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Fig_3.Rmd
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---
title: "Figure 3 and associated extended data figures"
author: "Sam Buckberry"
date: "`r Sys.Date()`"
output: github_document
---
```{r, message=FALSE, warning=FALSE, error=FALSE}
source("R/project_functions.R")
```
Load DNA methylation metadata
```{r, cache=TRUE}
mdat <- read.csv("wgbs/metadata/wgbs_metadata_local.csv")
```
Load the DMR object with annotations
```{r, cache=TRUE}
all_dmrs <- readRDS("wgbs/processed_data/classified_dmrs_granges.Rds")
```
Write DMR results to file
```{r, cache=TRUE}
all_dmrs_df <- as.data.frame(all_dmrs)
all_dmrs_df$loci <- gr_to_loci(all_dmrs)
write.table(all_dmrs_df, "wgbs/processed_data/all_dmrs_reults.csv", quote = FALSE, sep = ",", row.names = FALSE)
```
Make CG-DMR mCG/CG matrix for relevant samples
```{r, cache=TRUE}
fib_libs <- c("RL415", "RL702")
primed_libs <- c("RL417", "RL418", "RL703", "RL1751_1771")
naive_libs <- c("RL411", "RL412", "RL838")
tnt_libs <- c("RL1124", "RL1125")
ntp_libs <- c("RL936", "RL937", "RL837")
esc_libs <- c("RL2352_merge", "RL2351_merge", "SRS114877",
"SRR1561745_merge", "SRS004213", "SRS606777", "SRS606778")
libs <- c(fib_libs, primed_libs, naive_libs, tnt_libs, ntp_libs, esc_libs)
ind <- match(libs, mdat$Library_id)
dmr_mCG_matrix <- make_mC_matrix(obj_fls = mdat$BSseq_CG[ind],
gr = all_dmrs, cores = 4)
colnames(dmr_mCG_matrix) <- mdat$Library_id[ind]
```
Caclculate mean delta for hiPSCs compared to hESCs
```{r, cache=TRUE}
## Calculate mean delta
esc_n2p_d13_delta <- rowMeans(dmr_mCG_matrix[ ,tnt_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,esc_libs], na.rm = TRUE)
esc_n2p_p10_delta <- rowMeans(dmr_mCG_matrix[ ,ntp_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,esc_libs], na.rm = TRUE)
esc_naive_delta <- rowMeans(dmr_mCG_matrix[ ,naive_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,esc_libs], na.rm = TRUE)
esc_ips_delta <- rowMeans(dmr_mCG_matrix[ ,primed_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,esc_libs], na.rm = TRUE)
esc_fib_delta <- rowMeans(dmr_mCG_matrix[ ,fib_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,esc_libs], na.rm = TRUE)
all_dmrs$esc_n2p_d13_delta <- esc_n2p_d13_delta
all_dmrs$esc_n2p_p10_delta <- esc_n2p_p10_delta
all_dmrs$esc_ips_delta <- esc_ips_delta
all_dmrs$esc_naive_delta <- esc_naive_delta
#all_dmrs$esc_fib_delta <- esc_fib_delta
all_dmrs$loci <- gr_to_loci(all_dmrs)
```
Plot DMR delta values
```{r, cache=TRUE, warning=FALSE}
delta_df <- data.frame(TNT=all_dmrs$esc_n2p_d13_delta,
NtP=all_dmrs$esc_n2p_p10_delta,
Primed=all_dmrs$esc_ips_delta,
Naive=all_dmrs$esc_naive_delta,
#Fibroblast=esc_fib_delta,
class=all_dmrs$class)
delta_df_melt <- reshape2::melt(delta_df)
delta_df_melt$variable <- factor(delta_df_melt$variable,
levels=c("Primed", "Naive", "NtP", "TNT"))
delta_df_melt$class <- factor(delta_df_melt$class)
line_mm <- 0.5 / 2.835
gg_delta_correct <- ggplot(delta_df_melt,
aes(x = value, group=variable,
color=variable,
fill=variable)) +
geom_histogram(bins = 30, position="identity") +
geom_vline(xintercept = c(-.2, .2), linetype='dashed', size = line_mm) +
#geom_density() +
scale_color_manual(values=reprog_pal[c("Primed", "Naive", "TNT", "NtP")]) +
scale_fill_manual(values=reprog_pal[c("Primed", "Naive", "TNT", "NtP")]) +
facet_grid(class~variable, scales = "free_y") +
scale_x_continuous(limits = c(-1, 1)) +
xlab("DMR mCG delta \n(iPSC mCG - ESC mCG)") +
ylab("Number of DMRs") +
sams_pub_theme(legend_pos = 'right', x.text.angle = 0, hjust = 0.5)
pdf("wgbs/plots/mcg_dmr_esc_delta_histograms.pdf", width = 5, height = 2)
gg_delta_correct
dev.off()
gg_delta_correct_combined <- ggplot(delta_df_melt,
aes(x = value, group=variable,
color=variable,
fill=variable)) +
geom_histogram(bins = 30, position="identity") +
geom_vline(xintercept = c(-.2, .2), linetype='dashed', size = line_mm) +
#geom_density() +
scale_color_manual(values=reprog_pal[c("Primed", "Naive", "TNT", "NtP")]) +
scale_fill_manual(values=reprog_pal[c("Primed", "Naive", "TNT", "NtP")]) +
facet_grid(variable~.) +
scale_x_continuous(limits = c(-1, 1)) +
xlab("DMR mCG delta \n(iPSC mCG - ESC mCG)") +
ylab("Number of DMRs") +
sams_pub_theme(legend_pos = 'right', x.text.angle = 0, hjust = 0.5)
pdf("wgbs/plots/mcg_dmr_esc_delta_histograms_combined_vertical.pdf", width = 2.5, height = 3.25)
gg_delta_correct_combined
dev.off()
```
```{r, cache=TRUE}
wb_fig3 <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb_fig3, sheetName = "Fig_3c")
openxlsx::writeData(wb = wb_fig3, sheet = "Fig_3c",
x = gg_delta_correct_combined$data)
```
Assign DMRs as corrected or uncorrected and write bed files
```{r, cache=TRUE}
## Seperate if corrected or uncorrected
all_dmrs$tnt_corrected <- abs(all_dmrs$esc_n2p_d13_delta) < 0.2
all_dmrs$ntp_corrected <- abs(all_dmrs$esc_n2p_p10_delta) < 0.2
all_dmrs$both_corrected <- (all_dmrs$tnt_corrected == TRUE) &
(all_dmrs$ntp_corrected == TRUE)
all_dmrs$any_corrected <- (all_dmrs$tnt_corrected == TRUE) | (all_dmrs$ntp_corrected == TRUE)
all_dmrs$correction_class <- str_c(all_dmrs$class, "_", all_dmrs$both_corrected)
table(all_dmrs$correction_class)
all_dmrs
corrected_dmr_gr <- all_dmrs[all_dmrs$any_corrected == TRUE]
corrected_dmr_df <- data.frame(chr=seqnames(corrected_dmr_gr),
start=start(corrected_dmr_gr),
end=end(corrected_dmr_gr),
name=1:length(corrected_dmr_gr),
score=corrected_dmr_gr$esc_ips_delta, strand="+")
write.table(x = corrected_dmr_df, file = "wgbs/processed_data/corrected_dmrs.bed",
quote = FALSE, sep = "\t", row.names = FALSE, col.names = FALSE)
uncorrected_dmr_gr <- all_dmrs[all_dmrs$any_corrected == FALSE]
uncorrected_dmr_df <- data.frame(chr=seqnames(uncorrected_dmr_gr),
start=start(uncorrected_dmr_gr),
end=end(uncorrected_dmr_gr),
name=1:length(uncorrected_dmr_gr),
score=uncorrected_dmr_gr$esc_ips_delta, strand="+")
write.table(x = uncorrected_dmr_df, file = "wgbs/processed_data/uncorrected_dmrs.bed",
quote = FALSE, sep = "\t", row.names = FALSE, col.names = FALSE)
saveRDS(object = all_dmrs, file = "wgbs/processed_data/all_dmrs_annotated_granges.Rds")
```
Plot corrected fraction of DMRs
```{r, cache=TRUE}
# barplot correction fraction
correction_tab <- mcols(all_dmrs)[ ,c("tnt_corrected", "ntp_corrected", "class")] %>%
data.frame() %>% reshape2::melt(id.vars=c('class')) %>%
dplyr::group_by(class, variable, value) %>% dplyr::tally()
correction_tab$variable <- ifelse(test = correction_tab$variable == "tnt_corrected",
yes = "TNT-hiPSCs", no = "NtP-hiPSCs")
correction_tab$group <- ifelse(test = grepl(pattern = "emory",
x = correction_tab$class),
yes = "Memory", no = "Abberant")
bar_cols <- as.character(c(reprog_pal[7], vitC[2]))
dmr_element_corrected_gg <- ggplot(correction_tab,
aes(x = variable, y=n, fill=value)) +
geom_bar(position = "fill", stat="identity", alpha=0.9) +
facet_grid(.~group+class, scales = "free") +
scale_fill_manual(values = bar_cols) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.text.y = element_text(size=10)) +
ylab("Proportion of DMRs") +
scale_y_continuous(expand = c(0,0)) +
sams_pub_theme(legend_pos = "right")
dmr_element_corrected_gg
pdf("wgbs/plots/dmr_class_corrected_barplots.pdf",
width = 4, height =2)
dmr_element_corrected_gg
dev.off()
```
```{r, cache=TRUE}
wb_ed_fig3 <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb_ed_fig3, sheetName = "Fig_3f")
openxlsx::writeData(wb = wb_ed_fig3, sheet = "Fig_3f",
x = dmr_element_corrected_gg$data)
```
```{r, cache=TRUE}
correction_tab2 <- mcols(all_dmrs)[ ,c("tnt_corrected", "ntp_corrected", "loci")] %>%
data.frame() %>% reshape2::melt(id.vars='loci') %>%
dplyr::group_by(variable, value) %>% dplyr::tally()
correction_tab2$variable <- ifelse(test = correction_tab2$variable == "tnt_corrected",
yes = "TNT-hiPSC", no = "NtP-hiPSC")
dmr_element_corrected2_gg <- ggplot(correction_tab2,
aes(x = variable, y=n, fill=value)) +
geom_bar(position = "fill", stat="identity", alpha=0.9) +
scale_fill_manual(values = bar_cols) +
# theme(axis.text.x = element_text(angle = 0, hjust = 0.5, size = 10),
# axis.text.y = element_text(size=10)) +
ylab("Proportion of DMRs") +
coord_flip() +
scale_y_continuous(expand = c(0,0)) +
sams_pub_theme(legend_pos = "right", x.text.angle = 0, hjust = 0.5)
pdf("wgbs/plots/dmr_corrected_barplots.pdf", width = 2.9, height = 1)
dmr_element_corrected2_gg
dev.off()
sum(all_dmrs$tnt_corrected)
sum(all_dmrs$ntp_corrected)
dmr_element_corrected2_gg
```
```{r, cache=TRUE}
openxlsx::addWorksheet(wb_fig3, sheetName = "Fig_3b")
openxlsx::writeData(wb = wb_fig3, sheet = "Fig_3b",
x = dmr_element_corrected2_gg$data)
```
Venn diagram of corrected DMR overlaps
```{r, cache=TRUE}
venn_df <- data.frame(tnt=sum(all_dmrs$tnt_corrected),
ntp=sum(all_dmrs$ntp_corrected),
olap=sum(all_dmrs$both_corrected))
pdf(file = "wgbs/plots/DMR_corrected_venn.pdf", width = 2, height = 2)
VennDiagram::draw.pairwise.venn(area1 = venn_df$tnt,
area2 = venn_df$ntp,
cross.area = venn_df$olap, euler.d = TRUE)
dev.off()
VennDiagram::draw.pairwise.venn(area1 = venn_df$tnt,
area2 = venn_df$ntp,
cross.area = venn_df$olap, euler.d = TRUE)
```
```{r, cache=TRUE}
openxlsx::addWorksheet(wb_ed_fig3, sheetName = "Fig_3e")
openxlsx::writeData(wb = wb_ed_fig3, sheet = "Fig_3e",
x = venn_df)
```
Plot DMR correction heatmap
```{r, cache=TRUE}
## Rank matrix by variance
dmr_dat <- dmr_mCG_matrix
var_dat <- dmr_mCG_matrix[ ,c(esc_libs, primed_libs)] %>% as.matrix() %>% rowVars()
dmr_dat <- dmr_dat[order(var_dat, decreasing = FALSE), ]
# Split heatmap by corrected and uncorrected
corrected_ids <- all_dmrs$loci[all_dmrs$any_corrected == TRUE]
uncorrected_ids <- all_dmrs$loci[all_dmrs$any_corrected == FALSE]
dmr_dat_split <- rbind(dmr_dat[rownames(dmr_dat) %in% corrected_ids, ],
dmr_dat[rownames(dmr_dat) %in% uncorrected_ids, ])
heatmap_ind <- match(colnames(dmr_dat_split), mdat$Library_id)
colnames(dmr_dat_split) <- mdat$Manuscript.Name[heatmap_ind]
png(filename = "wgbs/plots/dmr_var_sorted_correction_split_heatmap.png",
height = 2.5, width = 3, units = "in", res = 300)
pheatmap(dmr_dat_split, cluster_rows = FALSE, cluster_cols = FALSE, fontsize = 6,
show_rownames = FALSE, gaps_col = c(2,6,9,11,14),
gaps_row = length(corrected_ids), border_color = NA)
dev.off()
pdf(file = "wgbs/plots/dmr_var_sorted_correction_split_heatmap.pdf",
height = 2.5, width = 3)
pheatmap(dmr_dat_split, cluster_rows = FALSE, cluster_cols = FALSE, fontsize = 6,
show_rownames = FALSE, gaps_col = c(2,6,9,11,14),
gaps_row = length(corrected_ids), border_color = NA)
dev.off()
gr_to_bed(gr = GRanges(seqnames = rownames(dmr_dat)),
out_path = str_c("wgbs/processed_data/all_DMR_variance_sort.bed"))
pheatmap(dmr_dat_split, cluster_rows = FALSE, cluster_cols = FALSE, fontsize = 6,
show_rownames = FALSE, gaps_col = c(2,6,9,11,14),
gaps_row = length(corrected_ids), border_color = NA)
```
```{r, cache=TRUE}
openxlsx::addWorksheet(wb_fig3, sheetName = "Fig_3d")
openxlsx::writeData(wb = wb_fig3, sheet = "Fig_3d",
x = dmr_dat_split)
```
Profile plot of all samples against hESC mean
```{r, cache=TRUE}
stopifnot(all(rownames(dmr_mCG_matrix) == all_dmrs$loci))
dmr_sample_delta_mat <- dmr_mCG_matrix - (rowMeans(dmr_mCG_matrix[ ,esc_libs]))
dmr_sample_delta_mat <- dmr_sample_delta_mat[ ,!colnames(dmr_sample_delta_mat) %in% fib_libs]
dmr_sample_delta_mat <- data.frame(dmr_sample_delta_mat)
dmr_sample_delta_mat$class <- as.character(all_dmrs$class)
dmr_sample_delta_mat <- reshape2::melt(dmr_sample_delta_mat)
dmr_sample_delta_mat <- dmr_sample_delta_mat[!is.na(dmr_sample_delta_mat$value), ]
head(dmr_sample_delta_mat)
table(dmr_sample_delta_mat$variable)
dmr_sample_delta_mat$sample_group <- NA
sample_order <- c(esc_libs, primed_libs, naive_libs, tnt_libs, ntp_libs) %>% rev()
dmr_sample_delta_mat$variable <- factor(dmr_sample_delta_mat$variable,
levels=sample_order)
dmr_sample_delta_mat$sample_group[dmr_sample_delta_mat$variable %in% primed_libs] <- "Primed-hiPSC"
dmr_sample_delta_mat$sample_group[dmr_sample_delta_mat$variable %in% tnt_libs] <- "TNT-hiPSC"
dmr_sample_delta_mat$sample_group[dmr_sample_delta_mat$variable %in% ntp_libs] <- "NtP-hiPSC"
dmr_sample_delta_mat$sample_group[dmr_sample_delta_mat$variable %in% esc_libs] <- "hESC"
dmr_sample_delta_mat$sample_group[dmr_sample_delta_mat$variable %in% naive_libs] <- "Naive-hiPSC"
dens_ind <- match(dmr_sample_delta_mat$variable, mdat$Library_id)
dmr_sample_delta_mat$id <- mdat$Manuscript.Name[dens_ind]
factor_ind <- match(sample_order, mdat$Library_id)
dmr_sample_delta_mat$id <- factor(dmr_sample_delta_mat$id, levels=mdat$Manuscript.Name[factor_ind])
gg_ridges <- ggplot(data = dmr_sample_delta_mat,
mapping = aes(y = id, x = value, fill=stat(x))) +
#facet_grid(sample_group~., drop = TRUE, scales = "free_y") +
geom_density_ridges_gradient(height=3) +
scale_fill_gradient2(low = "firebrick", mid = "white", high = "firebrick", midpoint = 0) +
geom_vline(xintercept = c(-0.2, .2), alpha=0.4, linetype='dashed') +
scale_x_continuous(limits = c(-1, 1)) + xlab("Cell line DMR mCG - hESC mean") +
sams_pub_theme(x.text.angle = 0, legend_pos = "right", hjust = 0.5)
pdf("wgbs/plots/mcg_dmr_esc_delta_density_sample_plots_.pdf", width = 3.5, height = 4)
gg_ridges
dev.off()
gg_ridges
```
```{r, cache=TRUE}
openxlsx::addWorksheet(wb_ed_fig3, sheetName = "Fig_3c")
openxlsx::writeData(wb = wb_ed_fig3, sheet = "Fig_3c",
x = gg_ridges$data)
```
DMR correction scatter-plots for hiPSCs relative to hESCs and fibroblasts
```{r, cache=TRUE}
# Make scatterplot of DMR correction
primed_delta <- rowMeans(dmr_mCG_matrix[ ,primed_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,esc_libs], na.rm = TRUE)
NtoP_delta <- rowMeans(dmr_mCG_matrix[ ,ntp_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,esc_libs], na.rm = TRUE)
TNT_delta <- rowMeans(dmr_mCG_matrix[ ,tnt_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,esc_libs], na.rm = TRUE)
fib_primed_delta <- rowMeans(dmr_mCG_matrix[ ,primed_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,fib_libs], na.rm = TRUE)
fib_tnt_delta <- rowMeans(dmr_mCG_matrix[ ,tnt_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,fib_libs], na.rm = TRUE)
fib_NtoP_delta <- rowMeans(dmr_mCG_matrix[ ,ntp_libs], na.rm = TRUE) - rowMeans(dmr_mCG_matrix[ ,fib_libs], na.rm = TRUE)
dmr_delta_df <- data.frame(Primed_delta = primed_delta, TNT_delta = TNT_delta, NtoP_delta = NtoP_delta,
fib_primed_delta = fib_primed_delta, fib_tnt_delta = fib_tnt_delta, fib_NtoP_delta = fib_NtoP_delta)
dmr_delta_df$Class <- ifelse(test = primed_delta < 0, yes = "Hypo", no = "Hyper")
primed_scatter <- ggplot(data = dmr_delta_df, mapping = aes(x = Primed_delta, y = fib_primed_delta, colour = Class, fill = Class)) +
geom_vline(xintercept = c(-.2, .2), alpha=0.5) + geom_hline(yintercept = c(-.2, .2), alpha=0.5) +
geom_point(alpha=0.1, inherit.aes = TRUE, size=1) +
scale_x_continuous(limits = c(-1, 1), expand = c(0,0)) + scale_y_continuous(limits = c(-1, 1), expand = c(0,0)) +
ylab("Primed hiPSC mCG - Fibroblast mCG") +
xlab("Primed hiPSC mCG - hESC mCG") +
scale_colour_manual(values = vitC[c(6,1)]) +
#scale_colour_manual(values = brewer.pal(n = 2, name = "Dark2")) +
sams_pub_theme(x.text.angle = 0, hjust = 0.5)
primed_scatter_gg <- ggExtra::ggMarginal(primed_scatter, groupColour = TRUE, groupFill=TRUE)
tnt_scatter <- ggplot(data = dmr_delta_df, mapping = aes(x = TNT_delta, y = fib_tnt_delta, colour = Class, fill = Class)) +
geom_vline(xintercept = c(-.2, .2), alpha=0.5) + geom_hline(yintercept = c(-.2, .2), alpha=0.5) +
geom_point(alpha=0.1, inherit.aes = TRUE, size=1) +
scale_x_continuous(limits = c(-1, 1), expand = c(0,0)) + scale_y_continuous(limits = c(-1, 1), expand = c(0,0)) +
ylab("TNT hiPSC mCG - Fibroblast mCG") +
xlab("TNT hiPSC mCG - hESC mCG") +
scale_colour_manual(values = vitC[c(6,1)]) +
#scale_colour_manual(values = brewer.pal(n = 2, name = "Dark2")) +
sams_pub_theme(x.text.angle = 0, hjust = 0.5)
tnt_scatter_gg <- ggExtra::ggMarginal(tnt_scatter, groupColour = TRUE, groupFill=TRUE)
ntp_scatter <- ggplot(data = dmr_delta_df, mapping = aes(x = NtoP_delta, y = fib_NtoP_delta, colour = Class, fill = Class)) +
geom_vline(xintercept = c(-.2, .2), alpha=0.5) + geom_hline(yintercept = c(-.2, .2), alpha=0.5) +
geom_point(alpha=0.1, inherit.aes = TRUE, size=1) +
scale_x_continuous(limits = c(-1, 1), expand = c(0,0)) + scale_y_continuous(limits = c(-1, 1), expand = c(0,0)) +
ylab("NtP hiPSC mCG - Fibroblast mCG") +
xlab("NtP hiPSC mCG - hESC mCG") +
scale_colour_manual(values = vitC[c(6,1)]) +
#scale_colour_manual(values = brewer.pal(n = 2, name = "Dark2")) +
sams_pub_theme(x.text.angle = 0, hjust = 0.5)
ntp_scatter_gg <- ggExtra::ggMarginal(ntp_scatter, groupColour = TRUE, groupFill=TRUE)
pdf("wgbs/plots/donor_DMR_correction_scatterplots_with_histograms.pdf", height = 2, width = 6)
plot_grid(primed_scatter_gg, tnt_scatter_gg, ntp_scatter_gg, ncol = 3)
dev.off()
```
Primed-hiPSC
```{r, cache=TRUE}
primed_scatter_gg
openxlsx::addWorksheet(wb_ed_fig3, sheetName = "Fig_3d1")
openxlsx::writeData(wb = wb_ed_fig3, sheet = "Fig_3d1",
x = primed_scatter$data)
```
TNT-hiPSC
```{r, cache=TRUE}
tnt_scatter_gg
openxlsx::addWorksheet(wb_ed_fig3, sheetName = "Fig_3d2")
openxlsx::writeData(wb = wb_ed_fig3, sheet = "Fig_3d2",
x = tnt_scatter$data)
```
NtP-hiPSC
```{r, cache=TRUE}
ntp_scatter_gg
openxlsx::addWorksheet(wb_ed_fig3, sheetName = "Fig_3d3")
openxlsx::writeData(wb = wb_ed_fig3, sheet = "Fig_3d3",
x = ntp_scatter$data)
```
Enrichment tests for DMRs (First chunk run on remote server)
```{r, eval=FALSE}
source("R/server_libraries_and_functions.R")
library(regioneR)
## Load CG DMRs
all_dmrs <- readRDS("wgbs/processed_data/all_dmrs_annotated_granges.Rds")
## Load list of genomics elements
all_elements_gr <- readRDS("resources/all_genomic_elements_granges_final.Rds")
### Add CH-DMRs
library(readxl)
real_ch <- read_excel(path = "resources/nature13551-s3.xlsx")
mCH_gr <- GRanges(seqnames = real_ch$chr,
ranges = IRanges(start = as.numeric(real_ch$start),
end = as.numeric(real_ch$end)))
mCH_gr$study <- real_ch$presence
mCH_gr$class <- "CH-DMR"
all_elements_gr <- c(all_elements_gr, mCH_gr)
target_elements <- unique(all_elements_gr$class) %>% as.character()
target_elements <- target_elements[!target_elements %in% c("Other", "Satellite", "snRNA", "tRNA", "srpRNA",
"rRNA", "RC", "scRNA", "RNA")]
unmappable_gr <- bed_to_gr("resources/d0_d3_d7_merged_zero_coverage_regions.bed")
test_element_enrichment <- function(target_element="Enhancer", dmr_class=TRUE,
permutations=200, mask=unmappable_gr){
gc()
message(Sys.time())
message(str_c("Running ", target_element, " ", dmr_class, "..."))
gr_dmr <- all_dmrs[all_dmrs$any_corrected == dmr_class]
target_gr <- all_elements_gr[all_elements_gr$class == target_element]
set.seed(123)
perm_test_results <- regioneR::permTest(A=gr_dmr, B=target_gr,
alternative = "auto",
randomize.function=regioneR::randomizeRegions,
ntimes = permutations,
evaluate.function=regioneR::numOverlaps,
count.once = TRUE,
genome="hg19",
mask = unmappable_gr,
allow.overlaps=FALSE,
per.chromosome=TRUE,
mc.cores=20,
mc.set.seed=FALSE,
force.parallel=TRUE)
lz <- localZScore(pt=perm_test_results, A=gr_dmr, B=target_gr,
step = mean(width(gr_dmr)/2),
window = 100000)
results <- list(pt=perm_test_results, lz=lz, element=target_element,
class=dmr_class)
message("Done!")
return(results)
}
test <- test_element_enrichment(target_element="Promoter", dmr_class=FALSE,
permutations=100, mask=unmappable_gr)
all_dmr_corrected_test <- lapply(X = target_elements,
FUN = test_element_enrichment,
dmr_class=TRUE)
saveRDS(object = all_dmr_corrected_test,
file = "wgbs/processed_data/corrected_dmr_enrichment_tests.Rds")
all_dmr_uncorrected_test <- lapply(X = target_elements,
FUN = test_element_enrichment,
dmr_class=FALSE)
saveRDS(object = all_dmr_uncorrected_test,
file = "wgbs/processed_data/uncorrected_dmr_enrichment_tests.Rds")
```
Plot the enrichment results
```{r, cache=TRUE}
all_dmr_uncorrected_test <- readRDS("wgbs/processed_data/uncorrected_dmr_enrichment_tests.Rds")
all_dmr_corrected_test <- readRDS("wgbs/processed_data/corrected_dmr_enrichment_tests.Rds")
aggregate_enrich_results <- function(x, enrich_list=all_dmr_corrected_test){
dat <- enrich_list[[x]]
df <- data.frame(DMR=dat$class, Element=dat$element, olaps=dat$pt$`regioneR::numOverlaps`$observed,
Pval=dat$pt$`regioneR::numOverlaps`$pval, Zscore=dat$pt$`regioneR::numOverlaps`$zscore)
return(df)
}
corrected_dat <- lapply(1:length(all_dmr_corrected_test), aggregate_enrich_results, enrich_list=all_dmr_corrected_test) %>%
do.call(rbind, .)
uncorrected_dat <- lapply(1:length(all_dmr_uncorrected_test), aggregate_enrich_results, enrich_list=all_dmr_uncorrected_test) %>% do.call(rbind, .)
all_enrich_dat <- rbind(corrected_dat, uncorrected_dat)
all_enrich_dat <- all_enrich_dat[order(all_enrich_dat$Zscore, decreasing = TRUE), ]
all_enrich_dat$Element <- factor(all_enrich_dat$Element,
levels=unique(rev(all_enrich_dat$Element[all_enrich_dat$DMR == TRUE])))
#all_enrich_dat <- all_enrich_dat[all_enrich_dat$olaps > 10, ]
all_enrich_dat$pc <- lapply(X = 1:nrow(all_enrich_dat), FUN = function(x){
ifelse(test = all_enrich_dat$DMR[x] == TRUE,
yes = all_enrich_dat$olaps[x] / length(all_dmrs[all_dmrs$any_corrected == TRUE]),
no = all_enrich_dat$olaps[x] / length(all_dmrs[all_dmrs$any_corrected == FALSE]))
}) %>% unlist()
all_enrich_dat$DMR <- ifelse(test = all_enrich_dat$DMR == TRUE, yes = "Corrected", no = "Uncorrected")
gg_enrich <- ggplot(data = all_enrich_dat, aes(y = Zscore, x = Element, fill=DMR)) +
geom_col(position = 'dodge') +
scale_fill_manual(values = rev(bar_cols)) +
#facet_grid(.~DMR) +
coord_flip() +
sams_pub_theme(x.text.angle = 0, legend_pos = "right")
pdf("wgbs/plots/dmr_enrichment_zscore_barplots_all.pdf", width = 4, height = 2)
gg_enrich
dev.off()
gg_enrich
```
```{r, cache=TRUE}
wb_ed_fig4 <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb_ed_fig4, sheetName = "Fig_4a")
openxlsx::writeData(wb = wb_ed_fig4, sheet = "Fig_4a",
x = gg_enrich$data)
```
```{r, cache=TRUE}
element_order <- c("Fibroblast_H3K9me3", "Fibroblast_PMD", "Fibroblast_LAD",
"Constitutive_H3K9me3", "Constitutive_LAD",
"ESC_H3K9me3", "ESC_LAD")
all_enrich_dat$DMR <- factor(all_enrich_dat$DMR, levels=c("Corrected", "Uncorrected"))
gg_enrich_sub <- ggplot(data = all_enrich_dat, aes(y = Zscore, x = Element, fill=DMR)) +
geom_col(position = 'dodge') +
#facet_wrap(DMR~.) +
scale_x_discrete(limits=element_order, drop=TRUE) +
scale_y_continuous(limits = c(-25, 55), breaks = c(-25, 0, 25, 50)) +
#coord_flip() +
scale_fill_manual(values = rev(bar_cols)) +
sams_pub_theme(x.text.angle = 45, legend_pos = "right")
gg_enrich_sub
pdf("wgbs/plots/dmr_enrichment_zscore_sub_barplots.pdf", width = 2.5, height = 2)
gg_enrich_sub
dev.off()
# gg_olaps <- ggplot(data = all_enrich_dat, aes(y = pc, x = Element)) +
# geom_col() +
# facet_wrap(DMR~.) +
# #geom_hline(yintercept = 959) +
# coord_flip()
# gg_olaps
pdf("wgbs/plots/dmr_enrichment_zscore_barplots.pdf", width = 4, height = 1.75)
gg_enrich
gg_enrich_sub
dev.off()
gg_enrich_sub
```
```{r, cache=TRUE}
openxlsx::addWorksheet(wb_fig3, sheetName = "Fig_3e")
openxlsx::writeData(wb = wb_fig3, sheet = "Fig_3e",
x = gg_enrich_sub$data)
```
Plot the CG-DMRs in CH-DMRs
```{r, cache=TRUE}
real_ch <- read_excel(path = "resources/nature13551-s3.xlsx")
mCH_gr <- GRanges(seqnames = real_ch$chr,
ranges = IRanges(start = as.numeric(real_ch$start),
end = as.numeric(real_ch$end)))
### PLot CH dmrs overlapping CG dmrs
all_dmrs <- readRDS("wgbs/processed_data/all_dmrs_annotated_granges.Rds")
all_dmrs$CH_olap <- overlapsAny(all_dmrs, mCH_gr)
ch_olap_dat <- mcols(all_dmrs)[ ,colnames(mcols(all_dmrs)) %in% c("esc_ips_delta",
"esc_n2p_d13_delta",
"esc_n2p_p10_delta",
"CH_olap",
"class",
"loci")] %>% data.frame()
colnames(ch_olap_dat)[2:4] <- c("TNT-hiPSC", "NtP-hiPSC", "Primed-hiPSC")
ch_olap_dat <- reshape2::melt(ch_olap_dat)
ch_olap_dat$CH_olap <- ifelse(test = ch_olap_dat$CH_olap, yes = "CH-DMR_overlap", no = "No overlap")
ch_olap_dat$CH_olap <- factor(ch_olap_dat$CH_olap, levels = c("FALSE", "TRUE"))
ch_olap_dat$variable <- factor(ch_olap_dat$variable,
levels = c("Primed-hiPSC", "TNT-hiPSC", "NtP-hiPSC"))
gg_ch_olap_violin <- ggplot(ch_olap_dat, aes(x = variable, y = abs(value), fill=CH_olap)) +
#geom_point(alpha=0.05, position = 'jitter') +
geom_violin(scale = 'width', draw_quantiles = 0.5, alpha=0.7,
trim = TRUE, size=0.2) +
scale_fill_pander()+ ylab("mCG delta in CG-DMRs") +
#geom_boxplot(color='black', outlier.alpha=0.05, outlier.size = 0.5) +
scale_y_continuous(breaks = seq(from=0, to=1, by=0.2)) +
#facet_grid(.~CH_olap) +
sams_pub_theme(legend_pos = "right")
pdf(file = "wgbs/plots/cg_dmr_delta_ch_olaps_violin.pdf", height = 2, width = 2.5)
gg_ch_olap_violin
dev.off()
gg_ch_olap_violin
```
```{r, cache=TRUE}
openxlsx::addWorksheet(wb_ed_fig4, sheetName = "Fig_4c")
openxlsx::writeData(wb = wb_ed_fig4, sheet = "Fig_4c",
x = gg_ch_olap_violin$data)
```
CG DMR in CH DMR sum
```{r, cache=TRUE}
## CG DMR in CH DMR sum
cg_in_ch <- sum(all_dmrs[all_dmrs$CH_olap == TRUE]$any_corrected) /
length(all_dmrs[all_dmrs$CH_olap == TRUE]$any_corrected)
cg_no_ch <- sum(all_dmrs[all_dmrs$CH_olap == FALSE]$any_corrected) /
length(all_dmrs[all_dmrs$CH_olap == FALSE]$any_corrected)
ch_cor_df <- data.frame(group=c("CG-DMR overlap CH-DMR", "No CH-DMR overlap"),
pc=c(cg_in_ch, cg_no_ch))
ch_dmr_olap_count <- ggplot(ch_cor_df, aes(x = group, y = pc*100)) +
geom_col() + scale_y_continuous(limits = c(0,100)) +
ylab("Proportion of CG-DMRs corrected (%)") +
geom_text(aes(label = sprintf("%.1f", pc*100), y= pc*100), vjust = 3) +
sams_pub_theme()
pdf("wgbs/plots/cg_dmr_ch_olaps_barplot.pdf", width = 1, height = 2.5)
ch_dmr_olap_count
dev.off()
ch_dmr_olap_count
```
```{r, cache=TRUE}
openxlsx::addWorksheet(wb_ed_fig4, sheetName = "Fig_4b")
openxlsx::writeData(wb = wb_ed_fig4, sheet = "Fig_4b",
x = ch_dmr_olap_count$data)
```
Plot H3K9me3 signal in corrected CG DMRs (first code block run on remote server)
```{r, eval=FALSE}
##### Get H3K9me3 in CG DMRs
# And fold-enrichment in CG-DMRs
##! Run this section on server
all_dmrs <- readRDS("wgbs/processed_data/all_dmrs_annotated_granges.Rds")
fibroblast <- c("ENCFF735TXC.bigWig")
ESC <- c("ENCFF108MOZ.bigWig")
Primed <- c("Primed_32F_H3K9me3_FE_sorted.bw", "Primed_38F_H3K9me3_FE_sorted.bw")
Naive <- c("RL2011_2020_02_06_P33_32F_Naive_SR_clone1_H3K9me3_ChIP_S7_merged_lanes_dedup_FE.bigwig")
TNT <- c("TNT_32F_H3K9me3_r1_FE_sorted.bw", "TNT_32F_H3K9me3_r2_FE_sorted.bw", "TNT_38F_H3K9me3_FE_sorted.bw")
N2P <- c("N2P_38F_H3K9me3_r1_FE_sorted.bw", "N2P_38F_H3K9me3_r2_FE_sorted.bw")
bw_files <- c(fibroblast, ESC, Primed, Naive, TNT, N2P)
bw_files <- str_c("/d/home/hg19ips/hg19ips_samb/mcc_hg19ips/sam/hs-reprogram/ChIPseq/bigwigs/", bw_files)
file.exists(bw_files)
bw_path <- bw_files[1]
gr <- all_dmrs
get_dmr_means <- function(bw_path, gr){
id <- basename(bw_path)
bw_dat <- import.bw(con = bw_path, which = gr)
hits <- findOverlaps(gr, bw_dat, ignore.strand = TRUE)
ans <- gr
mcols(ans) <- aggregate(bw_dat, hits, score.mean = mean(score),
drop = FALSE)
dat <- matrix(data = mcols(ans)$score.mean, nrow = length(gr),
byrow = TRUE) %>% data.frame()
dat$class <- gr$class
dat$loci <- gr_to_loci(gr)
dat$id <- id
colnames(dat)[1] <- "value"
return(dat)
}
all_cg_window_dat <- mclapply(X = bw_files, FUN = get_dmr_means, mc.cores = 10,
gr = all_dmrs)
all_cg_window_dat <- do.call(rbind, all_cg_window_dat)
write.table(all_cg_window_dat, "ChIPseq/processed_data/all_CG_DMR_h3k9me3_FE_means.txt",
quote = FALSE, sep = "\t", row.names = TRUE, col.names = TRUE)
```
```{r, cache=TRUE}
fibroblast <- c("ENCFF735TXC.bigWig")
ESC <- c("ENCFF108MOZ.bigWig")
Primed <- c("Primed_32F_H3K9me3_FE_sorted.bw", "Primed_38F_H3K9me3_FE_sorted.bw")
Naive <- c("RL2011_2020_02_06_P33_32F_Naive_SR_clone1_H3K9me3_ChIP_S7_merged_lanes_dedup_FE.bigwig")
TNT <- c("TNT_32F_H3K9me3_r1_FE_sorted.bw", "TNT_32F_H3K9me3_r2_FE_sorted.bw", "TNT_38F_H3K9me3_FE_sorted.bw")
N2P <- c("N2P_38F_H3K9me3_r1_FE_sorted.bw", "N2P_38F_H3K9me3_r2_FE_sorted.bw")
all_cg_window_dat <- read.table("ChIPseq/processed_data/all_CG_DMR_h3k9me3_FE_means.txt")
h3k9_dmr_mat <- reshape2::dcast(data = all_cg_window_dat[ ,c(1,3,4)], formula = loci~id)
rownames(h3k9_dmr_mat) <- h3k9_dmr_mat$loci
h3k9_dmr_mat$loci <- NULL
colnames(h3k9_dmr_mat) <- str_remove(string = colnames(h3k9_dmr_mat), pattern = "merged_lanes_dedup_FE.bigwig")
TNT_corrected <- all_dmrs$loci[all_dmrs$tnt_corrected == TRUE]
N2P_corrected <- all_dmrs$loci[all_dmrs$ntp_corrected == TRUE]
all_cg_window_dat$TNT_corrected <- all_cg_window_dat$loci %in% TNT_corrected
all_cg_window_dat$N2P_corrected <- all_cg_window_dat$loci %in% N2P_corrected
all_cg_window_dat$id %>% unique()
chip_keep <- list(Fibroblast=fibroblast, ESC=ESC, Primed=Primed, Naive=Naive, TNT=TNT, N2P=N2P) %>%
do.call(rbind, .) %>% reshape2::melt() %>% .[ ,c(1,3)] %>% unique()
all_cg_window_dat <- all_cg_window_dat[all_cg_window_dat$id %in% chip_keep$value, ]
chip_keep$group <- NA
chip_keep$group[1] <- "Fibroblast"
chip_keep$group[2] <- "ESC (H9)"
chip_keep$group[3:nrow(chip_keep)] <- grepl(pattern = "32F",
x = chip_keep$value[3:nrow(chip_keep)]) %>%
ifelse(yes = "32F", no = "38F")
ind <- match(all_cg_window_dat$id, chip_keep$value)
all_cg_window_dat$group <- chip_keep$Var1[ind]
all_cg_window_dat$donor <- chip_keep$group[ind]
all_cg_window_dat$both_corrected <- all_cg_window_dat$TNT_corrected == TRUE |
all_cg_window_dat$N2P_corrected == TRUE
# Get the mean for each group where there are replicates
replicate_mean_dat <- all_cg_window_dat[ ,c(1,3,4,7:9)] %>%
dplyr::group_by(loci, group, donor, both_corrected) %>%
dplyr::summarize(Mean = mean(value, na.rm=TRUE)) %>% data.frame()
replicate_mean_dat$group <- factor(replicate_mean_dat$group,
levels = c("Fibroblast", "ESC", "Primed",
"N2P", "TNT", "Naive"))
replicate_mean_dat$donor <- factor(replicate_mean_dat$donor,
levels = c("Fibroblast", "ESC (H9)",
"32F", "38F"))
replicate_mean_dat <- replicate_mean_dat[is.finite(replicate_mean_dat$Mean), ]
replicate_mean_dat <- replicate_mean_dat[replicate_mean_dat$group != "Naive", ]
replicate_mean_dat$group <- factor(replicate_mean_dat$group,
levels = c("Fibroblast", "Primed", "N2P",
"TNT", "ESC"))
h3k9me3_boxplots <- ggplot(replicate_mean_dat, aes(x = group, y=Mean, fill=group)) +
geom_boxplot(color='black', outlier.alpha=0.2, lwd = line_mm,
outlier.shape = NA, notch = TRUE) +
#geom_boxplot(color='black', outlier.alpha=0.2, lwd = line_mm,notch = TRUE) +
ylab("H3K9me3 ChIP-seq fold enrichment in CG-DMRs") +
#facet_grid(.~donor, scales = "free_x", space = "free", drop = TRUE) +
sams_pub_theme()
pdf(file = "ChIPseq/plots/h3k9me3_in_CG_DMRs_boxplot.pdf",
width = 1.5, height = 2.5)
h3k9me3_boxplots
dev.off()
## Stats test of groups
replicate_spread <- reshape2::recast(replicate_mean_dat[ ,c(1,2,5)], formula = loci~group, fun.aggregate = mean)
replicate_spread <- round(replicate_spread[ ,-1], digits = 3)
run_wilcox <- function(x,y){
wt <- wilcox.test(x = log2(replicate_spread[ ,x]), conf.int = TRUE,
y = log2(replicate_spread[ ,y]), alternative = 'greater',
paired = TRUE)
#return(wt)
return(wt$p.value)
}
wilcox_df <- data.frame(Primed_v_NtP = run_wilcox(x = "Primed", y = "N2P"),
Primed_v_TNT = run_wilcox(x = "Primed", y = "TNT"),
TNT_v_ESC = run_wilcox(x = "TNT", y = "ESC"),
NtP_v_ESC = run_wilcox(x = "N2P", y = "ESC"),
Primed_v_ESC = run_wilcox(x = "Primed", y = "ESC"))
h3k9me3_boxplots
```
```{r, cache=TRUE}
openxlsx::addWorksheet(wb_fig3, sheetName = "Fig_3f")
openxlsx::writeData(wb = wb_fig3, sheet = "Fig_3f",
x = h3k9me3_boxplots$data)
```
Calculate mCA methylayion in CH-DMRs.
This code was not run locally and needs a high memory machine.
```{r, eval=FALSE}
CH_dmr <- bed_to_gr("resources/nature13551-s3.bed")
make_window_matrix_nc_corrected <- function(x, gr, bins=30, nc_correct=TRUE,
nc_contig="chrL"){
rds_path <- mCA_files[x]
message(basename(rds_path))
#id <- mdat_wgbs$Library_id[x]
gr_expand <- gr + width(gr)
gr_expand$loci <- gr_to_loci(gr_expand)
gr_tiles <- tile(gr_expand, n = bins)
add_loci_to_grl <- function(x){
grt <- gr_tiles[[x]]
grt$loci <- gr_to_loci(gr_expand[x])
return(grt)
}
read_bs_obj <- function(rds_path){
stopifnot(file.exists(rds_path))
message(str_c("Reading ", rds_path))
message(Sys.time())
bs_obj <- readRDS(file = rds_path)
return(bs_obj)
}
# Calculate C coverage and methylation for ranges. Returns GRanges object
calc_mC_window <- function(bs_obj, gr){
message("Calculating coverage...")
gr$Cov <- getCoverage(BSseq = bs_obj, regions = gr, type = "Cov",
what = "perRegionTotal")
message("Calculating M...")
gr$M <- getCoverage(BSseq = bs_obj, regions = gr, type = "M",
what = "perRegionTotal")
message("Calculating methylation percentage...")
gr$pc <- gr$M / gr$Cov
return(gr)
}
gr_tiles <- lapply(1:length(gr_tiles), add_loci_to_grl) %>%
GRangesList() %>% unlist()
bs_obj <- read_bs_obj(rds_path)
mC_dat <- calc_mC_window(bs_obj = bs_obj, gr = gr_tiles)
dat <- matrix(data = mC_dat$pc, nrow = length(gr), byrow = TRUE)
# Correct for non-conversion
correct_nc <- function(bs_obj, nc_contig = "chrL"){
nc_obj <- chrSelectBSseq(BSseq = bs_obj, seqnames = nc_contig)
nc_cov <- getCoverage(BSseq = nc_obj, type = "Cov",
what = "perBase")
nc_m <- getCoverage(BSseq = nc_obj, type = "M",
what = "perBase")
nc_rate <- sum(nc_m) / sum(nc_cov)
return(nc_rate)
}
if (nc_correct == TRUE){
message("Correcting for non-conversion...")
nc_rate <- correct_nc(bs_obj = bs_obj, nc_contig = nc_contig)
dat <- dat - nc_rate
dat[dat < 0] <- 0
}
dat <- data.frame(dat)
rownames(dat) <- gr_to_loci(gr)
colnames(dat) <- 1:ncol(dat)
dat$id <- basename(rds_path)
return(dat)
}
mCA_files <- mdat_sub$BSseq_CA
mdat_sub$Library_id[!file.exists(mCA_files)]
mCA_files <- mCA_files[file.exists(mCA_files)]
all(file.exists(mCA_files))
dfl <- lapply(1:length(mCA_files), make_window_matrix_nc_corrected,
gr=CH_dmr)
dfl <- do.call(rbind, dfl)
saveRDS(dfl, "wgbs/processed_data/mCH_DMR_window_dat_all.Rds")
```
CH-DMR plots
```{r, cache=TRUE}
dfl <- readRDS("wgbs/processed_data/mCH_DMR_window_dat_all.Rds")
id_ind <- match(dfl$id, basename(mdat$BSseq_CA))