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Figure3.R
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Figure3.R
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#------------------------------------------------------------------------------#
# #
# Figure 3 #
# #
#------------------------------------------------------------------------------#
library(here)
library(wesanderson)
library(GenomicRanges)
library(tidyverse)
# Load paths to data files
source(here('helper_data_file_locations.R'))
# Load code to compute spike-in normalization factor
source(here('helper_spikein_normalization_factor.R'))
# Load analysis functions
source(here('helper_analysis.R'))
# Load ggplot2
source(here('helper_ggplot2_settings.R'))
# Plot colors -----------------------------------------------------------------#
wes_colors <- wes_palette(name = "Darjeeling1", n=8, type = "continuous")
wt_color <- 'black'
hop1_color <- wes_colors[5]
rec8_color <- wes_colors[3]
#------------------------------------------------------------------------------#
# Panel A #
# Spike-in normalization factor #
#------------------------------------------------------------------------------#
# Make data frame of spike-in normalization factors
# Locate samples
names(read_counts_per_chr)[str_detect(read_counts_per_chr,
fixed('ah119', ignore_case=TRUE))]
names(read_counts_per_chr)[str_detect(read_counts_per_chr,
fixed('ah8151', ignore_case=TRUE))]
spinfs <- data.frame(
sample=c('REC8', rep('rec8∆', 2)),
spinf=c(1,
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=list(
read_counts_per_chr[['ah119spikeb-062817_trimmed']],
read_counts_per_chr[['ah119spikeb-100917_trimmed']],
read_counts_per_chr[['ah119spike-chip_trimmed']]),
ref_input_counts=list(
read_counts_per_chr[['ah119spikea-062817_trimmed']],
read_counts_per_chr[['ah119spikea-100917_trimmed']],
read_counts_per_chr[['ah119spike-inp_trimmed']]),
test_chip_counts=
read_counts_per_chr[['ah8151spike-chip_trimmed']],
test_input_counts=
read_counts_per_chr[['ah8151spike-inp_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=list(
read_counts_per_chr[['ah119spikeb-062817_trimmed']],
read_counts_per_chr[['ah119spikeb-100917_trimmed']],
read_counts_per_chr[['ah119spike-chip_trimmed']]),
ref_input_counts=list(
read_counts_per_chr[['ah119spikea-062817_trimmed']],
read_counts_per_chr[['ah119spikea-100917_trimmed']],
read_counts_per_chr[['ah119spike-inp_trimmed']]),
test_chip_counts=
read_counts_per_chr[['ah8151spike-red1-chip_trimmed']],
test_input_counts=
read_counts_per_chr[['ah8151spike-red1-inp_trimmed']]))))
# Order samples
spinfs$sample <- factor(spinfs$sample, levels=c('REC8', 'rec8∆'))
# Plot
message('Red1 amount in rec8∆ relative to wild type:')
message(round(mean(subset(spinfs, sample == 'rec8∆')$spinf), 3),
'+/-',
round(sd(subset(spinfs, sample == 'rec8∆')$spinf), 3))
mean_values <- spinfs %>% group_by(sample) %>% summarise(spinf_mean=mean(spinf))
ggplot(spinfs, aes(sample, spinf * 100, colour=sample, fill=sample)) +
geom_hline(aes(yintercept = 100), linetype = 3) +
stat_summary(fun.y=mean, geom="bar", width=0.5, alpha=0.25, colour=NA) +
geom_point(size=1.5, alpha=1) +
scale_colour_manual('', values=c(wt_color, rec8_color), guide=FALSE) +
scale_fill_manual('', values=c(wt_color, rec8_color), guide=FALSE) +
scale_x_discrete(labels=c(expression(italic('REC8')),
expression(paste(italic('rec8'), Delta)))) +
ylim(0, 100) +
labs(title = '', x = '', y = 'Red1 amount\n(% of wild type)')
#------------------------------------------------------------------------------#
# Panel B #
# Red1 fragment pileup on selected example chromosomes #
#------------------------------------------------------------------------------#
WT <- import_bedGraph(WT_1st_pileup)
rec8 <- import_bedGraph(rec8_1st_pileup)
WT_keep <- WT
rec8_keep <- rec8
### Normalize signal
# Compute genome-wide signal average
WT_avrg <- average_chr_signal(
subset(WT, str_detect(WT@seqnames, '_SK1')))[[2]]
rec8_avrg <- average_chr_signal(
subset(rec8, str_detect(rec8@seqnames, '_SK1')))[[2]]
# Compute spike-in normalization factor
subset(names(read_counts_per_chr),
str_detect(names(read_counts_per_chr), '8115'))
nf <- spikein_normalization_factor_from_counts(
ref_chip_counts=list(read_counts_per_chr[['ah119spikeb-062817_trimmed']],
read_counts_per_chr[['ah119spikeb-100917_trimmed']],
read_counts_per_chr[['ah119spike-chip_trimmed']]),
ref_input_counts=list(read_counts_per_chr[['ah119spikea-062817_trimmed']],
read_counts_per_chr[['ah119spikea-100917_trimmed']],
read_counts_per_chr[['ah119spike-inp_trimmed']]),
test_chip_counts=read_counts_per_chr[['ah8151spike-red1-chip_trimmed']],
test_input_counts=read_counts_per_chr[['ah8151spike-red1-inp_trimmed']])
# Normalize by average signal and then spike-in factor
WT[str_detect(WT@seqnames, '_SK1')]$score <-
WT[str_detect(WT@seqnames, '_SK1')]$score / WT_avrg
rec8[str_detect(rec8@seqnames, '_SK1')]$score <-
rec8[str_detect(rec8@seqnames, '_SK1')]$score / rec8_avrg * nf
make_df_for_plotting <- function(gr, sample_name='WT',
normalize_by_genome_mean=FALSE, window_size=1,
genome='SK1_S288C', chr='chrI_SK1',
region=c(194000, 208000)) {
t0 <- proc.time()[3]
message('Compute binned score...')
gr <- bedGraph_to_binned_score(
gr, mean_normalize=normalize_by_genome_mean, tile_width=window_size,
coord_genome=genome)
message('Keep only selected chromosome...')
gr <- keepSeqlevels(gr, chr, pruning.mode='coarse')
if (!missing(region)) {
message('Keep only selected region...')
gr <- subset(gr, start >= region[1] & start <= region[2])
}
message('Convert to data frame for plotting...')
gr <- data.frame(seqnames=seqnames(gr),
position=start(gr) + floor(width(gr) / 2),
signal=gr$binned_score)
message('Add sample name to data frame...')
gr$strain <- sample_name
message('~~~')
message('Completed in ', elapsed_time(t0, proc.time()[3]))
gr
}
WT_chrI <- make_df_for_plotting(
WT, sample_name='REC8', normalize_by_genome_mean=FALSE,
window_size=100, chr='chrI_SK1')
rec8_chrI <- make_df_for_plotting(
rec8, sample_name='rec8∆', normalize_by_genome_mean=FALSE,
window_size=100, chr='chrI_SK1')
df_chrI <- rbind(WT_chrI, rec8_chrI)
WT_chrII <- make_df_for_plotting(
WT, sample_name='REC8', normalize_by_genome_mean=FALSE,
window_size=100, chr='chrII_SK1')
rec8_chrII <- make_df_for_plotting(
rec8, sample_name='rec8∆', normalize_by_genome_mean=FALSE,
window_size=200, chr='chrII_SK1')
df_chrII <- rbind(WT_chrII, rec8_chrII)
# Order strains
df_chrI$strain = factor(df_chrI$strain, levels=c('REC8', 'rec8∆'))
df_chrII$strain = factor(df_chrII$strain, levels=c('REC8', 'rec8∆'))
# Plot
cen_midpoint <- function(chr, ref_genome='SK1_S288C', y_coord=0) {
cen <- get_chr_coordinates(genome=ref_genome)
if (!missing(chr)) {
# Keep only required chr
cen <- cen[cen@seqnames == chr]
message(' Drop all chromosomes except ', chr, '...')
gr <- keepSeqlevels(cen, chr, pruning.mode="coarse")
}
start <- cen@ranges@start
half_width <- cen@ranges@width / 2
cen_mid <- round(start + half_width)
data.frame(seqnames = cen@seqnames, cen_mid = cen_mid, y = y_coord)
}
cen_mid <- cen_midpoint(chr='chrI_SK1', ref_genome='SK1_S288C', y_coord=0)
ggplot(df_chrI, aes(position / 1000, signal, colour=strain)) +
scale_color_manual(values=c(wt_color, rec8_color)) +
geom_area(position='identity', aes(fill=strain, colour=strain),
alpha=0.75, size=0.25) +
geom_point(data=cen_mid, aes(cen_mid/1000, 0.2), size=1, colour='black') +
annotate("text", x=cen_mid$cen_mid / 1000, y=-1, label="CEN",
size=2, colour='black') +
scale_fill_manual(values=c(wt_color, rec8_color)) +
labs(title='',
x=paste0('Position on chr I (Kb)'), y='') +
theme(legend.position = "none") +
labs(y=paste0('Red1 occupancy')) +
ylim(-1, 12) +
theme(
strip.background=element_blank(),
strip.text=element_blank()
)
cen_mid <- cen_midpoint(chr='chrII_SK1', ref_genome='SK1_S288C', y_coord=0)
ggplot(df_chrII, aes(position / 1000, signal, colour=strain)) +
scale_color_manual('', values=c(wt_color, rec8_color)) +
geom_area(position='identity', aes(fill=strain, colour=strain),
alpha=0.75, size=0.25) +
geom_point(data=cen_mid, aes(cen_mid/1000, 0.2), size=1, colour='black') +
annotate("text", x=cen_mid$cen_mid / 1000, y=-1, label="CEN",
size=2, colour='black') +
scale_fill_manual('', values=c(wt_color, rec8_color)) +
annotate('text', x=760, y=11, label="italic('REC8')",
parse=TRUE, size=3, colour=wt_color) +
annotate('text', x=760, y=9, label="paste(italic('rec8'), Delta)",
parse=TRUE, size=3, colour=rec8_color) +
labs(title='',
x=paste0('Position on chr II (Kb)'), y='') +
theme(legend.position = "none") +
labs(y=paste0('Red1 occupancy')) +
ylim(-1, 12) +
theme(
strip.background=element_blank(),
strip.text=element_blank()
)
#------------------------------------------------------------------------------#
# Panel C #
# Spike-in normalization factor #
#------------------------------------------------------------------------------#
# Make data frame of spike-in normalization factors
# Locate samples
names(read_counts_per_chr)[str_detect(read_counts_per_chr,
fixed('ah119', ignore_case=TRUE))]
names(read_counts_per_chr)[str_detect(read_counts_per_chr,
fixed('ah9120', ignore_case=TRUE))]
spinfs <- data.frame(
sample=c('HOP1', rep('hop1∆', 2)),
spinf=c(1,
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=list(
read_counts_per_chr[['ah119spikeb-062817_trimmed']],
read_counts_per_chr[['ah119spikeb-100917_trimmed']],
read_counts_per_chr[['ah119spike-chip_trimmed']]),
ref_input_counts=list(
read_counts_per_chr[['ah119spikea-062817_trimmed']],
read_counts_per_chr[['ah119spikea-100917_trimmed']],
read_counts_per_chr[['ah119spike-inp_trimmed']]),
test_chip_counts=
read_counts_per_chr[['ah9120spikeb-100917_trimmed']],
test_input_counts=
read_counts_per_chr[['ah9120spikea-100917_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=list(
read_counts_per_chr[['ah119spikeb-062817_trimmed']],
read_counts_per_chr[['ah119spikeb-100917_trimmed']],
read_counts_per_chr[['ah119spike-chip_trimmed']]),
ref_input_counts=list(
read_counts_per_chr[['ah119spikea-062817_trimmed']],
read_counts_per_chr[['ah119spikea-100917_trimmed']],
read_counts_per_chr[['ah119spike-inp_trimmed']]),
test_chip_counts=
read_counts_per_chr[['ah9120spike-chip_trimmed']],
test_input_counts=
read_counts_per_chr[['ah9120spike-inp_trimmed']]))))
# Order samples
spinfs$sample <- factor(spinfs$sample, levels=c('HOP1', 'hop1∆'))
# Plot
message('Red1 amount in hop1∆ relative to wild type:')
message(round(mean(subset(spinfs, sample == 'hop1∆')$spinf), 3),
'+/-',
round(sd(subset(spinfs, sample == 'hop1∆')$spinf), 3))
mean_values <- spinfs %>% group_by(sample) %>% summarise(spinf_mean=mean(spinf))
ggplot(spinfs, aes(sample, spinf * 100, colour=sample, fill=sample)) +
geom_hline(aes(yintercept = 100), linetype = 3) +
stat_summary(fun.y=mean, geom="bar", width=0.5, alpha=0.25, colour=NA) +
geom_point(size=1.5, alpha=1) +
# geom_jitter(width = 0.1) +
scale_colour_manual('', values=c(wt_color, hop1_color), guide=FALSE) +
scale_fill_manual('', values=c(wt_color, hop1_color), guide=FALSE) +
scale_x_discrete(labels=c(expression(italic('HOP1')),
expression(paste(italic('hop1'), Delta)))) +
ylim(0, 100) +
labs(title = '', x = '', y = 'Red1 amount\n(% of wild type)')
#------------------------------------------------------------------------------#
# Panel D #
# Red1 fragment pileup on selected example chromosomes #
#------------------------------------------------------------------------------#
WT <- import_bedGraph(WT_1st_pileup)
hop1 <- import_bedGraph(hop1_1st_pileup)
### Normalize signal
# Compute genome-wide signal average
WT_avrg <- average_chr_signal(
subset(WT, str_detect(WT@seqnames, '_SK1')))[[2]]
hop1_avrg <- average_chr_signal(
subset(hop1, str_detect(hop1@seqnames, '_SK1')))[[2]]
# Compute spike-in normalization factor
subset(names(read_counts_per_chr),
str_detect(names(read_counts_per_chr), '9120'))
nf <- spikein_normalization_factor_from_counts(
ref_chip_counts=list(read_counts_per_chr[['ah119spikeb-062817_trimmed']],
read_counts_per_chr[['ah119spikeb-100917_trimmed']],
read_counts_per_chr[['ah119spike-chip_trimmed']]),
ref_input_counts=list(read_counts_per_chr[['ah119spikea-062817_trimmed']],
read_counts_per_chr[['ah119spikea-100917_trimmed']],
read_counts_per_chr[['ah119spike-inp_trimmed']]),
test_chip_counts=read_counts_per_chr[['ah9120spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[['ah9120spikea-100917_trimmed']])
# Normalize by average signal and then spike-in factor
WT[str_detect(WT@seqnames, '_SK1')]$score <-
WT[str_detect(WT@seqnames, '_SK1')]$score / WT_avrg
hop1[str_detect(hop1@seqnames, '_SK1')]$score <-
hop1[str_detect(hop1@seqnames, '_SK1')]$score / hop1_avrg * nf
make_df_for_plotting <- function(gr, sample_name='WT',
normalize_by_genome_mean=FALSE, window_size=1,
genome='SK1_S288C', chr='chrI_SK1',
region=c(194000, 208000)) {
t0 <- proc.time()[3]
message('Compute binned score...')
gr <- bedGraph_to_binned_score(
gr, mean_normalize=normalize_by_genome_mean, tile_width=window_size,
coord_genome=genome)
message('Keep only selected chromosome...')
gr <- keepSeqlevels(gr, chr, pruning.mode='coarse')
if (!missing(region)) {
message('Keep only selected region...')
gr <- subset(gr, start >= region[1] & start <= region[2])
}
message('Convert to data frame for plotting...')
gr <- data.frame(seqnames=seqnames(gr),
position=start(gr) + floor(width(gr) / 2),
signal=gr$binned_score)
message('Add sample name to data frame...')
gr$strain <- sample_name
message('~~~')
message('Completed in ', elapsed_time(t0, proc.time()[3]))
gr
}
WT_chrI <- make_df_for_plotting(
WT, sample_name='HOP1', normalize_by_genome_mean=FALSE,
window_size=100, chr='chrI_SK1')
hop1_chrI <- make_df_for_plotting(
hop1, sample_name='hop1∆', normalize_by_genome_mean=FALSE,
window_size=100, chr='chrI_SK1')
df_chrI <- rbind(WT_chrI, hop1_chrI)
WT_chrII <- make_df_for_plotting(
WT, sample_name='HOP1', normalize_by_genome_mean=FALSE,
window_size=100, chr='chrII_SK1')
hop1_chrII <- make_df_for_plotting(
hop1, sample_name='hop1∆', normalize_by_genome_mean=FALSE,
window_size=200, chr='chrII_SK1')
df_chrII <- rbind(WT_chrII, hop1_chrII)
# Order strains
df_chrI$strain = factor(df_chrI$strain, levels=c('HOP1', 'hop1∆'))
df_chrII$strain = factor(df_chrII$strain, levels=c('HOP1', 'hop1∆'))
# Plot
cen_midpoint <- function(chr, ref_genome='SK1_S288C', y_coord=0) {
cen <- get_chr_coordinates(genome=ref_genome)
if (!missing(chr)) {
# Keep only required chr
cen <- cen[cen@seqnames == chr]
message(' Drop all chromosomes except ', chr, '...')
gr <- keepSeqlevels(cen, chr, pruning.mode="coarse")
}
start <- cen@ranges@start
half_width <- cen@ranges@width / 2
cen_mid <- round(start + half_width)
data.frame(seqnames = cen@seqnames, cen_mid = cen_mid, y = y_coord)
}
cen_mid <- cen_midpoint(chr='chrI_SK1', ref_genome='SK1_S288C', y_coord=0)
ggplot(df_chrI, aes(position / 1000, signal, colour=strain)) +
# geom_hline(yintercept=1, lty=3) +
scale_color_manual(values=c(wt_color, hop1_color)) +
geom_area(position='identity', aes(fill=strain, colour=strain),
alpha=0.75, size=0.25) +
geom_point(data=cen_mid, aes(cen_mid/1000, 0.2), size=1, colour='black') +
annotate("text", x=cen_mid$cen_mid / 1000, y=-1, label="CEN",
size=2, colour='black') +
# facet_grid(strain ~ .) +
scale_fill_manual(values=c(wt_color, hop1_color)) +
labs(title='',
x=paste0('Position on chr I (Kb)'), y='') +
theme(legend.position = "none") +
labs(y=paste0('Red1 occupancy')) +
ylim(-1, 16) +
theme(
strip.background=element_blank(),
strip.text=element_blank()
)
cen_mid <- cen_midpoint(chr='chrII_SK1', ref_genome='SK1_S288C', y_coord=0)
ggplot(df_chrII, aes(position / 1000, signal, colour=strain)) +
scale_color_manual('', values=c(wt_color, hop1_color)) +
geom_area(position='identity', aes(fill=strain, colour=strain),
alpha=0.75, size=0.25) +
geom_point(data=cen_mid, aes(cen_mid/1000, 0.2), size=1, colour='black') +
annotate("text", x=cen_mid$cen_mid / 1000, y=-1, label="CEN",
size=2, colour='black') +
scale_fill_manual('', values=c(wt_color, hop1_color)) +
annotate('text', x=760, y=14, label="italic('HOP1')", parse=TRUE,
size=3, colour=wt_color) +
annotate('text', x=760, y=11, label="paste(italic('hop1'), Delta)", parse=TRUE,
size=3, colour=hop1_color) +
labs(title='',
x=paste0('Position on chr II (Kb)'), y='') +
theme(legend.position = "none") +
labs(y=paste0('Red1 occupancy')) +
ylim(-1, 16) +
theme(
strip.background=element_blank(),
strip.text=element_blank()
)
#------------------------------------------------------------------------------#
# Panel E #
# Average Red1 signal per chromosome #
#------------------------------------------------------------------------------#
WT <- import_bedGraph(WT_1st_pileup)
hop1 <- import_bedGraph(hop1_1st_pileup)
# Drop spike-in genome
WT_SK1 <- keepSeqlevels(
WT, paste0('chr', as.roman(1:16), '_SK1'), pruning.mode="coarse")
hop1_SK1 <- keepSeqlevels(
hop1, paste0('chr', as.roman(1:16), '_SK1'), pruning.mode="coarse")
WT_avrg <- average_chr_signal(WT_SK1)[[2]]
hop1_avrg <- average_chr_signal(hop1_SK1)[[2]]
# Compute spike-in normalization factor
subset(names(read_counts_per_chr),
str_detect(names(read_counts_per_chr), '9120'))
nf <- spikein_normalization_factor_from_counts(
ref_chip_counts=list(read_counts_per_chr[['ah119spikeb-062817_trimmed']],
read_counts_per_chr[['ah119spikeb-100917_trimmed']],
read_counts_per_chr[['ah119spike-chip_trimmed']]),
ref_input_counts=list(read_counts_per_chr[['ah119spikea-062817_trimmed']],
read_counts_per_chr[['ah119spikea-100917_trimmed']],
read_counts_per_chr[['ah119spike-inp_trimmed']]),
test_chip_counts=read_counts_per_chr[['ah9120spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[['ah9120spikea-100917_trimmed']])
# Normalize by average signal and then spike-in factor
WT_SK1$score <- WT_SK1$score / WT_avrg
hop1_SK1$score <- hop1_SK1$score / hop1_avrg * nf
# Compute average signal per chromosome using normalized signal
WT_chrs <- average_chr_signal(WT_SK1)[[1]]
WT_chrs$strain <- 'HOP1'
hop1_chrs <- average_chr_signal(hop1_SK1)[[1]]
hop1_chrs$strain <- 'hop1∆'
chr_signal <- rbind(WT_chrs, hop1_chrs)
# Add chromosome sizes
chr_lengths <- seqlengths(get_chr_coordinates())
chr_lengths <- data.frame(chr=names(chr_lengths), chr_len=chr_lengths)
chr_signal <- dplyr::full_join(
chr_signal, subset(chr_lengths, str_detect(chr, '_SK1')), by=c('chr'='chr'))
# Order samples
chr_signal$strain <- factor(chr_signal$strain, levels=c('HOP1', 'hop1∆'))
facet_names <- list('HOP1'=expression(italic('HOP1')),
'hop1∆'=expression(paste(italic('hop1'), Delta)))
facet_labeller <- function(variable, value){return(facet_names[value])}
ggplot(chr_signal, aes(chr_len / 10^6, avrg_signal, colour=strain)) +
geom_point(stat="identity", size=2, alpha=0.8) +
facet_wrap(~strain, ncol=3, labeller=facet_labeller) +
scale_color_manual('', values=c(wt_color, hop1_color), guide=FALSE) +
labs(title = '', x = 'Chromosome size (Mb)',
y='Red1 occupancy\n(average per bp)') +
geom_hline(yintercept=1, lty=3) +
scale_x_continuous(limits = c(0, 1.6), breaks = c(0, 0.5, 1.0, 1.5)) +
ylim(0, 1.7) +
theme(strip.background = element_blank())