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FigureS4.R
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FigureS4.R
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#------------------------------------------------------------------------------#
# #
# Figure S5 #
# #
#------------------------------------------------------------------------------#
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'
dot1_color <- wes_colors[1]
set1_color <- wes_colors[4]
set1dot1_color <- wes_colors[8]
#------------------------------------------------------------------------------#
# 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('ah8583', ignore_case=TRUE))]
# Use all WT replicates
WT_chip_rep_counts <- list(
read_counts_per_chr[['ah119spikeb-062817_trimmed']],
read_counts_per_chr[['ah119spikeb-100917_trimmed']],
read_counts_per_chr[['ah119spike-chip_trimmed']])
WT_input_rep_counts <- list(
read_counts_per_chr[['ah119spikea-062817_trimmed']],
read_counts_per_chr[['ah119spikea-100917_trimmed']],
read_counts_per_chr[['ah119spike-inp_trimmed']])
sinfs <- data.frame(
sample=c('WT', rep('dot1∆', 2), rep('set1∆', 2), rep('dot1∆ set1∆', 2)),
Red1=c(1,
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8104spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8104spikea-100917_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8104spike-chip_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8104spike-inp_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8584spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8584spikea-100917_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8584spike-chip_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8584spike-inp_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8583spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8583spikea-100917_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8583spike-chip_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8583spike-inp_trimmed']]))))
# Order samples
sinfs$sample <- factor(
sinfs$sample, levels=c('WT', 'dot1∆', 'set1∆', 'dot1∆ set1∆'))
# Plot
color_values <- c(wt_color, dot1_color, set1_color, set1dot1_color)
ggplot(sinfs, aes(sample, Red1 * 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=color_values, guide=FALSE) +
scale_fill_manual('', values=color_values, guide=FALSE) +
scale_x_discrete(labels=c(
'Wild type', expression(paste(italic('dot1'), Delta)),
expression(paste(italic('set1'), Delta)),
expression(paste(italic('dot1'), Delta, italic('set1'), Delta)))) +
ylim(0, 100) +
labs(title = '', x = '', y = 'Red1 amount\n(% of wild type)')
#------------------------------------------------------------------------------#
# Panel B #
# Average Red1 signal per chromosome #
#------------------------------------------------------------------------------#
signal_tracks <- list(
'WT'=import_bedGraph(WT_1st_pileup),
'dot1∆'=import_bedGraph(dot1_1st_pileup),
'set1∆'=import_bedGraph(set1_1st_pileup),
'dot1∆ set1∆'=import_bedGraph(set1dot1_1st_pileup)
)
# Drop spike-in genome
signal_tracks <- lapply(
signal_tracks, keepSeqlevels,
paste0('chr', as.roman(1:16), '_SK1'), pruning.mode="coarse")
avrgs <- lapply(signal_tracks, function(x) average_chr_signal(x)[[2]])
# Compute spike-in normalization factor
# 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('ah8583', ignore_case=TRUE))]
# Use all WT replicates
WT_chip_rep_counts <- list(
read_counts_per_chr[['ah119spikeb-062817_trimmed']],
read_counts_per_chr[['ah119spikeb-100917_trimmed']],
read_counts_per_chr[['ah119spike-chip_trimmed']])
WT_input_rep_counts <- list(
read_counts_per_chr[['ah119spikea-062817_trimmed']],
read_counts_per_chr[['ah119spikea-100917_trimmed']],
read_counts_per_chr[['ah119spike-inp_trimmed']])
sinfs <- data.frame(
sample=c('WT', rep('dot1∆', 2), rep('set1∆', 2), rep('dot1∆ set1∆', 2)),
Red1=c(1,
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8104spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8104spikea-100917_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8104spike-chip_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8104spike-inp_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8584spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8584spikea-100917_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8584spike-chip_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8584spike-inp_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8583spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8583spikea-100917_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8583spike-chip_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8583spike-inp_trimmed']]))))
# Normalize by average signal and then spike-in factor
for (i in seq_along(signal_tracks)) {
sample <- names(signal_tracks)[i]
nf <- mean(sinfs[sinfs$sample == sample, 'Red1'])
signal_tracks[[i]]$score <- signal_tracks[[i]]$score / avrgs[[sample]] * nf
}
# Compute average signal per chromosome using normalized signal
chr_signal <- lapply(signal_tracks, function(x) average_chr_signal(x)[[1]])
for (i in seq_along(chr_signal)) chr_signal[[i]]$strain <- names(chr_signal)[i]
chr_signal <- do.call('rbind', chr_signal)
# 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('WT', 'dot1∆', 'set1∆', 'dot1∆ set1∆'))
facet_names <- list('WT'='Wild type',
'dot1∆'=expression(paste(italic('dot1'), Delta)),
'set1∆'=expression(paste(italic('dot1'), Delta)),
'dot1∆ set1∆'=expression(paste(italic('dot1'), Delta,
italic('set1'), 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=4, labeller=facet_labeller) +
scale_color_manual(
'', values=c(wt_color, dot1_color, set1_color, set1dot1_color),
guide=FALSE) +
#scale_y_continuous(limits = c(0, 2.4)) +
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())
#------------------------------------------------------------------------------#
# Panel C #
# Red1 fragment pileup on selected example chromosomes #
#------------------------------------------------------------------------------#
WT <- import_bedGraph(WT_1st_pileup)
dot1 <- import_bedGraph(dot1_1st_pileup)
set1 <- import_bedGraph(set1_1st_pileup)
set1dot1 <- import_bedGraph(set1dot1_1st_pileup)
### Normalize signal
# Compute genome-wide signal average
WT_avrg <- average_chr_signal(
subset(WT, str_detect(WT@seqnames, '_SK1')))[[2]]
dot1_avrg <- average_chr_signal(
subset(dot1, str_detect(dot1@seqnames, '_SK1')))[[2]]
set1_avrg <- average_chr_signal(
subset(set1, str_detect(set1@seqnames, '_SK1')))[[2]]
set1dot1_avrg <- average_chr_signal(
subset(set1dot1, str_detect(set1dot1@seqnames, '_SK1')))[[2]]
# Compute spike-in normalization factor
subset(names(read_counts_per_chr),
str_detect(names(read_counts_per_chr), '8104'))
# Use all WT replicates
WT_chip_rep_counts <- list(
read_counts_per_chr[['ah119spikeb-062817_trimmed']],
read_counts_per_chr[['ah119spikeb-100917_trimmed']],
read_counts_per_chr[['ah119spike-chip_trimmed']])
WT_input_rep_counts <- list(
read_counts_per_chr[['ah119spikea-062817_trimmed']],
read_counts_per_chr[['ah119spikea-100917_trimmed']],
read_counts_per_chr[['ah119spike-inp_trimmed']])
nfs <- data.frame(
sample=c('dot1∆', 'set1∆', 'dot1∆ set1∆'),
nf=c(suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8104spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8104spikea-100917_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8584spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8584spikea-100917_trimmed']])),
suppressMessages(
spikein_normalization_factor_from_counts(
ref_chip_counts=WT_chip_rep_counts,
ref_input_counts=WT_input_rep_counts,
test_chip_counts=read_counts_per_chr[[
'ah8583spikeb-100917_trimmed']],
test_input_counts=read_counts_per_chr[[
'ah8583spikea-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
nf <- as.numeric(subset(nfs, sample == 'dot1∆', select='nf'))
dot1[str_detect(dot1@seqnames, '_SK1')]$score <-
dot1[str_detect(dot1@seqnames, '_SK1')]$score / dot1_avrg * nf
nf <- as.numeric(subset(nfs, sample == 'set1∆', select='nf'))
set1[str_detect(set1@seqnames, '_SK1')]$score <-
set1[str_detect(set1@seqnames, '_SK1')]$score / set1_avrg * nf
nf <- as.numeric(subset(nfs, sample == 'dot1∆ set1∆', select='nf'))
set1dot1[str_detect(set1dot1@seqnames, '_SK1')]$score <-
set1dot1[str_detect(set1dot1@seqnames, '_SK1')]$score / set1dot1_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='WT', normalize_by_genome_mean=FALSE,
window_size=100, chr='chrI_SK1')
dot1_chrI <- make_df_for_plotting(
dot1, sample_name='dot1∆', normalize_by_genome_mean=FALSE,
window_size=100, chr='chrI_SK1')
set1_chrI <- make_df_for_plotting(
set1, sample_name='set1∆', normalize_by_genome_mean=FALSE,
window_size=100, chr='chrI_SK1')
set1dot1_chrI <- make_df_for_plotting(
set1dot1, sample_name='dot1∆ set1∆', normalize_by_genome_mean=FALSE,
window_size=100, chr='chrI_SK1')
df_chrI <- rbind(WT_chrI, dot1_chrI, set1_chrI, set1dot1_chrI)
WT_chrII <- make_df_for_plotting(
WT, sample_name='WT', normalize_by_genome_mean=FALSE,
window_size=200, chr='chrII_SK1')
dot1_chrII <- make_df_for_plotting(
dot1, sample_name='dot1∆', normalize_by_genome_mean=FALSE,
window_size=200, chr='chrII_SK1')
set1_chrII <- make_df_for_plotting(
set1, sample_name='set1∆', normalize_by_genome_mean=FALSE,
window_size=200, chr='chrII_SK1')
set1dot1_chrII <- make_df_for_plotting(
set1dot1, sample_name='dot1∆ set1∆', normalize_by_genome_mean=FALSE,
window_size=200, chr='chrII_SK1')
df_chrII <- rbind(WT_chrII, dot1_chrII, set1_chrII, set1dot1_chrII)
# Order strains
df_chrI$strain = factor(
df_chrI$strain, levels=c('WT', 'dot1∆', 'set1∆', 'dot1∆ set1∆'))
df_chrII$strain = factor(
df_chrII$strain, levels=c('WT', 'dot1∆', 'set1∆', 'dot1∆ set1∆'))
# 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)
color_values <- c(wt_color, dot1_color, set1_color, set1dot1_color)
ggplot(df_chrI, aes(position / 1000, signal, colour=strain)) +
scale_color_manual(values=color_values) +
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$cen_mid / 1000, y=-1.5, label="CEN",
size=2, colour='black') +
facet_grid(strain ~ .) +
scale_fill_manual(values=color_values) +
labs(title='',
x=paste0('Position on chr I (Kb)'), y='') +
theme(legend.position = "none") +
labs(y=paste0('Red1 occupancy')) +
ylim(-2, 14) +
theme(
strip.background=element_blank(),
strip.text=element_blank()
)
cen_mid <- cen_midpoint(chr='chrII_SK1', ref_genome='SK1_S288C', y_coord=0)
annotation_text <- data.frame(
position=750000, signal=10,
strain=factor(c('WT', 'dot1∆', 'set1∆', 'dot1∆ set1∆'),
levels=c('WT', 'dot1∆', 'set1∆', 'dot1∆ set1∆')))
ggplot(df_chrII, aes(position / 1000, signal, colour=strain)) +
scale_color_manual('', values=color_values) +
geom_area(position='identity', aes(fill=strain, colour=strain),
alpha=0.75, size=0.25) +
geom_point(data=cen_mid, aes(cen_mid/1000, 0.2), size=1, colour='black') +
annotate("text", x=cen_mid$cen_mid / 1000, y=-1.5, label="CEN",
size=2, colour='black') +
facet_grid(strain ~ .) +
geom_text(data=annotation_text,
label=c("paste('Wild ', type)",
"paste(italic('dot1'), Delta)",
"paste(italic('set1'), Delta)",
"paste(italic('dot1'), Delta, italic(' set1'), Delta)"),
parse=TRUE, size=3, colour=color_values) +
scale_fill_manual('', values=color_values) +
labs(title='',
x=paste0('Position on chr II (Kb)'), y='') +
theme(legend.position = "none") +
labs(y=paste0('Red1 occupancy')) +
ylim(-2, 14) +
theme(
strip.background=element_blank(),
strip.text=element_blank()
)