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##################################################
##### visualisation of eQTL analysis results #####
##################################################
#### prepare the script working environment ####
remove(list = ls())
gc()
set.seed(1000)
# Set working directory ####
work.dir <- "C:/Users/harta005/Projects/seed-germination-qtl"
setwd(work.dir)
# dependencies ####
library(dplyr)
library(ggplot2)
library(tidyr)
library(doParallel)
library(VennDiagram)
library(UpSetR)
library(forcats)
library(grid)
library(heritability)
library(topGO)
library(org.At.tair.db)
# unused libraries ####
# library(Mfuzz)
# library(Biobase)
# library(gplots)
# library(RColorBrewer)
# library(limma)
# library(factoextra)
# library(stats)
# library(amap)
#
#
#
# library(gridExtra)
# library(RCy3)
# library(corrplot)
# library(reshape2)
# library(Hmisc)
# library(igraph)
# library(threejs)
# library(MASS)
#
# load required function ####
setwd('functions/')
for(i in 1:length(dir())){
source(dir()[i])
} # read function from Mark
setwd(work.dir)
overlap <- function(table, stage) {
for (i in 1:length(stage)) {
if (nrow(table) != 0) {
table <- subset(table, table[stage[i]] == T)
} else {
break
}
}
nrow(table)
} # function to determine the overlapping qtl between 2 stages
# load the data ####
seed.stage <- c('pd', 'ar', 'im', 'rp')
# presentation
presentation <- theme(axis.text.x = element_text(size=10, face="bold", color="black"),
axis.text.y = element_text(size=10, face="bold", color="black"),
axis.title.x = element_text(size=12, face="bold", color="black"),
axis.title.y = element_text(size=12, face="bold", color="black"),
strip.text.x = element_text(size=12, face="bold", color="black"),
strip.text.y = element_text(size=12, face="bold", color="black"),
strip.text = element_text(size =12, face="bold", color="black"),
#plot.title = element_text(size=15, face="bold"),
panel.background = element_rect(fill = "white",color="black"),
panel.grid.major = element_line(colour = "grey80"),
panel.grid.minor = element_blank())
# combine the eqtl table and plotting - single stage ####
all.eqtl.table <- data.frame(matrix(ncol = 20, nrow = 0))
#colnames(all.eqtl.table) <- colnames(eqtl.table)
for (stage in seed.stage) {
print(stage)
eqtl.table <- readRDS(paste0('qtl-tables/table_single-stage-eqtl_', stage, '.rds')) %>%
mutate(stage = stage)
all.eqtl.table <- rbind(all.eqtl.table, eqtl.table)
}
all.eqtl.table <- all.eqtl.table %>%
mutate(qtl_type = ifelse(qtl_type == 'cis', 'local', 'distant'))
saveRDS(all.eqtl.table, 'qtl-tables/table_single-stage-eqtl_all.rds')
# comparing LOD, effect, and R2 of shared local and distant eQTL - single stage ####
eqtl.stat <- group_by(all.eqtl.table, stage, qtl_type) %>%
summarise(median_sig = round(median(qtl_significance), 2),
#sd_sig = sd(qtl_significance),
median_eff = round(median(abs(qtl_effect)), 2),
#sd_eff = sd(qtl_effect),
median_r2 = round(median(qtl_R2_sm), 2)) %>%
#sd_r2 = sd(qtl_R2_sm),) %>%
mutate(pval_sig = NA, pval_eff = NA, pval_r2 = NA) %>%
as.data.frame()
for (i in 1:nrow(eqtl.stat)) {
stage.row <- which(all.eqtl.table$stage == eqtl.stat[i, 1])
eqtl.stat[i, 6] <- wilcox.test(qtl_significance ~ qtl_type, data = all.eqtl.table[stage.row, ])$p.value
eqtl.stat[i, 7] <- wilcox.test(abs(qtl_effect) ~ qtl_type, data = all.eqtl.table[stage.row, ])$p.value
eqtl.stat[i, 8] <- wilcox.test(qtl_R2_sm ~ qtl_type, data = all.eqtl.table[stage.row, ])$p.value
}
# calculating shared local and distant eQTL ####
qtl.table.summary <- data.frame(matrix(ncol = 18, nrow = 0))
window.nu <- 4e6
maxsize <- 100e6
chr.num <- 5
all.eqtl.table2 <- mutate(all.eqtl.table, interval = findInterval(qtl_bp, seq(1, maxsize, by = window.nu)))
all.eqtl.table2 <- subset(x = all.eqtl.table2, select = c(trait, qtl_chromosome, interval, qtl_type,
stage))
local.table <- all.eqtl.table2[all.eqtl.table2$qtl_type == 'local', ] %>%
count(trait, qtl_chromosome, interval, qtl_type, stage) %>%
spread(stage, n) %>%
dplyr::select(-qtl_type) %>%
arrange(trait)
duplicated.local <- duplicated(local.table$trait)
for (i in 1:nrow(local.table)) {
if (i == 1) {
next
}
if (duplicated.local[i] &&
local.table$qtl_chromosome[i] == local.table$qtl_chromosome[i-1] &&
abs(local.table$interval[i] - local.table$interval[i-1]) == 1 &&
local.table$trait[i] == local.table$trait[i-1]) {
print(i)
table.tmp <- rbind(local.table[i, ], local.table[i-1, ])
table.tmp <- aggregate(table.tmp[4:7], by = list(trait = table.tmp$trait), sum, na.rm = TRUE)
table.tmp$qtl_chromosome <- local.table$qtl_chromosome[i]
table.tmp$interval <- local.table$interval[i]
table.tmp <- table.tmp[, c(colnames(local.table))]
local.table[(i-1), 1] <- 'removed'
local.table[i, ] <- table.tmp
}
}
local.table <- local.table[which(local.table$trait != 'removed'), ]
local.table[is.na(local.table)] <- 0
write.csv(local.table, 'files/shared-local.csv')
local.table <- local.table[, 4:7]
local.table <- apply(local.table, 2, as.integer)
distant.table <- all.eqtl.table2[all.eqtl.table2$qtl_type == 'distant', ] %>%
count(trait, qtl_chromosome, interval, qtl_type, stage) %>%
spread(stage, n) %>%
dplyr::select(-qtl_type) %>%
arrange(trait)
duplicated.distant <- duplicated(distant.table$trait)
for (i in 1:nrow(distant.table)) {
if (i == 1) {
next
}
if (duplicated.local[i] &&
distant.table$qtl_chromosome[i] == distant.table$qtl_chromosome[i-1] &&
abs(distant.table$interval[i] - distant.table$interval[i-1]) == 1 &&
distant.table$trait[i] == distant.table$trait[i-1]) {
print(i)
table.tmp <- rbind(distant.table[i, ], distant.table[i-1, ])
table.tmp <- aggregate(table.tmp[4:7], by = list(trait = table.tmp$trait), sum, na.rm = TRUE)
table.tmp$qtl_chromosome <- distant.table$qtl_chromosome[i]
table.tmp$interval <- distant.table$interval[i]
table.tmp <- table.tmp[, c(colnames(distant.table))]
distant.table[(i-1), 1] <- 'removed'
distant.table[i, ] <- table.tmp
}
}
distant.table <- distant.table[which(distant.table$trait != 'removed'), ]
distant.table[is.na(distant.table)] <- 0
write.csv(distant.table, 'files/shared-distant.csv')
distant.table <- distant.table[, 4:7]
distant.table <- apply(distant.table, 2, as.integer)
#subset(qtl.table.cis, qtl.table.cis[development.stage[1]] == T)
# venn diagram ####
draw.quad.venn(area1 = overlap(local.table, 'pd'),
area3 = overlap(local.table, 'ar'),
area4 = overlap(local.table, 'im'),
area2 = overlap(local.table, 'rp'),
n13 = overlap(local.table, c('pd', 'ar')),
n14 = overlap(local.table, c('pd', 'im')),
n12 = overlap(local.table, c('pd', 'rp')),
n34 = overlap(local.table, c('ar', 'im')),
n23 = overlap(local.table, c('ar', 'rp')),
n24 = overlap(local.table, c('im', 'rp')),
n134 = overlap(local.table, c('pd', 'ar', 'im')),
n123 = overlap(local.table, c('pd', 'ar', 'rp')),
n124 = overlap(local.table, c('pd', 'im', 'rp')),
n234 = overlap(local.table, c('ar', 'im', 'rp')),
n1234 = overlap(local.table, c('pd', 'ar', 'im', 'rp')),
category = c('primary\ndormant', 'radicle\nprotrusion', 'after-\nripenned', '6 hours after\nimbibition'),
lty = 'blank',
fill = c('#4daf4a', '#e41a1c', '#377eb8', '#984ea3'),
alpha = 0.5)
draw.quad.venn(area1 = overlap(distant.table, 'pd'),
area3 = overlap(distant.table, 'ar'),
area4 = overlap(distant.table, 'im'),
area2 = overlap(distant.table, 'rp'),
n13 = overlap(distant.table, c('pd', 'ar')),
n14 = overlap(distant.table, c('pd', 'im')),
n12 = overlap(distant.table, c('pd', 'rp')),
n34 = overlap(distant.table, c('ar', 'im')),
n23 = overlap(distant.table, c('ar', 'rp')),
n24 = overlap(distant.table, c('im', 'rp')),
n134 = overlap(distant.table, c('pd', 'ar', 'im')),
n123 = overlap(distant.table, c('pd', 'ar', 'rp')),
n124 = overlap(distant.table, c('pd', 'im', 'rp')),
n234 = overlap(distant.table, c('ar', 'im', 'rp')),
n1234 = overlap(distant.table, c('pd', 'ar', 'im', 'rp')),
category = c('primary\ndormant', 'radicle\nprotrusion', 'after-\nripenned', '6 hours after\nimbibition'),
lty = 'blank',
fill = c('#4daf4a', '#e41a1c', '#377eb8', '#984ea3'),
alpha = 0.5, )
pdf(file = 'figures/distant-local-venn-diagram.pdf', width = 10, height = 5)
grid.arrange(local.plot, distant.plot, ncol = 2, heights = c(5, 5))
dev.off()
# upset graph ####
upset.local <- upset(data = as.data.frame(local.table),
mainbar.y.max = 750,
sets = c('rp', 'im', 'ar', 'pd'),
empty.intersections = 'on',
mainbar.y.label = 'shared eQTL',
sets.x.label = 'eQTL per stage',
text.scale = 0.8,
point.size = 1,
line.size = 1,
keep.order = T,
set_size.scale_max = 1400)
tiff(file = paste0("figures/upset-local.tiff"),
width = 1000,
height = 850,
units = 'px',
res = 300,
compression = 'lzw')
upset.local
dev.off()
upset.distant <- upset(data = as.data.frame(distant.table),
mainbar.y.max = 750,
sets = c('rp', 'im', 'ar', 'pd'),
empty.intersections = 'on',
mainbar.y.label = 'shared eQTL',
sets.x.label = 'eQTL per stage',
text.scale = 0.8,
point.size = 1,
line.size = 1,
keep.order = T,
set_size.scale_max = 1400)
tiff(file = paste0("figures/upset-distant.tiff"),
width = 1000,
height = 850,
units = 'px',
res = 300,
compression = 'lzw')
upset.distant
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dev.off()
# cis-trans plot - single stage #####
all.eqtl.table3 <- all.eqtl.table %>%
mutate(stage = replace(stage, qtl_type == 'local', 'local\neQTL'))
#all.eqtl.table3$stage <- ordered(all.eqtl.table$stage, seed.stage)
all.eqtl.table3$qtl_type <- factor(all.eqtl.table3$qtl_type)
all.eqtl.table3 <- all.eqtl.table3 %>%
mutate(allele = ifelse(sign(qtl_effect) <= 0, 'sha',
ifelse(sign(qtl_effect) >=0, 'bay', 'null')))
eqtl.plot <- ggplot(all.eqtl.table3, aes(x = qtl_bp, y = gene_bp)) +
geom_segment(aes(x = qtl_bp_left, y = gene_bp, xend = qtl_bp_right, yend = gene_bp),
alpha = 0.75, colour = "grey") +
geom_point(aes(colour = ordered(stage, levels = c("local\neQTL", "pd", "ar", "im", "rp")),
shape = allele,
size = abs(qtl_effect),
alpha = log10(qtl_significance))
) +
scale_alpha(range = c(0.3, 1)) +
facet_grid(fct_rev(gene_chromosome) ~ qtl_chromosome, space = "free", scales = "free") +
presentation +
scale_colour_manual('stage', values = c('black','#ccbb44', '#228833', '#4477aa', '#cc3311')) +
labs(x = "eQTL peak position (Mb)", y = "Gene position (Mb)") +
scale_x_continuous(breaks=c(5, 10, 15, 20, 25, 30, 35, 40)*10^6,labels=c(5, 10, 15, 20, 25, 30, 35, 40)) +
scale_y_continuous(breaks=c(5, 10, 15, 20, 25, 30, 35, 40)*10^6,labels=c(5, 10, 15, 20, 25, 30, 35, 40))
tiff(file = paste0("figures/eqtl-plot.tiff"),
width = 2250,
height = 1600,
units = 'px',
res = 300,
compression = 'lzw')
eqtl.plot
dev.off()
# histogram - single stage ####
hist.threshold <- data.frame(stage = seed.stage, exp = c(7.45098, 5.464286, 7, 6.470588), threshold = rep(NA, 4))
for (i in 1:nrow(hist.threshold)) {
hist.threshold$threshold[i] <- qpois(p = 0.0001, lambda = hist.threshold$exp[i], lower.tail = F)
} # create horizontal line as a threshold
eqtl.hist <- ggplot(all.eqtl.table3 %>% filter(qtl_type != 'local'),
aes(x=qtl_bp, fill=ordered(stage, levels = c("pd", "ar", "im", "rp")))) + # 750 x 400
geom_histogram(binwidth = 2000000, right = T, origin = 0, alpha = 1) +
facet_grid(factor(stage, levels = c("pd", "ar", "im", "rp")) ~ qtl_chromosome, space = "free",scales="free") +
presentation +
scale_fill_manual('stage', values = c('#ccbb44', '#228833', '#4477aa', '#cc3311')) +
theme(legend.position = "right", legend.text=element_text(size=10)) +
labs(x="eQTL peak position (Mb)",y="eQTL counts") +
ylim(c(0, 100)) +
geom_hline(data = hist.threshold, aes(yintercept = threshold),
linetype = 'dashed',
color = 'red',
size = .1) +
scale_x_continuous(breaks=c(5, 10, 15, 20, 25, 30, 35, 40)*10^6,labels=c(5, 10, 15, 20, 25, 30, 35, 40))
tiff(file = paste0("figures/eqtl-hist.tiff"),
width = 2250,
height = 1200,
units = 'px',
res = 300,
compression = 'lzw')
eqtl.hist
dev.off()
# compare the R2, effect, andsignificance of local and distant eQTL ####
r2.plot <- ggplot(all.eqtl.table, aes(x = qtl_R2_sm,
fill = factor(qtl_type, levels = c('local', 'distant')),
color = factor(qtl_type, levels = c('local', 'distant')))) +
#geom_histogram(aes(y = ..density..), binwidth = 0.001, position = 'identity', alpha = 0.3) +
geom_density(alpha = 0.5, size = 0) +
facet_wrap( ~ factor(stage, levels = c('pd', 'ar', 'im', 'rp')), ncol = 2) +
geom_vline(data = group_by(all.eqtl.table, stage, qtl_type) %>%
summarise(median = median(qtl_R2_sm)), aes(xintercept = median,
color = factor(qtl_type, levels = c('local', 'distant'))),
linetype = 'dashed', size = .5) +
presentation +
scale_color_brewer(palette = 'Set1') +
scale_fill_brewer(palette = 'Set1') +
xlab('explained phenotypic variance (R2)') +
ylab('density') +
labs(fill = 'eQTL type', color = 'eQTL type') +
#ylim(0, 1) +
xlim(0, 1) +
theme(strip.text.x = element_text(size = 12))
tiff(file = paste0("figures/r2-plot.tiff"),
width = 2250,
height = 1200,
units = 'px',
res = 300,
compression = 'lzw')
r2.plot
dev.off()
effect.plot <- ggplot(all.eqtl.table, aes(x = log(abs(qtl_effect)),
fill = factor(qtl_type, levels = c('local', 'distant')),
color = factor(qtl_type, levels = c('local', 'distant')))) +
#geom_histogram(aes(y = ..density..), binwidth = 0.001, position = 'identity', alpha = 0.3) +
geom_density(alpha = 0.5, size = 0) +
facet_wrap( ~ factor(stage, levels = c('pd', 'ar', 'im', 'rp')), ncol = 2) +
geom_vline(data = group_by(all.eqtl.table, stage, qtl_type) %>%
summarise(median = median(log(abs(qtl_effect)))), aes(xintercept = median,
color = factor(qtl_type, levels = c('local', 'distant'))),
linetype = 'dashed', size = .5) +
presentation +
scale_color_brewer(palette = 'Set1') +
scale_fill_brewer(palette = 'Set1') +
xlab('absolute eQTL effect') +
ylab('density') +
labs(fill = 'eQTL type', color = 'eQTL type') +
#ylim(0, 1) +
#xlim(0, 1.2) +
theme(strip.text.x = element_text(size = 12))
tiff(file = paste0("figures/eff-plot.tiff"),
width = 2250,
height = 1200,
units = 'px',
res = 300,
compression = 'lzw')
effect.plot
dev.off()
sig.plot <- ggplot(all.eqtl.table, aes(x = qtl_significance,
fill = factor(qtl_type, levels = c('local', 'distant')),
color = factor(qtl_type, levels = c('local', 'distant')))) +
#geom_histogram(aes(y = ..density..), binwidth = 0.001, position = 'identity', alpha = 0.3) +
geom_density(alpha = 0.5, size = 0) +
facet_wrap( ~ factor(stage, levels = c('pd', 'ar', 'im', 'rp')), ncol = 2) +
geom_vline(data = group_by(all.eqtl.table, stage, qtl_type) %>%
summarise(median = median(qtl_significance)), aes(xintercept = median,
color = factor(qtl_type, levels = c('local', 'distant'))),
linetype = 'dashed', size = .5) +
presentation +
scale_color_brewer(palette = 'Set1') +
scale_fill_brewer(palette = 'Set1') +
xlab('-log10(p)') +
ylab('density') +
labs(fill = 'eQTL type', color = 'eQTL type') +
#ylim(0, 1) +
xlim(0, 36) +
theme(strip.text.x = element_text(size = 12))
tiff(file = paste0("figures/sig-plot.tiff"),
width = 2250,
height = 1200,
units = 'px',
res = 300,
compression = 'lzw')
sig.plot
dev.off()
# ggplot(all.eqtl.table, aes(x = h2_REML, y = qtl_R2_sm,
# fill = factor(qtl_type, levels = c('local', 'distant')),
# color = factor(qtl_type, levels = c('local', 'distant')))) +
# geom_point(size = 1, alpha = 0.5) +
# facet_wrap( ~ factor(stage, levels = c('pd', 'ar', 'im', 'rp')), ncol = 2) +
# geom_vline(data = group_by(all.eqtl.table, stage, qtl_type) %>%
# summarise(med = median(h2_REML, na.rm = T)), aes(xintercept = med,
# color = factor(qtl_type, levels = c('local', 'distant'))),
# linetype = 'dashed', size = .5) +
# geom_hline(data = group_by(all.eqtl.table, stage, qtl_type) %>%
# summarise(med = median(qtl_R2_sm, na.rm = T)), aes(yintercept = med,
# color = factor(qtl_type, levels = c('local', 'distant'))),
# linetype = 'dashed', size = .5) +
# geom_smooth(model = lm) +
# geom_abline(aes(slope = 1, intercept = 0), size = .75, linetype = 'dashed') +
# presentation +
# scale_color_brewer(palette = 'Set1') +
# scale_fill_brewer(palette = 'Set1') +
# xlab('narrow-sense heritabllity (h2)') +
# ylab('explained phenotypic variance (R2)') +
# labs(fill = 'eQTL type', color = 'eQTL type') +
# ylim(0, 1) +
# xlim(0, 1) +
# theme(strip.text.x = element_text(size = 8))