H3K9me3 summit processing
Renee Matthews
2026-01-08
Last updated: 2026-01-08
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Knit directory: DXR_continue/
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library(tidyverse)
library(GenomicRanges)
library(plyranges)
library(genomation)
library(readr)
library(rtracklayer)
library(stringr)
library(BiocParallel)
library(parallel)
library(future.apply)sampleinfo <- read_delim("data/sample_info.tsv", delim = "\t")
##Path to histone summit files
H3K9me3_dir <- "C:/Users/renee/Other_projects_data/DXR_data/final_data/summit_files/H3K9me3"
##pull all histone files together
H3K9me3_summit_files <- list.files(
path = H3K9me3_dir,
pattern = "\\.bed$",
recursive = TRUE,
full.names = TRUE
)
length(H3K9me3_summit_files)[1] 29# head(H3K9me3_summit_files)peakAnnoList_H3K9me3 <- readRDS("data/motif_lists/H3K9me3_annotated_peaks.RDS")
H3K9me3_sets_gr <- lapply(peakAnnoList_H3K9me3, function(df) {
as_granges(df)
})
read_summit <- function(file){
peaks <- read.table(file,header = FALSE)
colnames(peaks) <- c("chr","start","end","name","score")
GRanges(
seqnames = peaks$chr,
ranges = IRanges(start=peaks$start, end = peaks$start),
score=peaks$score,
file=basename(file),
Library_ID = stringr::str_remove(basename(file), "_FINAL_summits\\.bed$")
)
}
all_H3K9me3_summits_list<- lapply(H3K9me3_summit_files, read_summit)
all_H3K9me3_summits_gr <- do.call(c, all_H3K9me3_summits_list) # combine into one GRanges object
H3K9me3_lookup <- imap_dfr(peakAnnoList_H3K9me3[1:3], ~
tibble(Peakid = .x@anno$Peakid, cluster = .y)
)
###Adding in sampleinfo dataframe
meta <- as.data.frame(mcols(all_H3K9me3_summits_gr))
meta2 <- meta %>%
left_join(., sampleinfo, by=c("Library_ID"="Library ID"))
mcols(all_H3K9me3_summits_gr) <- meta2
mcols(all_H3K9me3_summits_gr)$group <-
paste(all_H3K9me3_summits_gr$Treatment,
all_H3K9me3_summits_gr$Timepoint,
sep = "_")
ROIs <- H3K9me3_sets_gr$all_H3K9me3# -------------------------
# Step 1: Reduce within groups (parallel, Windows-safe)
# -------------------------
get_highest_per_group_parallel <- function(summits_gr, group_col = "group",
score_col = "score", min_gap_within = 100,
workers = 2) {
# Split GRanges by group to reduce memory per worker
group_list <- split(summits_gr, mcols(summits_gr)[[group_col]])
plan(multisession, workers = workers) # Windows-compatible
results <- future_lapply(group_list, function(gr_sub) {
if (length(gr_sub) == 0) return(GRanges())
red <- GenomicRanges::reduce(gr_sub, min.gapwidth = min_gap_within, ignore.strand = TRUE, with.revmap = TRUE)
revmap <- mcols(red)$revmap
idx <- unlist(lapply(revmap, function(x) {
scores <- mcols(gr_sub)[[score_col]][x]
x[which.max(scores)]
}))
gr_sub[idx]
}, future.seed = TRUE)
do.call(c, results)
}
# -------------------------
# Step 2: Reduce across groups (parallel, Windows-safe)
# -------------------------
get_consensus_summits_parallel <- function(highest_per_group_gr, score_col = "score",
min_gap_across = 400, workers = 2) {
if (length(highest_per_group_gr) == 0) return(GRanges())
# Split by chromosome to reduce memory per worker
chr_list <- split(highest_per_group_gr, seqnames(highest_per_group_gr))
plan(multisession, workers = workers)
results <- future_lapply(chr_list, function(gr_sub) {
red <- GenomicRanges::reduce(gr_sub, min.gapwidth = min_gap_across, ignore.strand = TRUE, with.revmap = TRUE)
revmap <- mcols(red)$revmap
idx <- unlist(lapply(revmap, function(x) {
scores <- mcols(gr_sub)[[score_col]][x]
x[which.max(scores)]
}))
gr_sub[idx]
}, future.seed = TRUE)
do.call(c, results)
}
####### step 3 #####################3
assign_best_summit_to_ROI_parallel <- function(consensus_gr,
ROIs_gr,
max_dist = 500,
workers = 2) {
# Split by chromosome
roi_list <- split(ROIs_gr, seqnames(ROIs_gr))
cons_list <- split(consensus_gr, seqnames(consensus_gr))
plan(multisession, workers = workers)
results <- future_lapply(intersect(names(roi_list), names(cons_list)),
function(chr) {
rois_chr <- roi_list[[chr]]
cons_chr <- cons_list[[chr]]
if (length(rois_chr) == 0) return(tibble())
# -----------------------------
# ROI metadata (ground truth)
# -----------------------------
roi_meta <- tibble(
Peakid = rois_chr$Peakid,
roi_seqname = as.character(seqnames(rois_chr)),
roi_start = start(rois_chr),
roi_end = end(rois_chr)
)
# -----------------------------
# 1) Exact overlaps
# -----------------------------
ov <- findOverlaps(rois_chr, cons_chr)
assigned_df <- if (length(ov) > 0) {
tibble(
Peakid = rois_chr$Peakid[queryHits(ov)],
summit_pos = start(cons_chr)[subjectHits(ov)],
summit_score = mcols(cons_chr)$score[subjectHits(ov)]
) %>%
group_by(Peakid) %>%
slice_max(summit_score, with_ties = FALSE) %>%
ungroup()
} else {
tibble()
}
# -----------------------------
# 2) Nearest fallback
# -----------------------------
unassigned_peaks <- setdiff(roi_meta$Peakid, assigned_df$Peakid)
if (length(unassigned_peaks) > 0 && length(cons_chr) > 0) {
roi_unassigned <- rois_chr[rois_chr$Peakid %in% unassigned_peaks]
dn <- distanceToNearest(roi_unassigned, cons_chr)
dn_df <- tibble(
Peakid = roi_unassigned$Peakid[queryHits(dn)],
summit_pos = start(cons_chr)[subjectHits(dn)],
summit_score = mcols(cons_chr)$score[subjectHits(dn)],
distance = mcols(dn)$distance
) %>%
filter(distance <= max_dist) %>%
group_by(Peakid) %>%
slice_max(summit_score, with_ties = FALSE) %>%
ungroup()
assigned_df <- bind_rows(assigned_df, dn_df)
}
# -----------------------------
# 3) Merge + derived metrics
# -----------------------------
out_df <- left_join(roi_meta, assigned_df, by = "Peakid") %>%
mutate(
roi_center = roi_start + (roi_end - roi_start) / 2,
dist_center = ifelse(is.na(summit_pos),
NA_real_,
summit_pos - roi_center),
rel_pos = ifelse(is.na(summit_pos),
NA_real_,
(summit_pos - roi_start) / (roi_end - roi_start))
)
out_df
}, future.seed = TRUE)
final_df <- bind_rows(results)
# -----------------------------
# 4) GRanges of assigned summits
# -----------------------------
assigned_rows <- final_df %>% filter(!is.na(summit_pos))
assigned_gr <- if (nrow(assigned_rows) > 0) {
GRanges(
seqnames = assigned_rows$roi_seqname,
ranges = IRanges(
start = assigned_rows$summit_pos,
end = assigned_rows$summit_pos
),
Peakid = assigned_rows$Peakid,
score = assigned_rows$summit_score,
dist_center = assigned_rows$dist_center,
rel_pos = assigned_rows$rel_pos
)
} else {
GRanges()
}
list(df = final_df, gr = assigned_gr)
}Evaluation of gap width in summits
Reduction plots
Plotting effect of reduction bp number on total number of clusters
gap_sizes <- seq(0, 500, by = 10)
# Function to apply reduce for each gap and return counts
results <- lapply(gap_sizes, function(g) {
reduced <- GenomicRanges::reduce(all_H3K9me3_summits_gr, min.gapwidth = g)
data.frame(gap = g, n_regions = length(reduced))
})
# Combine results
min_gap_summary <- bind_rows(results)
ggplot(min_gap_summary, aes(x = gap, y= n_regions))+
geom_line(linewidth=2)+
geom_point()+
theme_bw(base_size=14)+
labs(
title = "Effect of Gap Size on Reduced Summits",
x = "Gap size (bp)",
y = "Number of merged regions"
)
| Version | Author | Date |
|---|---|---|
| 03be238 | reneeisnowhere | 2026-01-05 |
# Function to pick highest summit per cluster from a reduced GRanges list
get_highest_per_group <- function(reduced_groups, orig_summits_gr, group_col = "group") {
groups <- names(reduced_groups)
highest_per_group <- vector("list", length(reduced_groups))
names(highest_per_group) <- groups
for(i in seq_along(reduced_groups)) {
gr <- reduced_groups[[i]]
# original summits for this group
orig <- orig_summits_gr[mcols(orig_summits_gr)[[group_col]] == groups[i]]
scores <- orig$score
# revmap is a CompressedIntegerList
revmap <- mcols(gr)$revmap
# skip if revmap is NULL
if(is.null(revmap)) next
# unlist all indices once
all_idx <- unlist(revmap, use.names = FALSE)
# repeat cluster index for each element in revmap
cluster_idx <- rep(seq_along(revmap), times = elementNROWS(revmap))
# scores for all indices
all_scores <- scores[all_idx]
# For each cluster, pick the index of the max score
max_idx_per_cluster <- tapply(seq_along(all_scores), cluster_idx, function(ii) {
ii[which.max(all_scores[ii])]
})
# Convert back to original indices
orig_idx <- all_idx[unlist(max_idx_per_cluster)]
# subset original GRanges
highest_per_group[[i]] <- orig[orig_idx]
}
# Flatten any nested GRangesList
flatten_gr <- function(x) {
if (inherits(x, "GRanges")) return(x)
if (inherits(x, "GRangesList")) return(unlist(x, use.names = FALSE))
if (is.list(x)) return(do.call(c, lapply(x, flatten_gr)))
stop("Unexpected object type")
}
highest_summits_gr <- flatten_gr(highest_per_group)
# Return as long GRanges with group column
highest_summits_df <- bind_rows(
lapply(names(highest_per_group), function(gr_name) {
as.data.frame(highest_per_group[[gr_name]]) %>%
mutate(group = gr_name)
})
)
highest_summits_long_gr <- highest_summits_df %>% GRanges()
return(highest_summits_long_gr)
}###Adding in sampleinfo dataframe
# meta <- as.data.frame(mcols(all_H3K9me3_summits_gr))
# meta2 <- meta %>%
# left_join(., sampleinfo)
#
# mcols(all_H3K9me3_summits_gr) <- meta2
#
# mcols(all_H3K9me3_summits_gr)$group <-
# paste(all_H3K9me3_summits_gr$Treatment,
# all_H3K9me3_summits_gr$Timepoint,
# sep = "_")
###now splitting into grouped granges
gr_by_group <- split(all_H3K9me3_summits_gr,
all_H3K9me3_summits_gr$group)
# gr_by_group <- as(gr_by_group, "CompressedGRangesList")
### now reducing within some width by 100 bp with revmap
groups <- unique(all_H3K9me3_summits_gr$group)
reduced_groups <- lapply(groups, function(g) {
gr_sub <- all_H3K9me3_summits_gr[all_H3K9me3_summits_gr$group == g]
GenomicRanges::reduce(gr_sub, min.gapwidth = 100, ignore.strand = TRUE, with.revmap = TRUE)
})
reduced_groups_200 <- lapply(groups, function(g) {
gr_sub <- all_H3K9me3_summits_gr[all_H3K9me3_summits_gr$group == g]
GenomicRanges::reduce(gr_sub, min.gapwidth = 200, ignore.strand = TRUE, with.revmap = TRUE)
})
reduced_groups_300 <- lapply(groups, function(g) {
gr_sub <- all_H3K9me3_summits_gr[all_H3K9me3_summits_gr$group == g]
GenomicRanges::reduce(gr_sub, min.gapwidth = 300, ignore.strand = TRUE, with.revmap = TRUE)
})
reduced_groups_400 <- lapply(groups, function(g) {
gr_sub <- all_H3K9me3_summits_gr[all_H3K9me3_summits_gr$group == g]
GenomicRanges::reduce(gr_sub, min.gapwidth = 400, ignore.strand = TRUE, with.revmap = TRUE)
})
names(reduced_groups) <- groups
names(reduced_groups_200) <- groups
names(reduced_groups_300) <- groups
names(reduced_groups_400) <- groups
# redux_main_100 <- sum(sapply(reduced_groups, length))
# redux_main_200 <- sum(sapply(reduced_groups_200, length))
# redux_main_300 <- sum(sapply(reduced_groups_300, length))
# redux_main_400 <- sum(sapply(reduced_groups_400, length))reduced_sets <- list(
"100bp" = reduced_groups,
"200bp" = reduced_groups_200,
"300bp" = reduced_groups_300,
"400bp" = reduced_groups_400
)
highest_summits_all <- lapply(reduced_sets, get_highest_per_group, orig_summits_gr = all_H3K9me3_summits_gr)Looking at number of summits across groups as a function of min.gap number
##Compute counts per group for each reduced set
summit_counts_group <- lapply(names(highest_summits_all), function(gap_name) {
gr <- highest_summits_all[[gap_name]]
# make sure group column exists
if(!"group" %in% colnames(mcols(gr))) stop("GRanges must have 'group' column")
df <- as.data.frame(gr) %>%
count(group, name = "n_summits") %>%
mutate(gap = gap_name)
return(df)
}) %>% bind_rows()
ggplot(summit_counts_group, aes(x = gap, y = n_summits, group = group, color = group)) +
geom_line(size = 1.2) +
geom_point(size = 3) +
theme_classic(base_size = 14) +
labs(
title = "Effect of min-gap width on number of highest summits per group",
x = "Min-gap width (bp)",
y = "Number of highest summits",
color = "Group"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
| Version | Author | Date |
|---|---|---|
| 03be238 | reneeisnowhere | 2026-01-05 |
Summit average per ROI
What is the average number of summits per ROI as a function of min.gapwidth
double_reduce_summits <- function(summits_gr, ROIs, groups,
min_gap_within_seq = c(100,200,300),
min_gap_across_seq = c(100,200,300),
BPPARAM = MulticoreParam(4)) {
# Pre-split summits by group to avoid repeated subsetting
summits_by_group <- split(summits_gr, summits_gr$group)
# Create all gap combinations
gap_combos <- expand.grid(min_gap_within = min_gap_within_seq,
min_gap_across = min_gap_across_seq,
stringsAsFactors = FALSE)
# Apply in parallel for each combination
results <- bplapply(seq_len(nrow(gap_combos)), function(i) {
g1 <- gap_combos$min_gap_within[i]
g2 <- gap_combos$min_gap_across[i]
# -----------------
# Step 1: Reduce within group
# -----------------
highest_per_group <- lapply(groups, function(grp) {
gr_sub <- summits_by_group[[grp]]
if(length(gr_sub) == 0) return(NULL)
# reduce within group with revmap
red <- GenomicRanges::reduce(gr_sub, min.gapwidth = g1, ignore.strand = TRUE, with.revmap = TRUE)
revmap <- mcols(red)$revmap
if(length(revmap) == 0) return(NULL)
# pick highest score per cluster
cluster_idx <- rep(seq_along(revmap), times = elementNROWS(revmap))
all_idx <- unlist(revmap, use.names = FALSE)
all_scores <- gr_sub$score[all_idx]
max_idx_per_cluster <- tapply(seq_along(all_scores), cluster_idx, function(ii) {
ii[which.max(all_scores[ii])]
})
gr_sub[all_idx[unlist(max_idx_per_cluster)]]
})
highest_per_group <- highest_per_group[!sapply(highest_per_group, is.null)]
# -----------------
# Step 2: Merge across groups
# -----------------
if(length(highest_per_group) == 0) return(NULL)
all_highest <- do.call(c, highest_per_group)
consensus <- GenomicRanges::reduce(all_highest, min.gapwidth = g2, ignore.strand = TRUE)
# -----------------
# Step 3: Count summits per ROI (vectorized)
# -----------------
hits <- findOverlaps(ROIs, consensus)
counts <- as.data.frame(table(queryHits(hits)))
colnames(counts) <- c("ROI_idx", "n_summits")
counts$ROI_idx <- as.integer(as.character(counts$ROI_idx))
counts$min_gap_within <- g1
counts$min_gap_across <- g2
counts
}, BPPARAM = BPPARAM)
# Combine results
results_df <- bind_rows(results)
return(results_df)
}BPPARAM <- SnowParam(workers = 4, type = "SOCK")
register(BPPARAM)
heatmap_data <- double_reduce_summits(
summits_gr = all_H3K9me3_summits_gr,
ROIs = H3K9me3_sets_gr$all_H3K9me3,
groups = unique(all_H3K9me3_summits_gr$group),
min_gap_within_seq = c(100,200,300,400),
min_gap_across_seq = c(100,200,300,400),
BPPARAM = BPPARAM
)
heatmap_avg <- heatmap_data %>%
group_by(min_gap_within, min_gap_across) %>%
summarise(mean_summits = mean(n_summits, na.rm = TRUE), .groups = "drop")Exploring differences in min gaps in step 1 and step 2 reductions
ggplot(heatmap_avg, aes(x = factor(min_gap_within),
y = factor(min_gap_across),
fill = mean_summits)) +
geom_tile(color = "white") +
geom_text(aes(label = round(mean_summits, 1)), color = "black", size = 4) +
scale_fill_viridis_c(option = "plasma") +
labs(
x = "Within-group min-gap (bp)",
y = "Across-group min-gap (bp)",
fill = "Mean # summits per ROI",
title = "Effect of min-gap width on summits per ROI"
) +
theme_minimal(base_size = 14) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
| Version | Author | Date |
|---|---|---|
| 03be238 | reneeisnowhere | 2026-01-05 |
options(future.globals.maxSize = 10 * 1024^3)
workers <- parallel::detectCores() - 1
### Step 1
temp_highest_per_group <- get_highest_per_group_parallel(all_H3K9me3_summits_gr,
group_col = "group",
score_col = "score",
min_gap_within = 100,
workers = workers)
# Concatenate into one GRanges (Step 1.2)
flat_highest_gr <- (c(temp_highest_per_group$DOX_144R, temp_highest_per_group$DOX_24R,temp_highest_per_group$DOX_24T,temp_highest_per_group$VEH_144R,temp_highest_per_group$VEH_24R,temp_highest_per_group$VEH_24T))
### STep 2
consensus_summits <- get_consensus_summits_parallel(
flat_highest_gr,
score_col = "score",
min_gap_across = 400,
workers = workers
)
####Concatenate into one GRanges
consensus_summits_gr <- (c(consensus_summits$chr1,consensus_summits$chr2,
consensus_summits$chr3,consensus_summits$chr4,
consensus_summits$chr5,consensus_summits$chr6,
consensus_summits$chr7,consensus_summits$chr8,
consensus_summits$chr9,consensus_summits$chr10,
consensus_summits$chr11,consensus_summits$chr12,
consensus_summits$chr13,consensus_summits$chr14,
consensus_summits$chr15,consensus_summits$chr16,
consensus_summits$chr17,consensus_summits$chr18,
consensus_summits$chr19,consensus_summits$chr20,
consensus_summits$chr21,consensus_summits$chr22))
final_data <- assign_best_summit_to_ROI_parallel(consensus_summits_gr, ROIs,max_dist = 500,workers = workers)
# data.frame with one row per ROI (assigned or NA)
final_df <- final_data$df
# GRanges of ROIs that received an assigned summit (with metadata)
assigned_summits_gr <- final_data$gr
# Quick checks
n_missing <- sum(is.na(final_df$summit_pos))
cat("ROIs lacking any summit within max_dist:", n_missing, "\n")ROIs lacking any summit within max_dist: 10190 na_rois <- final_df %>% filter(is.na(summit_pos))
na_rois_gr <- GRanges(
seqnames = na_rois$roi_seqname,
ranges = IRanges(start = na_rois$roi_start, end = na_rois$roi_end),
Peakid = na_rois$Peakid
)
ov <- findOverlaps(na_rois_gr, flat_highest_gr)
overlap_df <- tibble(
roi_idx = queryHits(ov),
summit_idx = subjectHits(ov),
summit_pos = start(all_H3K9me3_summits_gr)[subjectHits(ov)],
summit_score = mcols(all_H3K9me3_summits_gr)$score[subjectHits(ov)]
)
best_summits <- overlap_df %>%
group_by(roi_idx) %>%
slice_max(summit_score, with_ties = FALSE) %>%
ungroup()
assigned_df <- tibble(
roi_idx = seq_along(na_rois_gr),
Peakid = na_rois_gr$Peakid,
roi_seqname = as.character(seqnames(na_rois_gr)),
roi_start = start(na_rois_gr),
roi_end = end(na_rois_gr)
) %>%
left_join(best_summits, by = "roi_idx")
assigned_df_calc <- assigned_df %>% mutate(
dist_center = summit_pos - (roi_start + (roi_end - roi_start)/2),
rel_pos = (summit_pos - roi_start)/(roi_end - roi_start)
)
complete_summit_df <- final_df %>%
dplyr::select(!distance) %>%
dplyr::filter(!is.na(summit_pos)) %>%
bind_rows(.,assigned_df_calc) %>%
left_join(., H3K9me3_lookup, by = "Peakid") %>%
dplyr::select(!summit_idx) %>%
# dplyr::select(!cons_idx) %>%
group_by(Peakid) %>%
slice_min(order_by = abs(dist_center), n = 1) %>%
distinct
only_complete <- complete_summit_df %>% filter(!is.na(summit_pos))
complete_summit_gr <- GRanges(
seqnames=only_complete$roi_seqname,
ranges=IRanges(start= only_complete$summit_pos,
end= only_complete$summit_pos))
# Columns to exclude from metadata (used to define GRanges)
exclude_cols <- c("roi_seqname", "summit_pos")
# Only keep columns that exist in df
meta_cols <- intersect(setdiff(colnames(only_complete), exclude_cols), colnames(only_complete))
# Assign metadata
mcols(complete_summit_gr) <- only_complete[, meta_cols, drop = FALSE]### adding in cluster membership for export
library(BSgenome.Hsapiens.UCSC.hg38)
genome <- BSgenome.Hsapiens.UCSC.hg38
seqlengths(complete_summit_gr) <- seqlengths(genome)[names(seqlengths(complete_summit_gr))]
SET_1_gr <- complete_summit_gr[
!is.na(mcols(complete_summit_gr)$cluster) &
mcols(complete_summit_gr)$cluster == "Set_1"
]
resize_and_trim <- function(gr, flank = 300, genome = BSgenome.Hsapiens.UCSC.hg38) {
gr <- resize(gr, width = 1 + 2*flank, fix = "center")
seqlengths(gr) <- seqlengths(genome)[seqlevels(gr)]
gr <- trim(gr)
gr <- gr[width(gr) > 0]
return(gr)
}
H3K9me3_set1_600 <- resize_and_trim(SET_1_gr,flank=300)
H3K9me3_set1_400 <- resize_and_trim(SET_1_gr,flank=200)
H3K9me3_set1_600<- trim(H3K9me3_set1_600)
H3K9me3_set1_600 <- H3K9me3_set1_600[width(H3K9me3_set1_600) > 0]
H3K9me3_set1_400 <- resize(SET_1_gr, width = 1 + 200*2, fix = "center")
H3K9me3_set1_400<- trim(H3K9me3_set1_400)
H3K9me3_set1_400 <- H3K9me3_set1_400[width(H3K9me3_set1_400) > 0]
rtracklayer::export(SET_1_gr, "data/Bed_exports/H3K9me3_Set_1_summits.bed")
rtracklayer::export(H3K9me3_set1_600, "data/Bed_exports/H3K9me3_Set_1_600.bed")
rtracklayer::export(H3K9me3_set1_400, "data/Bed_exports/H3K9me3_Set_1_400.bed")
SET_2_gr <- complete_summit_gr[
!is.na(mcols(complete_summit_gr)$cluster) &
mcols(complete_summit_gr)$cluster == "Set_2"]
H3K9me3_set2_600 <- resize_and_trim(SET_2_gr,flank=300)
H3K9me3_set2_400 <- resize_and_trim(SET_2_gr,flank=400)
H3K9me3_set2_600 <- resize(SET_2_gr, width = 1 + 300*2, fix = "center")
H3K9me3_set2_600 <- trim(H3K9me3_set2_600)
H3K9me3_set2_600 <- H3K9me3_set2_600[width(H3K9me3_set2_600) > 0]
H3K9me3_set2_400 <- resize(SET_2_gr, width = 1 + 200*2, fix = "center")
H3K9me3_set2_400 <- trim(H3K9me3_set2_400)
H3K9me3_set2_400 <- H3K9me3_set2_400[width(H3K9me3_set2_400) > 0]
rtracklayer::export(SET_2_gr, "data/Bed_exports/H3K9me3_Set_2_summits.bed")
rtracklayer::export(H3K9me3_set2_600, "data/Bed_exports/H3K9me3_Set_2_600.bed")
rtracklayer::export(H3K9me3_set2_400, "data/Bed_exports/H3K9me3_Set_2_400.bed")
SET_3_gr <- complete_summit_gr[
!is.na(mcols(complete_summit_gr)$cluster) &
mcols(complete_summit_gr)$cluster == "Set_3"]
H3K9me3_set3_600 <- resize_and_trim(SET_3_gr,flank=300)
H3K9me3_set3_400 <- resize_and_trim(SET_3_gr,flank=200)
H3K9me3_set3_600 <- resize(SET_3_gr, width = 1 + 300*2, fix = "center")
H3K9me3_set3_600 <- trim(H3K9me3_set3_600)
H3K9me3_set3_600 <- H3K9me3_set3_600[width(H3K9me3_set3_600) > 0]
H3K9me3_set3_400 <- resize(SET_3_gr, width = 1 + 200*2, fix = "center")
H3K9me3_set3_400 <- trim(H3K9me3_set3_400)
H3K9me3_set3_400 <- H3K9me3_set3_400[width(H3K9me3_set3_400) > 0]
rtracklayer::export(SET_3_gr, "data/Bed_exports/H3K9me3_Set_3_summits.bed")
rtracklayer::export(H3K9me3_set3_600, "data/Bed_exports/H3K9me3_Set_3_600.bed")
rtracklayer::export(H3K9me3_set3_400, "data/Bed_exports/H3K9me3_Set_3_400.bed")
rtracklayer::export(complete_summit_gr, "data/Bed_exports/H3K9me3_complete_final_summits.bed")
outdir <- "data/Bed_exports/summit_groups/"
dir.create(outdir, showWarnings = FALSE)
group_gr_list <- split(consensus_summits_gr, consensus_summits_gr$group)
for (nm in names(group_gr_list)) {
outfile <- file.path(outdir, paste0(nm, "_H3K9me3_summits.bed"))
export(group_gr_list[[nm]], outfile, format = "BED")
}
names(group_gr_list)
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] parallel grid stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] ChIPseeker_1.42.1 future.apply_1.20.0 future_1.67.0
[4] BiocParallel_1.40.2 rtracklayer_1.66.0 genomation_1.38.0
[7] plyranges_1.26.0 GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
[10] IRanges_2.40.1 S4Vectors_0.44.0 BiocGenerics_0.52.0
[13] lubridate_1.9.4 forcats_1.0.0 stringr_1.5.1
[16] dplyr_1.1.4 purrr_1.1.0 readr_2.1.5
[19] tidyr_1.3.1 tibble_3.3.0 ggplot2_3.5.2
[22] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3
[2] rstudioapi_0.17.1
[3] jsonlite_2.0.0
[4] magrittr_2.0.3
[5] ggtangle_0.0.7
[6] GenomicFeatures_1.58.0
[7] farver_2.1.2
[8] rmarkdown_2.29
[9] fs_1.6.6
[10] BiocIO_1.16.0
[11] zlibbioc_1.52.0
[12] vctrs_0.6.5
[13] memoise_2.0.1
[14] Rsamtools_2.22.0
[15] RCurl_1.98-1.17
[16] ggtree_3.14.0
[17] htmltools_0.5.8.1
[18] S4Arrays_1.6.0
[19] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[20] plotrix_3.8-4
[21] curl_7.0.0
[22] gridGraphics_0.5-1
[23] SparseArray_1.6.2
[24] sass_0.4.10
[25] parallelly_1.45.1
[26] KernSmooth_2.23-26
[27] bslib_0.9.0
[28] plyr_1.8.9
[29] impute_1.80.0
[30] cachem_1.1.0
[31] GenomicAlignments_1.42.0
[32] igraph_2.1.4
[33] whisker_0.4.1
[34] lifecycle_1.0.4
[35] pkgconfig_2.0.3
[36] Matrix_1.7-3
[37] R6_2.6.1
[38] fastmap_1.2.0
[39] GenomeInfoDbData_1.2.13
[40] MatrixGenerics_1.18.1
[41] enrichplot_1.26.6
[42] digest_0.6.37
[43] aplot_0.2.8
[44] colorspace_2.1-1
[45] patchwork_1.3.2
[46] AnnotationDbi_1.68.0
[47] ps_1.9.1
[48] rprojroot_2.1.1
[49] RSQLite_2.4.3
[50] labeling_0.4.3
[51] timechange_0.3.0
[52] httr_1.4.7
[53] abind_1.4-8
[54] compiler_4.4.2
[55] bit64_4.6.0-1
[56] withr_3.0.2
[57] DBI_1.2.3
[58] gplots_3.2.0
[59] R.utils_2.13.0
[60] rappdirs_0.3.3
[61] DelayedArray_0.32.0
[62] rjson_0.2.23
[63] caTools_1.18.3
[64] gtools_3.9.5
[65] tools_4.4.2
[66] ape_5.8-1
[67] httpuv_1.6.16
[68] R.oo_1.27.1
[69] glue_1.8.0
[70] restfulr_0.0.16
[71] callr_3.7.6
[72] nlme_3.1-168
[73] GOSemSim_2.32.0
[74] promises_1.3.3
[75] getPass_0.2-4
[76] gridBase_0.4-7
[77] reshape2_1.4.4
[78] snow_0.4-4
[79] fgsea_1.32.4
[80] generics_0.1.4
[81] gtable_0.3.6
[82] BSgenome_1.74.0
[83] tzdb_0.5.0
[84] R.methodsS3_1.8.2
[85] seqPattern_1.38.0
[86] data.table_1.17.8
[87] hms_1.1.3
[88] XVector_0.46.0
[89] ggrepel_0.9.6
[90] pillar_1.11.0
[91] yulab.utils_0.2.1
[92] vroom_1.6.5
[93] later_1.4.2
[94] splines_4.4.2
[95] treeio_1.30.0
[96] lattice_0.22-7
[97] bit_4.6.0
[98] tidyselect_1.2.1
[99] GO.db_3.20.0
[100] Biostrings_2.74.1
[101] knitr_1.50
[102] git2r_0.36.2
[103] SummarizedExperiment_1.36.0
[104] xfun_0.52
[105] Biobase_2.66.0
[106] matrixStats_1.5.0
[107] stringi_1.8.7
[108] UCSC.utils_1.2.0
[109] lazyeval_0.2.2
[110] boot_1.3-32
[111] ggfun_0.2.0
[112] yaml_2.3.10
[113] evaluate_1.0.5
[114] codetools_0.2-20
[115] qvalue_2.38.0
[116] ggplotify_0.1.2
[117] cli_3.6.5
[118] processx_3.8.6
[119] jquerylib_0.1.4
[120] dichromat_2.0-0.1
[121] Rcpp_1.1.0
[122] globals_0.18.0
[123] png_0.1-8
[124] XML_3.99-0.18
[125] blob_1.2.4
[126] DOSE_4.0.1
[127] bitops_1.0-9
[128] listenv_0.9.1
[129] viridisLite_0.4.2
[130] tidytree_0.4.6
[131] scales_1.4.0
[132] crayon_1.5.3
[133] rlang_1.1.6
[134] fastmatch_1.1-6
[135] cowplot_1.2.0
[136] KEGGREST_1.46.0