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FigureS3.R
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FigureS3.R
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#------------------------------------------------------------------------------#
# #
# Figure S1 #
# #
#------------------------------------------------------------------------------#
library(here)
library(wesanderson)
library(tidyverse)
library(GenomicRanges)
# Load paths to data files
source(here('helper_data_file_locations.R'))
# Load IO code
source(here('helper_io.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")
spiked_color <- wes_colors[2]
ctrl_color <- 'black'
#------------------------------------------------------------------------------#
# Panel A #
# In silico spike-in versus control: Red1 fragment pileup example #
# EDIT: Drop in silico experiment; just use spiked versus non-spiked low-Red1 #
#------------------------------------------------------------------------------#
# In the zoom in add SNPs as vertical red bars
# WT_spike_pileup <- import_bedGraph(WT_rep_pileup)
WT_spike_pileup <- import_bedGraph(WT_1st_pileup)
WT_nonspiked_pileup <- import_bedGraph(nonspiked_rep_pileup[['WT']])
# Discard S288C (spike-in) chromosomes and standardize names
WT_spike_pileup <- keepSeqlevels(WT_spike_pileup,
paste0('chr', as.roman(1:16), '_SK1'),
pruning.mode='coarse')
renaming_map_names <- paste0('chr', as.roman(1:16), '_SK1')
renaming_map <- paste0('chr', as.roman(1:16))
names(renaming_map) <- renaming_map_names
WT_spike_pileup <- renameSeqlevels(WT_spike_pileup, renaming_map)
make_df_for_plotting <- function(gr, sample_name='WT',
normalize_by_genome_mean=FALSE, window_size=1,
genome='SK1_S228C', 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_spike_pileup_chrI <- make_df_for_plotting(
WT_spike_pileup, sample_name='Spiked',
normalize_by_genome_mean=FALSE, window_size=100, genome='SK1', chr='chrI')
WT_nonspiked_pileup_chrI <- make_df_for_plotting(
WT_nonspiked_pileup, sample_name='Non-spiked',
normalize_by_genome_mean=FALSE, window_size=100, genome='SK1', chr='chrI')
WT_spike_chrI_region <- make_df_for_plotting(
WT_spike_pileup, sample_name='Spiked',
normalize_by_genome_mean=FALSE, window_size=1, genome='SK1', chr='chrI',
region=c(53000, 57000))
WT_nonspiked_chrI_region <- make_df_for_plotting(
WT_nonspiked_pileup, sample_name='Non-spiked',
normalize_by_genome_mean=FALSE, window_size=1, genome='SK1', chr='chrI',
region=c(53000, 57000))
df_chrI <- rbind(WT_spike_pileup_chrI, WT_nonspiked_pileup_chrI)
df_chrI_region <- rbind(WT_spike_chrI_region, WT_nonspiked_chrI_region)
# Order strains
df_chrI$strain = factor(
df_chrI$strain, levels=c('Spiked', 'Non-spiked'))
df_chrI_region$strain = factor(
df_chrI_region$strain, levels=c('Spiked', 'Non-spiked'))
### Plot
# Whole chromosome
ggplot(df_chrI, aes(position / 1000, signal, colour=strain)) +
scale_color_manual(values=c(spiked_color, ctrl_color)) +
geom_area(position='identity', aes(fill=strain, colour=strain),
alpha=0.5, size=0.25) +
facet_grid(strain ~ .) +
annotate('text', x=23, y=7, label=unique(df_chrI$strain),
size=3, colour=c(spiked_color, ctrl_color)) +
scale_fill_manual(values=c(spiked_color, ctrl_color)) +
labs(title='', x=paste0('Position on chr I (Kb)'), y='') +
theme(legend.position = "none", strip.background = element_blank()) +
labs(y=paste0('Red1 occupancy')) +
ylim(0, 8) +
theme(
strip.background=element_blank(),
strip.text=element_blank()
)
# Annotate SNPs in region plot
SNPs <- read_tsv(SNPs)
SNPs_chrI <- subset(
SNPs, chr == 'chrI_SK1' & position >= 53000 & position <= 57000)
# Selected region
ggplot(df_chrI_region, aes(position / 1000, signal, colour=strain)) +
scale_color_manual(values=c(spiked_color, ctrl_color)) +
geom_area(position='identity', aes(fill=strain, colour=strain),
alpha=0.5, size=0.25) +
facet_grid(strain ~ .) +
annotate('text', x=53.5, y=6, label=unique(df_chrI_region$strain),
size=3, colour=c(spiked_color, ctrl_color)) +
scale_y_continuous(limits=c(-0.7, 8)) +
annotate("text", x=SNPs_chrI$position/1000, y=-0.5,
label = "|", colour='red') +
annotate("text", x=57, y=-0.5, label = "SNPs", colour='red', size=2.5) +
scale_fill_manual(values=c(spiked_color, ctrl_color)) +
labs(title='', x=paste0('Position on chr I (Kb)'), y='') +
theme(legend.position = "none", strip.background = element_blank()) +
labs(y=paste0('Red1 occupancy')) +
# ylim(0, 8) +
theme(
strip.background=element_blank(),
strip.text=element_blank()
)
#------------------------------------------------------------------------------#
# Panel B #
# In silico spike-in versus control: compare peak number and overlap #
# EDIT: Drop in silico experiment; just use spiked versus non-spiked low-Red1 #
#------------------------------------------------------------------------------#
spiked_narrow <- import_MACS2_peaks(
WT_spike_rep_peaks[['narrow']], type='narrow')
nonspiked_narrow <- import_MACS2_peaks(
nonspiked_rep_peaks[['narrow']], type='narrow')
spiked_broad <- import_MACS2_peaks(WT_spike_rep_peaks[['broad']], type='broad')
nonspiked_broad <- import_MACS2_peaks(
nonspiked_rep_peaks[['broad']], type='broad')
# Discard S288C (spike-in) chromosomes and standardize names
spiked_narrow <- keepSeqlevels(spiked_narrow,
paste0('chr', as.roman(1:16), '_SK1'),
pruning.mode='coarse')
spiked_broad <- keepSeqlevels(spiked_broad,
paste0('chr', as.roman(1:16), '_SK1'),
pruning.mode='coarse')
renaming_map_names <- paste0('chr', as.roman(1:16), '_SK1')
renaming_map <- paste0('chr', as.roman(1:16))
names(renaming_map) <- renaming_map_names
spiked_narrow <- renameSeqlevels(spiked_narrow, renaming_map)
spiked_broad <- renameSeqlevels(spiked_broad, renaming_map)
length(spiked_narrow)
length(nonspiked_narrow)
length(spiked_broad)
length(nonspiked_broad)
# Mind the difference between:
# 1. all overlaps (equal to the reciprocal)
# 2. all peaks with overlaps (NOT equal to the reciprocal)
query <- spiked_narrow
subject <- nonspiked_narrow
message('Overlaps: ')
message(' A in B = B in A: ',
sum(countOverlaps(query, subject)), ' = ',
sum(countOverlaps(subject, query)))
message()
message('Peaks with overlaps: ')
message(' A in B != B in A: ',
length(subsetByOverlaps(query, subject)), ' != ',
length(subsetByOverlaps(subject, query)))
# Overlaps between pairs of samples
overlaps_between <- function(query, subject){
message('Peaks in ', deparse(substitute(query)), ' vs ',
deparse(substitute(subject)), ': ')
message(' overlapping: ', length(subsetByOverlaps(query, subject)))
message(' unique: ', length(query[!(query %over% subject)]))
message('Peaks in ', deparse(substitute(subject)), ' vs ',
deparse(substitute(query)), ': ')
message(' overlapping: ', length(subsetByOverlaps(subject, query)))
message(' unique: ', length(subject[!(subject %over% query)]))
get_overlaps <- function(x, y) {
overlaps <- list(overlap = subsetByOverlaps(x, y),
unique = x[!(x %over% y)])
return(overlaps)
}
q_on_s <- get_overlaps(query, subject)
s_on_q <- get_overlaps(subject, query)
return(list(q_on_s=q_on_s, s_on_q=s_on_q))
}
on <- overlaps_between(spiked_narrow, nonspiked_narrow)
ob <- overlaps_between(spiked_broad, nonspiked_broad)
narrow_peak_counts <- as.data.frame(matrix(nrow=6, ncol=3))
colnames(narrow_peak_counts) <- c('Intersection', 'Count', 'Sample')
narrow_peak_counts$Intersection <- c('All', 'Intersected', 'Unique')
narrow_peak_counts$Sample <- c(rep('Non-spiked', 3),
rep('Spiked', 3))
narrow_peak_counts$Count <- c(length(on$s_on_q$overlap) + length(on$s_on_q$unique),
length(on$s_on_q$overlap),
length(on$s_on_q$unique),
length(on$q_on_s$overlap) + length(on$q_on_s$unique),
length(on$q_on_s$overlap),
length(on$q_on_s$unique))
broad_peak_counts <- as.data.frame(matrix(nrow=6, ncol=3))
colnames(broad_peak_counts) <- c('Intersection', 'Count', 'Sample')
broad_peak_counts$Intersection <- c('All', 'Intersected', 'Unique')
broad_peak_counts$Sample <- c(rep('Non-spiked', 3),
rep('Spiked', 3))
broad_peak_counts$Count <- c(length(ob$s_on_q$overlap) + length(ob$s_on_q$unique),
length(ob$s_on_q$overlap),
length(ob$s_on_q$unique),
length(ob$q_on_s$overlap) + length(ob$q_on_s$unique),
length(ob$q_on_s$overlap),
length(ob$q_on_s$unique))
# Order intersection
narrow_peak_counts$Intersection = factor(
narrow_peak_counts$Intersection, levels=c('Unique', 'Intersected'))
broad_peak_counts$Intersection = factor(
broad_peak_counts$Intersection, levels=c('Unique', 'Intersected'))
ggplot(narrow_peak_counts[
narrow_peak_counts$Intersection %in% c('Unique', 'Intersected'), ],
aes(Sample, Count, alpha=Intersection)) +
geom_bar(stat='identity', colour='white', fill='Black') +
scale_alpha_manual('', values=c(0.5, 1)) +
ylim(0, 2900) +
# scale_fill_manual('', values=c(insilico_spike_color, ctrl_color)) +
labs(x='', y='Narrow peak count') +
theme(axis.text.x = element_text(vjust=0.5))
ggplot(broad_peak_counts[
broad_peak_counts$Intersection %in% c('Unique', 'Intersected'), ],
aes(Sample, Count, alpha=Intersection)) +
geom_bar(stat='identity', colour='white', fill='Black') +
# ylim(0, 2600) +
scale_alpha_manual('', values=c(0.5, 1)) +
ylim(0, 2900) +
# scale_fill_manual('', values=c(insilico_spike_color, ctrl_color)) +
labs(x='', y='Broad peak count') +
theme(axis.text.x = element_text(vjust=0.5))