novel_type == "novel_donor") %>%
dplyr::distinct(novel_junID, .keep_all = T) %>%
pull(novel_sum_counts) %>%
sum()
annotated_p = annotated * 100 / (annotated + acceptor + donor)
acceptor_p = acceptor * 100 / (annotated + acceptor + donor)
donor_p = donor * 100 / (annotated + acceptor + donor)
return(data.frame(cluster = cluster_id,
type = c("annotated","acceptor", "donor"),
prop = c(annotated_p, acceptor_p, donor_p),
sum_counts = c(annotated, acceptor, donor)))
})
df_mean_counts <- df_mean_counts %>% mutate(type = factor(type, levels = c("donor", "acceptor", "annotated")),
prop_label = paste0(round(x = prop, digits = 2), "%"))
## Save source data
write_csv(x = df_mean_counts, file = file.path(args$data_folder, "figure8_b.csv"), col_names = T)
######################
## BAR PLOT
######################
p <- ggplot(data = df_mean_counts, aes(x = cluster, y = prop, fill = type)) +
geom_bar(stat = "identity") +
ggforce::facet_zoom(ylim = c(97, 100)) +
geom_text(aes(label=prop_label), vjust = 1.3, position = "stack", color="white", size=3, fontface = "bold") +
theme_light() +
ylab("% cumulative split read counts") +
xlab("") +
custom_ggtheme +
theme(axis.line = element_line(colour = "black"), legend.position = "top") +
scale_fill_manual(values = c( "#999999","#64037d", "#35B779FF"),
breaks = c( "annotated","acceptor", "donor"),
label = c("Annotated","Acceptor", "Donor")) +
guides(fill = guide_legend(title = "", ncol = 3, nrow = 1 )) +
theme(plot.margin = margin(t = 5, r = 5, l = 5, b = -5), legend.box.margin=margin(b = -10, t = -5))
## Only annotated zoomed graph
pb <- ggplot_build(p)
pb$data[[2]][1:6, 'alpha'] <- 0
pg <- ggplot_gtable(pb)
## save plot
png(filename = file.path(args$figures_folder, "figure8_b.png"), width = 100, height = 70, units = "mm", res = 300)
plot(pg)
dev.off()
ggplot2::ggsave(file.path(args$figures_folder, "figure8_b.svg"), plot = pg, width = 100, height = 70, units = "mm", dpi = 300)
}
#' Title
#' Visualise differences in modulo3 values in introns from AD vs control samples
#' Only using common, subsampled introns
#' @return
#' @export
#'
#' @examples
main_figure8_c <- function() {
common_introns_subsample <- readRDS(file = file.path(args$results_folder, "/common_subsampled_introns_seed1000.rds"))
## Calculate modulo3 from MANE transcripts in 100bp distance
df_modulo <- map_df(all_clusters, function(cluster_id) {
# cluster_id <- all_clusters[1]
print(paste0(Sys.time(), " - ", cluster_id))
#####################################
## GET NOVEL JUNCTIONS
#####################################
query <- paste0("SELECT novel_junID FROM '", cluster_id, "_", args$project_id, "_misspliced'")
novel_jnx <- dbGetQuery(con, query)
query <- paste0("SELECT ref_junID, novel_junID, distance FROM 'novel' WHERE novel_junID IN (", paste(novel_jnx$novel_junID, collapse = ","),")")
novel_jnx <- novel_jnx %>% left_join(y = dbGetQuery(con, query), by = "novel_junID") %>% as_tibble()
#####################################
## GET INTRON AND TRANSCRIPT INFO
#####################################
## Add the transcript and MANE info
query <- paste0("SELECT intron.ref_junID, intron.protein_coding, transcript.MANE
FROM 'intron' INNER JOIN transcript ON intron.transcript_id = transcript.id
WHERE ref_junID IN (", paste(novel_jnx$ref_junID, collapse = ","),")")
all_jxn <- novel_jnx %>%
inner_join(y = dbGetQuery(con, query), by = "ref_junID") %>% as_tibble()
df_novel_tidy <- all_jxn %>%
distinct(novel_junID, .keep_all = T) %>%
filter(abs(distance) <= 100, MANE == 1, ref_junID %in% (common_introns_subsample$ref_junID)) %>%
mutate(modulo = abs(distance) %% 3)
df_novel_tidy %>% distinct(ref_junID) %>% nrow %>% print
df_novel_tidy <- df_novel_tidy %>%
group_by(modulo) %>%
summarise(n = n()) %>%
mutate(freq = n / sum(n)) %>%
mutate(cluster = cluster_id)
return(df_novel_tidy)
})
df_modulo_tidy <- df_modulo %>% mutate(freq = freq * 100)
df_modulo_tidy$modulo = factor(df_modulo_tidy$modulo, levels = c( "0", "1", "2"))
## Save Source Data
write_csv(x = df_modulo_tidy, file = file.path(args$data_folder, "figure8_c.csv"), col_names = T)
################
## BAR PLOT
################
ggplot(data = df_modulo_tidy %>%
mutate(freq_label = paste0(round(x = freq, digits = 2), "%"), modulo = factor(modulo, levels = c("2", "1", "0"))),
aes(x = cluster, y = freq, fill = modulo)) +
geom_bar(stat = "identity") +
xlab("") +
ylab("% of novel junctions") +
geom_text(aes(label=freq_label), vjust=2, position = "stack", color="white", size=3, fontface = "bold") +
ggsci::scale_fill_npg(breaks=c('0', '1', '2')) +
theme_light() +
custom_ggtheme +
guides(fill = guide_legend(title = "Modulo: ", ncol = 3, nrow = 1 )) +
theme(plot.margin = margin(t = 5, r = 5, l = 5, b = -3), legend.box.margin=margin(b = -10, t = -5))
ggplot2::ggsave(file.path(args$figures_folder, "figure8_c.png"), width = 70, height = 70, units = "mm", dpi = 300)
ggplot2::ggsave(file.path(args$figures_folder, "figure8_c.svg"), width = 70, height = 70, units = "mm", dpi = 300)
}
#' Title
#' GO, KEGG and REACTOME ENRICHMENT analysis of the introns showing increasing MSR values in AD compared to control samples
#' Only using common, subsampled introns
#' @return
#' @export
#'
#' @examples
main_figure8_d_e <- function() {
## Load common introns
common_introns_subsample <- readRDS(file = file.path(args$results_folder, "/common_subsampled_introns_seed1000.rds"))
#############################
# Select and arrange necessary columns
MSR_introns <- map(c("MSR_D", "MSR_A"), function(MSR_type) {
# MSR_type <- "MSR_D"
common_introns_subsample_MSR <- common_introns_subsample %>%
dplyr::select(MSR = all_of(MSR_type), subclass, sample_type) %>%
arrange(subclass)
# Filter and merge data for 'control' and 'AD' sample types in one step
common_introns_subsample_MSR_tidy <- common_introns_subsample_MSR %>%
filter(sample_type %in% c("control", "AD")) %>%
spread(key = sample_type, value = MSR) %>%
mutate(MSR_difference = AD - control)
# Separate introns based on MSR difference
introns_MSR_increasing <- common_introns_subsample_MSR_tidy %>%
filter(MSR_difference > 0)
introns_MSR_decreasing <- common_introns_subsample_MSR_tidy %>%
filter(MSR_difference < 0)
list(MSR_type, introns_increasing = introns_MSR_increasing, introns_decreasing = introns_MSR_decreasing)
})
#############################
## Get introns with increasing MSR values (these introns were also paired by similar expression levels)
MSR_D_introns_increasing <- MSR_introns[[1]]$introns_increasing %>%
left_join(y = common_introns_subsample %>% dplyr::select(ref_junID, subclass), by = "subclass")
MSR_A_introns_increasing <- MSR_introns[[2]]$introns_increasing %>%
left_join(y = common_introns_subsample %>% dplyr::select(ref_junID, subclass), by = "subclass")
MSR_introns_increasing <- rbind(MSR_D_introns_increasing, MSR_A_introns_increasing) %>% distinct(ref_junID)
## GET MASTER INTRON DATA
df_master_intron_tidy <- master_introns %>%
dplyr::select(ref_junID, transcript_id) %>%
inner_join(y = master_transcripts %>% dplyr::select(id, gene_id), by =c("transcript_id" = "id")) %>%
inner_join(y = master_gene %>% dplyr::select(id, gene_name, gene_id), by =c("gene_id" = "id"))
## Add gene name to introns with increasing MSR_A values in AD compared to control
genes_increasing_MSR <- MSR_introns_increasing %>%
inner_join(y = df_master_intron_tidy, by = c("ref_junID")) %>%
distinct(gene_name) %>% pull()
genes_increasing_MSR %>% length()
## Get gene background data
bg_genes <- common_introns_subsample %>% inner_join(y = df_master_intron_tidy, by =c("ref_junID")) %>% distinct(gene_name) %>% pull()
################################
## KEGG ENRICHMENT
################################
library('org.Hs.eg.db')
category_terms <- 50
entrez_genes_increasing <- AnnotationDbi::mapIds(org.Hs.eg.db::org.Hs.eg.db, genes_increasing_MSR, 'ENTREZID', 'SYMBOL')#
entrez_genes_bg <- AnnotationDbi::mapIds(org.Hs.eg.db::org.Hs.eg.db, bg_genes, 'ENTREZID', 'SYMBOL')
# mapIds(org.Hs.eg.db, introns_increasing_msra, 'ENTREZID', 'SYMBOL')
ekegg_MSR <- clusterProfiler::enrichKEGG(
gene = entrez_genes_increasing,
organism = "hsa",
keyType = "kegg",
universe = entrez_genes_bg,
pAdjustMethod = "fdr")
plotKEGG <- clusterProfiler::dotplot(ekegg_MSR %>% mutate(ONTOLOGY = "KEGG") %>%
filter(!(str_detect(string = Description, pattern = "tyrosine"))) %>%
filter(!(str_detect(string = Description, pattern = "Chemical carcino"))) %>%
filter(!(str_detect(string = Description, pattern = "Protein processing in"))) %>%
filter(!(str_detect(string = Description, pattern = "Amino sugar"))),
showCategory = category_terms,
split="ONTOLOGY")
plotKEGG +
scale_y_discrete(labels =
function(x) stringr::str_wrap(x, width = 40)) +
xlab("Gene Ratio") +
ggforce::facet_row(ONTOLOGY~., scales = "free", space = "free") +
#Coord_flip() +
custom_ggtheme +
theme(text = element_text(colour = "black", size = 7),
legend.position = "top",
legend.box="horizontal",
plot.margin = margin(t = 0, b =0, r = 5, 0),
legend.margin = margin(t = -5, b = -5, r = 5, l = 1),
legend.box.margin=margin(t = -10,b = -5, r = 0,l = 0)) +
scale_size(range = c(1, 5))+
guides(colour = guide_legend(title = "q: "),
size = guide_legend(title = "Gene count: "),
nrow = 2, ncol = 2 )
ggplot2::ggsave(file.path(args$figures_folder, "figure8_d.png"), width = 120, height = 70, units = "mm", dpi = 300)
## Save Source Data
write_csv(x = ekegg_MSR %>% as.data.frame(), file = file.path(args$data_folder, "figure8_d.csv"), col_names = T)
################################
## GO ENRICHMENT
################################
ego_MSR <- clusterProfiler::enrichGO(
gene = genes_increasing_MSR,
universe = bg_genes,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db", ##Genome wide annotation for Human, primarily based on mapping using Entrez Gene identifiers.
ont = "ALL",
pAdjustMethod = "fdr",
pvalueCutoff = 0.05,
qvalueCutoff = 0.05
)
category_terms <- 20
clusterProfiler::dotplot(ego_MSR %>% filter(ONTOLOGY == "CC"), x = "GeneRatio",
showCategory = category_terms, split="ONTOLOGY") +
scale_y_discrete(labels = function(x) stringr::str_wrap(x, width = 60)) +
xlab("Gene Ratio") +
ggforce::facet_col(ONTOLOGY~., scales = "free_y", space = "free") +
custom_ggtheme +
theme(legend.position = "top",legend.box = "horizontal",
plot.margin = margin(t = -5,b = 0,l = 5,r = 5),
legend.margin=margin(t = -5,b = -5, r = 5, 0),
legend.box.margin=margin(t = -5,r = 10,b = -5,l = 0)) +
scale_size(range = c(1, 5)) +
guides(size = guide_legend(title = "Gene Count: "), colour = guide_legend(title = "q: "))
ggplot2::ggsave(file.path(args$figures_folder, "figure8_e.png"), width = 180, height = 75, units = "mm", dpi = 300)
ggplot2::ggsave(file.path(args$figures_folder, "figure8_e.svg"), width = 180, height = 75, units = "mm", dpi = 300)
## Save Source Data
write_csv(x = ego_MSR %>% as.data.frame(), file = file.path(args$data_folder, "figure8_e.csv"), col_names = T)
################################
## DISEASE ENRICHMENT
################################
x <- DOSE::enrichDO(gene = entrez_genes_increasing,
ont = "DO",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
minGSSize = 10,
#universe = names(entrez_genes_bg),
qvalueCutoff = 0.05,
readable = F)
head(x)
}
#' Title
#' Test for differences in MSR_D and MSR_A between the introns from AD vs control samples.
#' Using one-tailed paired Wilcoxon test
#' @return
#' @export
#'
#' @examples
paper_stats_MSR <- function() {
common_introns_subsample <- readRDS(file = file.path(args$results_folder, "/common_subsampled_introns_seed1000.rds"))
## Pair introns by subsampling criteria
## donor
common_introns_subsample_MSRD <- common_introns_subsample %>%
dplyr::select(ref_junID, MSR_D, subclass, mean_coverage, sample_type) %>%
arrange(subclass)
## acceptor
common_introns_subsample_MSRA <- common_introns_subsample %>%
dplyr::select(ref_junID, MSR_A, subclass, mean_coverage, sample_type) %>%
arrange(subclass)
#########################################
## TEST DONOR
#########################################
wilcox.test(x = common_introns_subsample_MSRD %>% filter(sample_type == args$case_type) %>% pull(MSR_D),
y = common_introns_subsample_MSRD %>% filter(sample_type == args$control_type) %>% pull(MSR_D),
alternative = "greater", paired = T, correct = T)
rstatix::wilcox_effsize(data = common_introns_subsample_MSRD,
formula = MSR_D ~ sample_type,
paired = T)
########################################
## TEST ACCEPTOR
########################################
wilcox.test(x = common_introns_subsample_MSRA %>% filter(sample_type == args$case_type) %>% pull(MSR_A),
y = common_introns_subsample_MSRA %>% filter(sample_type == args$control_type) %>% pull(MSR_A),
alternative = "greater", paired = T, correct = T)
rstatix::wilcox_effsize(data = common_introns_subsample_MSRA, formula = MSR_A ~ sample_type, paired = T)
}
#' Title
#' Get an overview of the metadata of the project
#' @return
#' @export
#'
#' @examples
supplementary_figure27_a_to_d <- function() {
## Num samples
plot_num_samples <- ggplot(df_metadata %>% dplyr::count(SRA_project, cluster)) +
geom_bar(aes(x = n, y = SRA_project, fill = cluster),
stat = "identity", position = position_dodge()) +
theme_light() +
scale_fill_hue() +
labs(y = "", x = "Num. samples" ) +
guides(fill = guide_legend(title = NULL, ncol = 2, nrow = 1)) +
theme(axis.text.y = element_text(angle = 90, hjust = 0.5, vjust = 0.5)) +
scale_fill_manual(values = c("#bfbfbf","#666666"),
breaks = c("control", args$case_type),
label = c("Control", args$case_type)) +
custom_ggtheme
plot_num_samples
if ( any(names(df_metadata) == "gender") ) {
## Gender
plot_gender <- ggplot(df_metadata %>%
mutate(gender = gender %>% as.character()) %>%
dplyr::count(cluster, gender)) +
geom_bar(aes(y = cluster, x = n, group = gender, fill = gender), alpha = 0.8,
stat = "identity", position = "dodge") +
theme_light() +
labs(y = "", x = "Num. samples" ) +
scale_fill_manual(labels = c("Male", "Female"), values = c("1", "2"), palette=scales::hue_pal()) +
guides(fill = guide_legend(title = "Gender: ", ncol = 2, nrow = 1)) +
theme(axis.text.y = element_text(angle = 90, hjust = 0.5, vjust = 0.5)) +
custom_ggtheme
plot_gender
}
if ( any(names(df_metadata) == "age") ) {
## AGE
plot_age <- ggplot(df_metadata ) +
geom_density(aes(x = age, fill = cluster), alpha = 0.7) +
theme_light() +
labs(x = "AGE" ) +
scale_fill_hue() +
guides(fill = guide_legend(title = NULL, ncol = 3, nrow = 1)) +
scale_fill_manual(values = c("#bfbfbf","#666666"),
breaks = c("control", args$case_type),
label = c("Control", args$case_type)) +
custom_ggtheme
}
if ( any(names(df_metadata) == "rin") ) {
## RIN
plot_rin <- ggplot(df_metadata ) +
geom_density(aes(x = rin, fill = cluster), alpha = 0.7) +
theme_light() +
labs(x = "RIN" ) +
scale_fill_hue() +
guides(fill = guide_legend(title = NULL, ncol = 3, nrow = 1)) +
scale_fill_manual(values = c("#bfbfbf","#666666"),
breaks = c("control", args$case_type),
label = c("Control", args$case_type)) +
custom_ggtheme
}
## Mapped read depth
plot_read_depth <- ggplot(df_metadata ) +
geom_density(aes(x = all_mapped_reads, fill = cluster), alpha = 0.6) +
theme_light() +
scale_fill_hue() +
labs(x = "Mapped Read Count" ) +
guides(fill = guide_legend(title = NULL, ncol = 3, nrow = 1)) +
scale_fill_manual(values = c("#bfbfbf","#666666"),
breaks = c("control", args$case_type),
label = c("Control", args$case_type)) +
custom_ggtheme
plot_read_depth
if ( exists("plot_age") ) {
ggpubr::ggarrange(plot_num_samples, plot_gender, plot_age, plot_read_depth,
labels = c("a","b","c","d"))
} else if ( exists("plot_rin") ) {
ggpubr::ggarrange(plot_num_samples, plot_gender, plot_rin, plot_read_depth,
labels = c("a","b","c","d"))
} else {
ggpubr::ggarrange(plot_num_samples, plot_read_depth,
labels = c("a","b"))
}
ggplot2::ggsave(file.path(args$figures_folder, "supplementary_figure27_ad.png"), width = 180, height = 100, units = "mm", dpi = 300)
}
#' Title
#' Get common introns between cases and controls (ONLY FRONTAL CORTEX) and subsample them by read coverage
#' @return
#' @export
#'
#' @examples
supplementary_figure27_e_f <- function() {
print("Getting common introns between sample cluster...")
clusters <- df_metadata$cluster %>% unique()
#################################
## GET COMMON INTRONS
## ACROSS CONTROL AND AD SAMPLES
#################################
df_common_introns <- get_common_introns()
# df_common_introns %>% group_by(sample_type) %>% distinct(ref_junID) %>% dplyr::count(sample_type)
if ( df_common_introns %>% dplyr::count(ref_junID) %>% filter(n == 1) %>% nrow() > 0 ) {
print("ERROR. Common annotated introns should appear only once per sample cluster.")
}
##########################################################
## PLOT COVERAGE OF SHARED INTRONS BEFORE SUBSAMPLING
##########################################################
plot_BS <- ggplot(data = df_common_introns) +
geom_density(mapping = aes(x = mean_coverage, fill = sample_type), alpha = 0.8) +
ggtitle("Before subsampling") +
theme_light() +
custom_ggtheme +
xlab("log10 mean expression level") +
scale_fill_manual(name = "Sample group: ",
values = c("#bfbfbf","#666666"),
breaks = c(args$control_type, args$case_type ),
label = c(args$control_type, args$case_type )) +
ylim(c(0, 1))+
xlim(c(0, 3))
plot_BS
#####################################
#### START SUBSAMPLIING
#####################################
# "To compare differences in splicing accuracy between the annotated introns found across the 48 samples studied, we made use of their MSR values.
# To avoid potential biases derived from differences in mean expression levels, we only considered those annotated introns overlapping both groups of samples
# that displayed a maximum difference in their log10 expression levels of 0.005 (matchit() function, MatchIt R package, version 4.5.0). "
subsample <- if ( file.exists(file.path(args$results_folder, "common_subsampled_introns_seed1000.rds")) ) {
readRDS(file = file.path(args$results_folder, "/common_subsampled_introns_seed1000.rds"))
} else {
subsample_introns(df_common_introns)
}
# subsample %>% group_by(sample_type) %>% distinct(ref_junID) %>% dplyr::count(sample_type)
#######################################
## PLOT COVERAGE AFTER SUBSAMPLE
#######################################
plot_AS <- ggplot(data = subsample) +
geom_density(mapping = aes(x = mean_coverage, fill = sample_type), alpha = 0.8) +
ggtitle("After subsampling") +
theme_light() +
custom_ggtheme +
xlab("log10 mean expression level") +
scale_fill_manual(name = "Sample group: ",
values = c("#bfbfbf","#666666"),
breaks = c(args$control_type, args$case_type ),
label = c(args$control_type, args$case_type )) +
ylim(c(0, 1))+
xlim(c(0, 3))
ggpubr::ggarrange(plot_BS, plot_AS, labels = c("a", "b"), common.legend = T)
dir.create(path = figures_path, recursive = T,showWarnings = T)
ggplot2::ggsave(file.path(args$figures_path, "supplementary_figure27_e_f.png"), width = 180, height = 100, units = "mm", dpi = 300)
}
##################################
## SUPPLEMENTARY FIGURE 22
##################################
## FUNCTIONS TO GENERATE SUPPLEMENTARY FIGURE 22 & Supplementary Table 13
get_data_supplementary_figure_22 <- function(replace = T) {
#common_introns <- readRDS(file = paste0(results_path, "/common_subsampled_introns_seed1000.rds"))
local_results_folder <- paste0(args$results_folder, "/MSR_normalisation_by_TPM/")
dir.create(path = local_results_folder, showWarnings = F)
local_figures_folder <- paste0(args$figures_folder, "/MSR_normalisation_by_TPM/")
dir.create(path = local_figures_folder, showWarnings = F)
do_paired_test <- T
################################
## LOAD RBPs OF INTEREST
################################
# Only get genes with splicing regulator, spliceosome, Exon.Junction.Complex and NMD functions
all_RBPs <- (xlsx::read.xlsx(file = paste0(base_folder,'/dependencies/RBPs_subgroups.xlsx'), header = TRUE, sheetIndex = 1) %>% as_tibble() %>% mutate(NMD = 0))[-116,]
all_NMDs <- data.frame(name = c("SMG1", "SMG5", "SMG6", "SMG7", "UPF1", "UPF2", "UPF3A"),
id = c("ENSG00000157106", "ENSG00000198952", "ENSG00000070366", "ENSG00000116698", "ENSG00000005007", "ENSG00000151461", "ENSG00000169062"),
Splicing.regulation = c(0,0,0,0,0,0,0),
Spliceosome = c(0,0,0,0,0,0,0),
Exon.Junction.Complex = c(0,0,0,0,0,0,0),
NMD = c(1,1,1,1,1,1,1))
all_RBPs_tidy <- rbind(all_NMDs,all_RBPs)
for (project_id in all_projects) {
# project_id = all_projects[1]
message(project_id, "...")
if (!file.exists(paste0(local_results_folder, "/supplementary_figure_22.rds")) || replace) {
################################
## CALCULATE MSR VALUES
## NORMALISED BY NMD/TPM VALUES
################################
fold_change_TPM <- if (file.exists(paste0(local_results_folder, "/", project_id, "_inverse_fold-change_TPM.rds"))) {
readRDS(file = paste0(local_results_folder, "/", project_id, "_inverse_fold-change_TPM.rds"))
} else {
fold_change_TPM <- AD_control_calculate_fold_change_TPM(project.id = project_id, RBP.list = all_RBPs_tidy, get.median = T )
fold_change_TPM %>% arrange(t_test) %>% as_tibble()
saveRDS(object = fold_change_TPM, file = paste0(local_results_folder, "/", project_id, "_inverse_fold-change_TPM.rds"))
fold_change_TPM
}
MSR_normalised <- AD_control_normalise_MSR_values_by_gene_TPM(project.id = project_id, fold.change.TPM = fold_change_TPM, gene.list = all_RBPs_tidy)
## Get common introns between AD and control
introns_control <- MSR_normalised %>% filter(groups == "control") %>% distinct(ref_junID)
introns_AD <- MSR_normalised %>% filter(groups == "AD") %>% distinct(ref_junID)
common_introns <- intersect(introns_AD$ref_junID, introns_control$ref_junID) %>% unique()
common_introns %>% length()
################################
## CALCULATE AGE EFFECT SIZES
## USING MSR NORMALISED DATA
################################
message("Calculating AD effect sizes using MSR normalised data....")
gene_list <- MSR_normalised$gene_normalised %>% unique %>% sort
doParallel::registerDoParallel(cores = 4)
effect_size_normalised_TPM <- foreach(j = seq(length(gene_list)), .combine = "rbind") %dopar% {
gene = gene_list[j]
# gene <- gene_list[1]
message(gene, "....")
map_df( c("MSR Donor", "MSR Acceptor"), function(MSR_type) {
# MSR_type = "MSR Donor"
message(MSR_type, "....")
if (MSR_type == "MSR Donor") {
MSR_normalised_data = MSR_normalised %>% dplyr::select(ref_junID, MSR_normalised = MSR_D_normalised, disease_group = groups, gene_normalised)
} else {
MSR_normalised_data = MSR_normalised %>% dplyr::select(ref_junID, MSR_normalised = MSR_A_normalised, disease_group = groups, gene_normalised)
}
MSR_normalised_data = MSR_normalised_data %>%
filter(ref_junID %in% (common_introns)) %>%
filter(gene_normalised == gene) %>%
spread(key = disease_group, MSR_normalised)
## Wilcoxon one-tailed test
wilcox_pval <- data.frame(pval = c((wilcox.test(x = MSR_normalised_data$control,
y = MSR_normalised_data$AD,
alternative = "less",
paired = T))$p.value))
## Wilcoxon effect size
message(MSR_type, " - Wilcoxon paired effect size test ....")
w_effsize <- rstatix::wilcox_effsize(data = MSR_normalised_data %>%
dplyr::select(ref_junID, control, AD) %>%
gather(key = disease_group, value = MSR, -ref_junID) %>%
mutate(disease_group = disease_group %>% as.factor()),
formula = MSR ~ disease_group,
alternative = "less",
ref.group = "control",
conf.level = .05,
paired = T) %>%
mutate(MSR_type = MSR_type,
project = project_id,
gene_normalised = gene)
# ## Save results
# dir.create(path = paste0(local_results_folder, "/",project_id,"_paired",do_paired_test,"/"),
# recursive = T)
# saveRDS(object = r_effect1 %>% cbind(wilcox_pval),
# file = paste0(local_results_folder, "/",project_id,"_paired",do_paired_test,"/",
# project_id,"_", gene, "_",MSR_type,"_effect_size_age_normalised_TPM.rds"))
## Return results
return(w_effsize %>% cbind(wilcox_pval))
})
}
saveRDS(object = effect_size_normalised_TPM %>% inner_join(y = all_RBPs_tidy, by = c("gene_normalised" = "name")),
file = paste0(local_results_folder, "/supplementary_figure_22.rds"))
} else {
effect_size_normalised_TPM <- readRDS(file = paste0(local_results_folder, "/supplementary_figure_22.rds"))