H3K27ac and TE overlap
Renee Matthews
2026-02-06
Last updated: 2026-02-06
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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(ggrepel)
library(DT)First steps: breakdown repeatmasker into groups and pull out the ones by each class I am interested in.
repeatmasker <- read_delim("data/Other_paper_data/repeatmasker_20250911.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)
colnames(repeatmasker) [1] "#bin" "swScore" "milliDiv" "milliDel" "milliIns" "genoName"
[7] "genoStart" "genoEnd" "genoLeft" "strand" "repName" "repClass"
[13] "repFamily" "repStart" "repEnd" "repLeft" "id" autosomes <- paste0("chr", 1:22)
repeatmasker_clean <- repeatmasker %>% mutate(
strand = ifelse(strand == "C", "-", "+")
) %>%
mutate(
start = genoStart + 1,
end = genoEnd)%>%
mutate(repFamily= str_remove(repFamily, "\\?$")) %>%
dplyr::filter(genoName %in% autosomes)
rpt_split <- split(repeatmasker_clean, repeatmasker_clean$repClass)
rpt_split_gr_list <- lapply(rpt_split, function(df) {
GRanges(
seqnames = df$genoName,
ranges = IRanges(start = df$start, end = df$end),
strand = df$strand,
repName = df$repName,
repClass = df$repClass,
repFamily = df$repFamily,
swScore = df$swScore,
milliDiv = df$milliDiv,
id = df$id
)
})SINE_gr <- rpt_split_gr_list$SINE
SINE_df <- SINE_gr %>%
as.data.frame()
SINE_split_df <- split(SINE_df, SINE_df$repFamily)
LINE_gr <- rpt_split_gr_list$LINE
LINE_df <- LINE_gr %>%
as.data.frame()
LINE_split_df <- split(LINE_df, LINE_df$repFamily)
LTR_gr <- rpt_split_gr_list$LTR
LTR_df <- LTR_gr %>%
as.data.frame()
LTR_split_df <- split(LTR_df, LTR_df$repFamily)
SVA_gr <- rpt_split_gr_list$Retroposon
SVA_df <- SVA_gr %>%
as.data.frame()
SVA_split_df <- split(SVA_df, SVA_df$repFamily)
DNA_gr <- rpt_split_gr_list$DNA
DNA_df <- DNA_gr %>%
as.data.frame()
DNA_split_df <- split(DNA_df, DNA_df$repFamily)H3K27ac_summit_gr <- readRDS("data/RDS_files/H3K27ac_complete_summit_gr.RDS")
peakAnnoList_H3K27ac <- readRDS("data/motif_lists/H3K27ac_annotated_peaks.RDS")
H3K27ac_lookup <- imap_dfr(peakAnnoList_H3K27ac[1:3], ~
tibble(Peakid = .x@anno$Peakid, cluster = .y)
)
H3K27ac_sets_gr <- lapply(peakAnnoList_H3K27ac, function(df) {
as_granges(df)
})
##assigning Peakid as name of summit region
mcols(H3K27ac_summit_gr)$name <- mcols(H3K27ac_summit_gr)$Peakid
comparisons <- tibble(
cluster2 = c("Set_2", "Set_3"),
cluster1 = c("Set_1", "Set_1")
)# Generic pairwise Fisher test
test_pair_TE_generic <- function(df_long, te_name, cluster1, cluster2) {
sub_df <- df_long %>%
filter(TE_type == te_name) %>%
complete(
cluster = c(cluster1, cluster2),
status = c("TE", "not_TE"),
fill = list(count = 0))
# enforce fixed order
status_levels <- c("TE", "not_TE")
# assume "status" column has TE vs wnot_TE automatically
statuses <- unique(sub_df$status)
if(length(statuses) != 2) {
# ensure we have exactly two categories, fill missing with 0
sub_df <- sub_df %>%
complete(cluster, status, fill = list(count = 0))
statuses <- unique(sub_df$status)
}
# extract counts for cluster1
c1_counts <- sub_df %>%
filter(cluster == cluster1) %>%
arrange(factor(status, levels = status_levels)) %>% # ensure same order
pull(count)
# extract counts for cluster2
c2_counts <- sub_df %>%
filter(cluster == cluster2) %>%
arrange(factor(status, levels = status_levels)) %>%
pull(count)
# build 2x2 matrix
mat <- matrix(
c(c2_counts, c1_counts),
nrow = 2,
byrow = TRUE,
dimnames = list(
cluster = c(cluster2, cluster1),
category = status_levels
)
)
ft <- tryCatch(
fisher.test(mat, workspace = 2e8),
error = function(e) fisher.test(mat, simulate.p.value = TRUE, B = 1e5)
)
tibble(
TE_type = te_name,
comparison = paste(cluster2, "vs", cluster1),
odds_ratio = ft$estimate,
lower_CI = ft$conf.int[1],
upper_CI = ft$conf.int[2],
p_value = ft$p.value
)
}test_pair_TE_repName <- function(df_long, rep_name, cluster1, cluster2) {
# Subset for the specific repName
sub_df <- df_long %>%
filter(repName == rep_name) %>%
complete(
cluster = c(cluster1, cluster2),
status = c("TE", "not_TE"),
fill = list(count = 0)
)
# fixed order of statuses
status_levels <- c("TE", "not_TE")
# make sure both statuses exist
statuses <- unique(sub_df$status)
if(length(statuses) != 2) {
sub_df <- sub_df %>%
complete(cluster, status, fill = list(count = 0))
statuses <- unique(sub_df$status)
}
# counts for cluster1
c1_counts <- sub_df %>%
filter(cluster == cluster1) %>%
arrange(factor(status, levels = status_levels)) %>%
pull(count)
# counts for cluster2
c2_counts <- sub_df %>%
filter(cluster == cluster2) %>%
arrange(factor(status, levels = status_levels)) %>%
pull(count)
# 2x2 matrix for Fisher test
mat <- matrix(
c(c2_counts, c1_counts),
nrow = 2,
byrow = TRUE,
dimnames = list(
cluster = c(cluster2, cluster1),
category = status_levels
)
)
ft <- tryCatch(
fisher.test(mat, workspace = 2e8),
error = function(e) fisher.test(mat, simulate.p.value = TRUE, B = 1e5)
)
tibble(
repName = rep_name,
comparison = paste(cluster2, "vs", cluster1),
odds_ratio = ft$estimate,
lower_CI = ft$conf.int[1],
upper_CI = ft$conf.int[2],
p_value = ft$p.value
)
}SINE
Overlapping SINE family with summits to get a count
sine_hits <- findOverlaps(H3K27ac_summit_gr, SINE_gr, ignore.strand = TRUE)
SINE_overlap_df <- tibble(
summit_id = queryHits(sine_hits),
cluster = mcols(H3K27ac_summit_gr)$cluster[queryHits(sine_hits)],
TE_type = mcols(SINE_gr)$repFamily[subjectHits(sine_hits)])
SINE_counts <- SINE_overlap_df %>%
count(cluster, TE_type, name = "count") %>%
mutate(status = "TE")
total_SINE_summits <- tibble(
cluster = mcols(H3K27ac_summit_gr)$cluster
) %>% count(cluster, name = "total")
not_SINE_counts <- SINE_counts %>%
left_join(total_SINE_summits, by = "cluster") %>%
mutate(count = total - count,
status = "not_TE") %>%
dplyr::select(cluster, TE_type, status, count)
SINE_df_long <- bind_rows(SINE_counts %>%
dplyr::select(cluster, TE_type, status, count),
not_SINE_counts) %>%
filter(!is.na(cluster))
SINE_results <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
SINE_df_long %>%
distinct(TE_type) %>%
pull() %>%
map_dfr(function(te) {
test_pair_TE_generic(
SINE_df_long,
te_name = te,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))datatable(SINE_counts,
rownames = FALSE,
filter = 'top', # add filter/search boxes
options = list(
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE))total_SINE_summits# A tibble: 4 × 2
cluster total
<chr> <int>
1 Set_1 110084
2 Set_2 1385
3 Set_3 6623
4 <NA> 31979# ---- Prepare the table ----
SINE_counts_display <- SINE_results %>%
# 1. Split comparison
separate(comparison, into = c("cluster2", "cluster1"), sep = " vs ", remove = FALSE) %>%
# 2. Add log2 odds ratio
mutate(log2OR = log2(odds_ratio)) %>%
# 3. Add enrichment/depletion direction
mutate(direction = case_when(
odds_ratio > 1 ~ "enriched",
odds_ratio < 1 ~ "depleted",
TRUE ~ "neutral"
)) %>%
# 4. Flag significant
mutate(significant = FDR < 0.05) %>%
# Optional: arrange for readability
arrange(cluster2, direction, desc(log2OR))
# ---- Create interactive datatable ----
datatable(
SINE_counts_display,
rownames = FALSE,
filter = 'top', # add filter/search boxes
options = list(
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE
)
) %>%
# Conditional coloring by direction
formatStyle(
'direction',
target = 'row',
backgroundColor = styleEqual(
c('enriched', 'depleted'),
c('#FFDD99', '#99CCFF') # enriched = light orange, depleted = light blue
)
) %>%
# Bold significant rows
formatStyle(
'significant',
fontWeight = styleEqual(TRUE, 'bold')
) %>%
# Round numeric columns for readability
formatRound(columns = c('odds_ratio','log2OR','FDR'), digits = 2)ggplot(SINE_results, aes(x = (odds_ratio), y = -log10(FDR),label = TE_type)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(size=3, max.overlaps = Inf)+
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "SINE family enrichment at ROI summits\nH3K27ac"
) +
theme_classic() +
facet_wrap(~comparison)
Overlapping SINE family with ROIs to get a count
count_sine_families <- function(peak_gr, sine_gr, cluster_name) {
hits <- findOverlaps(peak_gr, sine_gr)
if (length(hits) == 0) {
return(tibble(
cluster = cluster_name,
TE_type = character(),
status = character(),
count = integer()
))
}
hit_df <- tibble(
family = ifelse(
mcols(sine_gr)$repFamily[subjectHits(hits)] == "SVA",
mcols(sine_gr)$repName[subjectHits(hits)],
mcols(sine_gr)$repFamily[subjectHits(hits)]
)
)
## TE counts per family (Alu/MIR vs SVA_A/B/C…)
te_counts <- hit_df %>%
count(family, name = "count") %>%
mutate(
cluster = cluster_name,
TE_type = family,
status = "TE"
)
## non-TE peaks (no SINE overlap)
n_total_peaks <- length(peak_gr)
n_sine_peaks <- length(unique(queryHits(hits)))
not_te <- tibble(
cluster = cluster_name,
TE_type = unique(te_counts$TE_type),
status = "not_TE",
count = n_total_peaks - n_sine_peaks
)
bind_rows(te_counts, not_te)
}count_ERV1_repNames <- function(peak_gr, TE_gr, cluster_name) {
# find overlaps
hits <- findOverlaps(peak_gr, TE_gr)
if (length(hits) == 0) {
return(tibble(
cluster = cluster_name,
repName = character(),
status = character(),
count = integer()
))
}
# subset hits to only ERV1
is_ERV1 <- mcols(TE_gr)$repFamily[subjectHits(hits)] == "ERV1"
hits <- hits[is_ERV1]
if (length(hits) == 0) {
return(tibble(
cluster = cluster_name,
repName = character(),
status = character(),
count = integer()
))
}
# make tibble of repNames
hit_df <- tibble(
repName = mcols(TE_gr)$repName[subjectHits(hits)]
)
# TE counts per repName
te_counts <- hit_df %>%
count(repName, name = "count") %>%
mutate(
cluster = cluster_name,
status = "TE"
)
# non-TE peaks
n_total_peaks <- length(peak_gr)
n_TE_peaks <- length(unique(queryHits(hits)))
not_te <- tibble(
cluster = cluster_name,
repName = unique(te_counts$repName),
status = "not_TE",
count = n_total_peaks - n_TE_peaks
)
bind_rows(te_counts, not_te)
}fullROI_long_sine <- purrr::imap_dfr(
H3K27ac_sets_gr,
~count_sine_families(.x, SINE_gr, .y)
)
SINE_results_full <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
fullROI_long_sine %>%
distinct(family) %>%
pull() %>%
map_dfr(function(te) {
test_pair_TE_generic(
fullROI_long_sine,
te_name = te,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))
ggplot(SINE_results_full, aes(x = (odds_ratio), y = -log10(FDR),label = TE_type)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(size=3, max.overlaps = Inf)+
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_vline(xintercept = 1, linetype = 3) +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "SINE family enrichment at full ROI\nH3K27ac"
) +
theme_classic() +
facet_wrap(~comparison)
| Version | Author | Date |
|---|---|---|
| 0606abf | reneeisnowhere | 2026-01-27 |
LINE
Overlapping LINE family with summits to get a count
LINE_hits <- findOverlaps(H3K27ac_summit_gr, LINE_gr, ignore.strand = TRUE)
LINE_overlap_df <- tibble(
summit_id = queryHits(LINE_hits),
cluster = mcols(H3K27ac_summit_gr)$cluster[queryHits(LINE_hits)],
TE_type = mcols(LINE_gr)$repFamily[subjectHits(LINE_hits)])
LINE_counts <- LINE_overlap_df %>%
count(cluster, TE_type, name = "count") %>%
mutate(status = "TE")
total_LINE_summits <- tibble(
cluster = mcols(H3K27ac_summit_gr)$cluster
) %>% count(cluster, name = "total")
not_LINE_counts <- LINE_counts %>%
left_join(total_LINE_summits, by = "cluster") %>%
mutate(count = total - count,
status = "not_TE") %>%
select(cluster, TE_type, status, count)
LINE_df_long <- bind_rows(LINE_counts %>%
dplyr::select(cluster, TE_type, status, count),
not_LINE_counts) %>%
filter(!is.na(cluster))
LINE_results <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
LINE_df_long %>%
distinct(TE_type) %>%
pull() %>%
map_dfr(function(te) {
test_pair_TE_generic(
LINE_df_long,
te_name = te,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))datatable(LINE_counts,
rownames = FALSE,
filter = 'top', # add filter/search boxes
options = list(
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE))total_LINE_summits# A tibble: 4 × 2
cluster total
<chr> <int>
1 Set_1 110084
2 Set_2 1385
3 Set_3 6623
4 <NA> 31979# ---- Prepare the table ----
LINE_counts_display <- LINE_results %>%
# 1. Split comparison
separate(comparison, into = c("cluster2", "cluster1"), sep = " vs ", remove = FALSE) %>%
# 2. Add log2 odds ratio
mutate(log2OR = log2(odds_ratio)) %>%
# 3. Add enrichment/depletion direction
mutate(direction = case_when(
odds_ratio > 1 ~ "enriched",
odds_ratio < 1 ~ "depleted",
TRUE ~ "neutral"
)) %>%
# 4. Flag significant
mutate(significant = FDR < 0.05) %>%
# Optional: arrange for readability
arrange(cluster2, direction, desc(log2OR))
# ---- Create interactive datatable ----
datatable(
LINE_counts_display,
rownames = FALSE,
filter = 'top', # add filter/search boxes
options = list(
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE
)
) %>%
# Conditional coloring by direction
formatStyle(
'direction',
target = 'row',
backgroundColor = styleEqual(
c('enriched', 'depleted'),
c('#FFDD99', '#99CCFF') # enriched = light orange, depleted = light blue
)
) %>%
# Bold significant rows
formatStyle(
'significant',
fontWeight = styleEqual(TRUE, 'bold')
) %>%
# Round numeric columns for readability
formatRound(columns = c('odds_ratio','log2OR','FDR'), digits = 2)ggplot(LINE_results, aes(x = (odds_ratio), y = -log10(FDR),label = TE_type)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(size=3, max.overlaps = Inf)+
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "LINE family enrichment at ROI summits\nH3K27ac"
) +
theme_classic()+
facet_wrap(~comparison)
Overlapping LINE families using Full ROI
fullROI_long_LINE <- purrr::imap_dfr(
H3K27ac_sets_gr,
~count_sine_families(.x, LINE_gr, .y)
)
LINE_results_full <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
fullROI_long_LINE %>%
distinct(family) %>%
pull() %>%
map_dfr(function(te) {
test_pair_TE_generic(
fullROI_long_LINE,
te_name = te,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))
ggplot(LINE_results_full, aes(x = (odds_ratio), y = -log10(FDR),label = TE_type)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(size=3, max.overlaps = Inf)+
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_vline(xintercept = 1, linetype = 3) +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "LINE family enrichment at full ROI\nH3K27ac"
) +
theme_classic() +
facet_wrap(~comparison)
| Version | Author | Date |
|---|---|---|
| 0606abf | reneeisnowhere | 2026-01-27 |
LTR
Overlapping LTR family summits to get a count
LTR_hits <- findOverlaps(H3K27ac_summit_gr, LTR_gr, ignore.strand = TRUE)
LTR_overlap_df <- tibble(
summit_id = queryHits(LTR_hits),
cluster = mcols(H3K27ac_summit_gr)$cluster[queryHits(LTR_hits)],
TE_type = mcols(LTR_gr)$repFamily[subjectHits(LTR_hits)])
LTR_counts <- LTR_overlap_df %>%
count(cluster, TE_type, name = "count") %>%
mutate(status = "TE")
total_LTR_summits <- tibble(
cluster = mcols(H3K27ac_summit_gr)$cluster
) %>% count(cluster, name = "total")
not_LTR_counts <- LTR_counts %>%
left_join(total_LTR_summits, by = "cluster") %>%
mutate(count = total - count,
status = "not_TE") %>%
select(cluster, TE_type, status, count)
LTR_df_long <- bind_rows(LTR_counts %>%
dplyr::select(cluster, TE_type, status, count),
not_LTR_counts) %>%
filter(!is.na(cluster))
LTR_results <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
LTR_df_long %>%
distinct(TE_type) %>%
pull() %>%
map_dfr(function(te) {
test_pair_TE_generic(
LTR_df_long,
te_name = te,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))datatable(LTR_counts,
rownames = FALSE,
filter = 'top', # add filter/search boxes
options = list(
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE))total_LTR_summits# A tibble: 4 × 2
cluster total
<chr> <int>
1 Set_1 110084
2 Set_2 1385
3 Set_3 6623
4 <NA> 31979# ---- Prepare the table ----
LTR_counts_display <- LTR_results %>%
# 1. Split comparison
separate(comparison, into = c("cluster2", "cluster1"), sep = " vs ", remove = FALSE) %>%
# 2. Add log2 odds ratio
mutate(log2OR = log2(odds_ratio)) %>%
# 3. Add enrichment/depletion direction
mutate(direction = case_when(
odds_ratio > 1 ~ "enriched",
odds_ratio < 1 ~ "depleted",
TRUE ~ "neutral"
)) %>%
# 4. Flag significant
mutate(significant = FDR < 0.05) %>%
# Optional: arrange for readability
arrange(cluster2, direction, desc(log2OR))
# ---- Create interactive datatable ----
datatable(
LTR_counts_display,
rownames = FALSE,
filter = 'top', # add filter/search boxes
options = list(
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE
)
) %>%
# Conditional coloring by direction
formatStyle(
'direction',
target = 'row',
backgroundColor = styleEqual(
c('enriched', 'depleted'),
c('#FFDD99', '#99CCFF') # enriched = light orange, depleted = light blue
)
) %>%
# Bold significant rows
formatStyle(
'significant',
fontWeight = styleEqual(TRUE, 'bold')
) %>%
# Round numeric columns for readability
formatRound(columns = c('odds_ratio','log2OR','FDR'), digits = 2)ggplot(LTR_results, aes(x = (odds_ratio), y = -log10(FDR),label = TE_type)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(size=3, max.overlaps = Inf)+
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "LTR family enrichment at ROI summits\nH3K27ac"
) +
theme_classic()+
facet_wrap(~comparison)
LTR_hits <- findOverlaps(H3K27ac_summit_gr, LTR_gr, ignore.strand = TRUE)
LTR_name_overlap_df <- tibble(
summit_id = queryHits(LTR_hits),
cluster = mcols(H3K27ac_summit_gr)$cluster[queryHits(LTR_hits)],
TE_type = mcols(LTR_gr)$repFamily[subjectHits(LTR_hits)],
repName= mcols(LTR_gr)$repName[subjectHits(LTR_hits)])
LTR_ERV1_counts <-
LTR_name_overlap_df %>%
dplyr::filter(TE_type=="ERV1") %>%
count(cluster, repName, name = "count") %>%
mutate(status = "TE")
total_LTR_summits <- tibble(
cluster = mcols(H3K27ac_summit_gr)$cluster
) %>% count(cluster, name = "total")
not_LTR_ERV1_counts <- LTR_ERV1_counts %>%
left_join(total_LTR_summits, by = "cluster") %>%
mutate(count = total - count,
status = "not_TE") %>%
select(cluster, repName, status, count)
LTR_ERV1_df_long <- bind_rows(LTR_ERV1_counts %>%
dplyr::select(cluster, repName, status, count),
not_LTR_ERV1_counts) %>%
filter(!is.na(cluster))
ERV1_LTR_results <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
LTR_ERV1_df_long %>%
distinct(repName) %>%
pull() %>%
map_dfr(function(rep) {
test_pair_TE_repName(
LTR_ERV1_df_long,
rep_name = rep,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))ggplot(ERV1_LTR_results, aes(x = (odds_ratio), y = -log10(FDR),label = repName)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(data = subset(ERV1_LTR_results, FDR < 0.05), # only significant points
size = 3,
max.overlaps = 100
) +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "LTR Name (type) enrichment at ROI summits\nH3K27ac"
) +
theme_classic()+
facet_wrap(~comparison)
| Version | Author | Date |
|---|---|---|
| 03a278d | reneeisnowhere | 2026-02-05 |
Overlapping LTR families using Full ROI
fullROI_long_LTR <- purrr::imap_dfr(
H3K27ac_sets_gr,
~count_sine_families(.x, LTR_gr, .y)
)
LTR_results_full <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
fullROI_long_LTR %>%
distinct(family) %>%
pull() %>%
map_dfr(function(te) {
test_pair_TE_generic(
fullROI_long_LTR,
te_name = te,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))
ggplot(LTR_results_full, aes(x = (odds_ratio), y = -log10(FDR),label = TE_type)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(size=3, max.overlaps = Inf)+
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_vline(xintercept = 1, linetype = 3) +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "LTR family enrichment at full ROI\nH3K27ac"
) +
theme_classic() +
facet_wrap(~comparison)
fullROI_long_LTR_ERV1 <- purrr::imap_dfr(
H3K27ac_sets_gr,
~count_ERV1_repNames(.x, LTR_gr, .y)
)
LTRERV1_results_full <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
fullROI_long_LTR_ERV1 %>%
distinct(repName) %>%
pull() %>%
map_dfr(function(rep) {
test_pair_TE_repName(
LTR_ERV1_df_long,
rep_name = rep,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))
ggplot(LTRERV1_results_full, aes(x = (odds_ratio), y = -log10(FDR),label = repName)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
geom_text_repel(data = subset(LTRERV1_results_full, FDR < 0.05), # only significant points
size = 3,
max.overlaps = Inf
) +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_vline(xintercept = 1, linetype = 3) +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "LTR:ERV1 type enrichment at full ROI\nH3K27ac"
) +
theme_classic() +
facet_wrap(~comparison)
| Version | Author | Date |
|---|---|---|
| 03a278d | reneeisnowhere | 2026-02-05 |
SVA
Overlapping SVA family with summits to get a count
SVA_hits <- findOverlaps(H3K27ac_summit_gr, SVA_gr, ignore.strand = TRUE)
SVA_overlap_df <- tibble(
summit_id = queryHits(SVA_hits),
cluster = mcols(H3K27ac_summit_gr)$cluster[queryHits(SVA_hits)],
TE_type = mcols(SVA_gr)$repName[subjectHits(SVA_hits)])
SVA_counts <- SVA_overlap_df %>%
count(cluster, TE_type, name = "count") %>%
mutate(status = "TE")
total_SVA_summits <- tibble(
cluster = mcols(H3K27ac_summit_gr)$cluster
) %>% count(cluster, name = "total")
not_SVA_counts <- SVA_counts %>%
left_join(total_SVA_summits, by = "cluster") %>%
mutate(count = total - count,
status = "not_TE") %>%
select(cluster, TE_type, status, count)
SVA_df_long <- bind_rows(SVA_counts %>%
dplyr::select(cluster, TE_type, status, count),
not_SVA_counts) %>%
filter(!is.na(cluster))
SVA_results <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
SVA_df_long %>%
distinct(TE_type) %>%
pull() %>%
map_dfr(function(te) {
test_pair_TE_generic(
SVA_df_long,
te_name = te,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))datatable(SVA_counts,
rownames = FALSE,
filter = 'top', # add filter/search boxes
options = list(
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE))total_SVA_summits# A tibble: 4 × 2
cluster total
<chr> <int>
1 Set_1 110084
2 Set_2 1385
3 Set_3 6623
4 <NA> 31979# ---- Prepare the table ----
SVA_counts_display <- SVA_results %>%
# 1. Split comparison
separate(comparison, into = c("cluster2", "cluster1"), sep = " vs ", remove = FALSE) %>%
# 2. Add log2 odds ratio
mutate(log2OR = log2(odds_ratio)) %>%
# 3. Add enrichment/depletion direction
mutate(direction = case_when(
odds_ratio > 1 ~ "enriched",
odds_ratio < 1 ~ "depleted",
TRUE ~ "neutral"
)) %>%
# 4. Flag significant
mutate(significant = FDR < 0.05) %>%
# Optional: arrange for readability
arrange(cluster2, direction, desc(log2OR))
# ---- Create interactive datatable ----
datatable(
SVA_counts_display,
rownames = FALSE,
filter = 'top', # add filter/search boxes
options = list(
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE
)
) %>%
# Conditional coloring by direction
formatStyle(
'direction',
target = 'row',
backgroundColor = styleEqual(
c('enriched', 'depleted'),
c('#FFDD99', '#99CCFF') # enriched = light orange, depleted = light blue
)
) %>%
# Bold significant rows
formatStyle(
'significant',
fontWeight = styleEqual(TRUE, 'bold')
) %>%
# Round numeric columns for readability
formatRound(columns = c('odds_ratio','log2OR','FDR'), digits = 2)ggplot(SVA_results, aes(x = (odds_ratio), y = -log10(FDR),label = TE_type)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(size=3, max.overlaps = Inf)+
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "SVA family enrichment at ROI summits\nH3K27ac"
) +
theme_classic()+
facet_wrap(~comparison)
Overlapping SVA families using Full ROI
fullROI_long_SVA <- purrr::imap_dfr(
H3K27ac_sets_gr,
~count_sine_families(.x, SVA_gr, .y)
)
SVA_results_full <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
fullROI_long_SVA %>%
distinct(family) %>%
pull() %>%
map_dfr(function(te) {
test_pair_TE_generic(
fullROI_long_SVA,
te_name = te,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))
ggplot(SVA_results_full, aes(x = (odds_ratio), y = -log10(FDR),label = TE_type)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(size=3, max.overlaps = Inf)+
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_vline(xintercept = 1, linetype = 3) +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "SVA family enrichment at full ROI\nH3K27ac"
) +
theme_classic() +
facet_wrap(~comparison)
DNA
Overlapping DNA family with summits to get a count
DNA_hits <- findOverlaps(H3K27ac_summit_gr, DNA_gr, ignore.strand = TRUE)
DNA_overlap_df <- tibble(
summit_id = queryHits(DNA_hits),
cluster = mcols(H3K27ac_summit_gr)$cluster[queryHits(DNA_hits)],
TE_type = mcols(DNA_gr)$repFamily[subjectHits(DNA_hits)])
DNA_counts <- DNA_overlap_df %>%
count(cluster, TE_type, name = "count") %>%
mutate(status = "TE")
total_DNA_summits <- tibble(
cluster = mcols(H3K27ac_summit_gr)$cluster
) %>% count(cluster, name = "total")
not_DNA_counts <- DNA_counts %>%
left_join(total_DNA_summits, by = "cluster") %>%
mutate(count = total - count,
status = "not_TE") %>%
select(cluster, TE_type, status, count)
DNA_df_long <- bind_rows(DNA_counts %>%
dplyr::select(cluster, TE_type, status, count),
not_DNA_counts) %>%
filter(!is.na(cluster))
DNA_results <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
DNA_df_long %>%
distinct(TE_type) %>%
pull() %>%
map_dfr(function(te) {
test_pair_TE_generic(
DNA_df_long,
te_name = te,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))datatable(DNA_counts,
rownames = FALSE,
filter = 'top', # add filter/search boxes
options = list(
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE))total_DNA_summits# A tibble: 4 × 2
cluster total
<chr> <int>
1 Set_1 110084
2 Set_2 1385
3 Set_3 6623
4 <NA> 31979# ---- Prepare the table ----
DNA_counts_display <- DNA_results %>%
# 1. Split comparison
separate(comparison, into = c("cluster2", "cluster1"), sep = " vs ", remove = FALSE) %>%
# 2. Add log2 odds ratio
mutate(log2OR = log2(odds_ratio)) %>%
# 3. Add enrichment/depletion direction
mutate(direction = case_when(
odds_ratio > 1 ~ "enriched",
odds_ratio < 1 ~ "depleted",
TRUE ~ "neutral"
)) %>%
# 4. Flag significant
mutate(significant = FDR < 0.05) %>%
# Optional: arrange for readability
arrange(cluster2, direction, desc(log2OR))
# ---- Create interactive datatable ----
datatable(
DNA_counts_display,
rownames = FALSE,
filter = 'top', # add filter/search boxes
options = list(
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE
)
) %>%
# Conditional coloring by direction
formatStyle(
'direction',
target = 'row',
backgroundColor = styleEqual(
c('enriched', 'depleted'),
c('#FFDD99', '#99CCFF') # enriched = light orange, depleted = light blue
)
) %>%
# Bold significant rows
formatStyle(
'significant',
fontWeight = styleEqual(TRUE, 'bold')
) %>%
# Round numeric columns for readability
formatRound(columns = c('odds_ratio','log2OR','FDR'), digits = 2)ggplot(DNA_results, aes(x = (odds_ratio), y = -log10(FDR),label = TE_type)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(size=3, max.overlaps = Inf)+
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "DNA family enrichment at ROI summits\nH3K27ac"
) +
theme_classic()+
facet_wrap(~comparison)
peakAnnoList_H3K27ac <- readRDS("data/motif_lists/H3K27ac_annotated_peaks.RDS")
out_dir <- "data/Bed_exports/H3K27ac_sets"
set_list <- names(peakAnnoList_H3K27ac)
for (group_name in set_list) {
cs <- peakAnnoList_H3K27ac[[group_name]]
gr <- cs@anno
# Set BED name column
mcols(gr)$name <- mcols(gr)$Peakid
# Optional: if you want a score column
if(!"score" %in% colnames(mcols(gr))) {
mcols(gr)$score <- 0
}
# Export to BED
bed_file <- file.path(out_dir, paste0(group_name, "_H3K27ac.bed"))
# Export
export(gr, bed_file, format = "BED")
cat("Exported:", bed_file, "\n")
}Overlapping DNA families using Full ROI
fullROI_long_DNA <- purrr::imap_dfr(
H3K27ac_sets_gr,
~count_sine_families(.x, DNA_gr, .y)
)
DNA_results_full <- comparisons %>%
mutate(results = map2(cluster2, cluster1, function(c2, c1) {
fullROI_long_DNA %>%
distinct(family) %>%
pull() %>%
map_dfr(function(te) {
test_pair_TE_generic(
fullROI_long_DNA,
te_name = te,
cluster1 = c1,
cluster2 = c2
)
})
})
) %>%
unnest(results) %>%
mutate(FDR = p.adjust(p_value, method = "BH"))
ggplot(DNA_results_full, aes(x = (odds_ratio), y = -log10(FDR),label = TE_type)) +
geom_point(aes(color = odds_ratio > 1), size = 3) +
# geom_text_repel(data = subset(SVA_results, FDR < 0.05)) +
geom_text_repel(size=3, max.overlaps = Inf)+
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_vline(xintercept = 1, linetype = 3) +
labs(
x = "(Odds Ratio)",
y = "-log10(FDR)",
title = "DNA family enrichment at full ROI\nH3K27ac"
) +
theme_classic() +
facet_wrap(~comparison)
Proportions of SETs and SETS of TEs
H3K27ac_set_case_lfc <- readRDS("data/RDS_files/H3K27ac_set_case_lfc.RDS")
annotated_tables <- readRDS("data/TE_annotation/annotated_tables_1bp_ol.RDS")
anno_H3K27ac <- annotated_tables$annotated_H3K27ac
H3K27ac_lookup# A tibble: 118,092 × 2
Peakid cluster
<chr> <chr>
1 chr1:778197-779527 Set_1
2 chr1:819712-820327 Set_1
3 chr1:821438-822526 Set_1
4 chr1:825652-826234 Set_1
5 chr1:826639-827783 Set_1
6 chr1:831254-832817 Set_1
7 chr1:910490-911487 Set_1
8 chr1:913389-914255 Set_1
9 chr1:918697-919102 Set_1
10 chr1:920182-924328 Set_1
# ℹ 118,082 more rowsH3K27ac_set_case_lookup <- bind_rows(H3K27ac_set_case_lfc, .id = "set_case") %>%
dplyr::select(Peakid,cluster:set_case)
anno_H3K27ac %>%
left_join(H3K27ac_set_case_lookup) %>%
group_by(cluster) %>%
count() %>%
ungroup() %>%
mutate(percent = n / sum(n)) %>%
ggplot(.,aes(x=cluster, y=percent, fill=cluster)) +
geom_col() +
geom_text(
aes(label = sprintf("%.3f", percent)),
vjust = -0.3,
size = 3
) +
ylab("proportion")+
ggtitle("Proportion of ROIs by cluster")+
theme_bw() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text = element_text(face = "bold")
)
| Version | Author | Date |
|---|---|---|
| 26f72a7 | reneeisnowhere | 2026-02-05 |
# LTR_counts %>%
# group_by(cluster) %>%
# summarize(n_LTR = sum(count),
# .groups = "drop") %>%
# mutate(percent = n_LTR/ sum(n_LTR))
H3K27ac_te_summary <- anno_H3K27ac %>%
left_join(H3K27ac_set_case_lookup) %>%
mutate(repClass = na_if(repClass, ""), # treat empty as NA
n_TE_class = case_when(
is.na(repClass) ~ 0L,
TRUE ~ lengths(strsplit(repClass, ";")))) %>%
mutate( category = case_when(n_TE_class == 0 ~ "No TE overlap",
n_TE_class == 1 ~ "Single TE class",
n_TE_class > 1 ~ "Multiple TE classes"))
pie_df <- H3K27ac_te_summary %>%
count(category) %>%
mutate(
percent = n / sum(n) * 100
)
ggplot(pie_df, aes(x = "", y = percent, fill = category)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
theme_void() +
geom_text(
aes(label = sprintf("%.1f%%", percent)),
position = position_stack(vjust = 0.5),
size = 4
) +
labs(
title = "ROI overlap with transposable elements"
)
| Version | Author | Date |
|---|---|---|
| 26f72a7 | reneeisnowhere | 2026-02-05 |
of ROIs that overlap TEs, what is the distrubution across TE families?
H3K27ac_te_family_long <- H3K27ac_te_summary %>%
filter(!is.na(repClass), repClass != "") %>% # TE-positive only
separate_rows(repClass, sep = ";") %>%
distinct(Peakid, repClass,cluster)
fam_pie_df <- H3K27ac_te_family_long %>%
count(repClass, name = "n") %>%
mutate(percent = n / sum(n)*100) %>%
mutate(
repClass = if_else(percent < 2, "Other", repClass)
) %>%
group_by(repClass) %>%
summarise(n = sum(n), .groups = "drop") %>%
mutate(percent = n / sum(n) * 100)
ggplot(fam_pie_df, aes(x = "", y = percent, fill = repClass)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
theme_void() +
geom_text(
aes(label = sprintf("%s\n%.1f%%", repClass, percent)),
position = position_stack(vjust = 0.5),
size = 3
) +
labs(
title = "TE Class composition among TE-overlapping ROIs",
fill = "TE Class")
| Version | Author | Date |
|---|---|---|
| 26f72a7 | reneeisnowhere | 2026-02-05 |
# unique(rpt_split_gr_list$LTR$repFamily)H3K27ac_long_repFamily <- H3K27ac_te_summary %>%
filter(!is.na(repFamily), repFamily != "") %>% # TE-positive only
separate_rows(repFamily, sep = ";") %>%
distinct(Peakid, repFamily)
human_genome_LTR <- LTR_df %>%
dplyr::filter(repFamily %in% c("ERV1", "ERVK","ERVL", "ERVL-MaLR","Gypsy","LTR")) %>%
count(repFamily, name = "n") %>%
mutate(percent= n/ sum(n) * 100)
H3K27ac_LTR_pie_df <- H3K27ac_long_repFamily %>%
dplyr::filter(repFamily %in% c("ERV1", "ERVK","ERVL", "ERVL-MaLR","Gypsy","LTR")) %>%
count(repFamily, name = "n") %>%
mutate(percent= n/ sum(n) * 100)
ggplot(H3K27ac_LTR_pie_df, aes(x = "", y = percent, fill = repFamily)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
theme_void() +
geom_text(
aes(label = sprintf("%s\n%.1f%%", repFamily, percent)),
position = position_stack(vjust = 0.5),
size = 3
) +
labs(
title = "LTR family composition among TE-overlapping ROIs",
fill = "TE family"
)
| Version | Author | Date |
|---|---|---|
| 26f72a7 | reneeisnowhere | 2026-02-05 |
ggplot(human_genome_LTR, aes(x = "", y = percent, fill = repFamily)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
theme_void() +
geom_text(
aes(label = sprintf("%s\n%.1f%%", repFamily, percent)),
position = position_stack(vjust = 0.5),
size = 3
) +
labs(
title = "LTR family composition across human genome",
fill = "TE family"
)
rpt_genome <- repeatmasker_clean %>%
mutate(cluster = "hg38") %>%
group_by(cluster, repClass) %>%
summarise(n = n(), .groups = "drop") %>%
mutate(percent = n / sum(n) * 100) %>%
mutate(
repClass = if_else(percent < 1.2, "Other", repClass)
) %>%
group_by(repClass) %>%
summarise(n = sum(n), .groups = "drop") %>%
mutate(percent = n / sum(n) * 100)
H3K27ac_te_family_clust <- H3K27ac_te_family_long %>%
dplyr::filter(cluster != "not_assigned") %>%
distinct(Peakid, cluster, repClass) %>%
count(cluster, repClass, name = "n") %>%
group_by(cluster) %>%
mutate(percent = n / sum(n)*100) %>%
mutate(
repClass = if_else(percent < 2, "Other", repClass)
) %>%
group_by(cluster,repClass) %>%
summarise(n = sum(n), .groups = "drop") %>%
group_by(cluster) %>%
mutate(percent = n / sum(n) * 100) %>%
rbind((rpt_genome %>% mutate(cluster="hg38")))
ggplot(H3K27ac_te_family_clust, aes(x = "", y=percent, fill=repClass))+
geom_col(width =1, color="white")+
coord_polar(theta = "y")+
geom_text(
aes(x= 1.5,label = sprintf("%s\n%.1f%%", repClass, percent)),
position = position_stack(vjust = 0.5),
size = 3
) +
facet_wrap(~cluster)+
theme_void()+
labs(
title = "TE Class composition among TE-overlapping ROIs per set",
fill = "TE family"
)
| Version | Author | Date |
|---|---|---|
| 03a278d | reneeisnowhere | 2026-02-05 |
ggplot(H3K27ac_te_family_clust, aes(x = cluster, y=percent, fill=repClass))+
geom_col(width = 0.8, color = "white") +
geom_text(
aes(label = sprintf("%s\n%.1f%%", repClass, percent)),
position = position_stack(vjust = 0.5),
size = 3
) +
theme_bw() +
labs(
title = "TE Class composition among TE-overlapping ROIs per set",
fill = "TE family"
)
ggplot(H3K27ac_te_family_clust, aes(x = cluster, y=percent, fill=repClass))+
geom_col(width = 0.8, color = "white") +
# geom_text(
# aes(label = sprintf("%s\n%.1f%%", repClass, percent)),
# position = position_stack(vjust = 0.5),
# size = 3
# ) +
theme_bw() +
labs(
title = "TE Class composition among TE-overlapping ROIs per set",
fill = "TE family"
)
H3K27ac_long_repFamily <-
H3K27ac_te_summary %>%
dplyr::filter(cluster != "not_assigned") %>%
filter(!is.na(repFamily), repFamily != "") %>% # TE-positive only
separate_rows(repFamily, sep = ";") %>%
distinct(cluster,Peakid, repFamily) %>%
dplyr::filter(repFamily %in% c("ERV1", "ERVK","ERVL", "ERVL-MaLR","Gypsy","LTR")) %>%
count(cluster,repFamily, name = "n") %>%
group_by(cluster) %>%
mutate(percent = n / sum(n)*100) %>%
# mutate(
# repFamily = if_else(percent < 2, "Other", repFamily)
# ) %>%
group_by(cluster,repFamily) %>%
summarise(n = sum(n), .groups = "drop") %>%
group_by(cluster) %>%
mutate(percent = n / sum(n) * 100)
ggplot(H3K27ac_long_repFamily, aes(x = "", y=percent, fill=repFamily))+
geom_col(width =1, color="white")+
coord_polar(theta = "y")+
geom_text_repel(
aes(label = sprintf("%s\n%.1f%%", repFamily, percent)),
position = position_stack(vjust = 0.5),
size = 3,
show.legend = FALSE
) +
facet_wrap(~cluster)+
theme_void()+
labs(
title = "TE family relative abundance among ROIs by set",
fill = "TE family"
)
| Version | Author | Date |
|---|---|---|
| 03a278d | reneeisnowhere | 2026-02-05 |
LTR_bar_df <-H3K27ac_te_summary %>%
filter(!is.na(repFamily), repFamily != "") %>% # TE-positive only
separate_rows(repFamily, sep = ";") %>%
distinct(cluster,Peakid, repFamily) %>%
dplyr::filter(repFamily %in% c("ERV1", "ERVK","ERVL", "ERVL-MaLR","Gypsy","LTR")) %>%
group_by(repFamily, cluster) %>%
summarise(n = dplyr::n(), .groups = "drop") %>%
# optionally convert NA to a string
mutate(cluster = if_else(is.na(cluster), "NA", cluster))
ggplot(LTR_bar_df, aes(x = repFamily, y = n, fill = cluster)) +
geom_col(position = "fill") +
theme_classic() +
labs(
x = "LTR Family",
y = "Proportion of ROIs",
fill = "Cluster",
title = "Proportion of ROI clusters for each LTR family"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
| Version | Author | Date |
|---|---|---|
| 03a278d | reneeisnowhere | 2026-02-05 |
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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ChIPseeker_1.42.1 DT_0.33 ggrepel_0.9.6
[4] rtracklayer_1.66.0 genomation_1.38.0 plyranges_1.26.0
[7] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3 IRanges_2.40.1
[10] S4Vectors_0.44.0 BiocGenerics_0.52.0 lubridate_1.9.4
[13] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[16] purrr_1.1.0 readr_2.1.5 tidyr_1.3.1
[19] tibble_3.3.0 ggplot2_3.5.2 tidyverse_2.0.0
[22] 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] SparseArray_1.6.2
[23] gridGraphics_0.5-1
[24] sass_0.4.10
[25] KernSmooth_2.23-26
[26] bslib_0.9.0
[27] htmlwidgets_1.6.4
[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] crosstalk_1.2.2
[50] RSQLite_2.4.3
[51] labeling_0.4.3
[52] timechange_0.3.0
[53] httr_1.4.7
[54] abind_1.4-8
[55] compiler_4.4.2
[56] bit64_4.6.0-1
[57] withr_3.0.2
[58] BiocParallel_1.40.2
[59] DBI_1.2.3
[60] gplots_3.2.0
[61] R.utils_2.13.0
[62] rappdirs_0.3.3
[63] DelayedArray_0.32.0
[64] rjson_0.2.23
[65] caTools_1.18.3
[66] gtools_3.9.5
[67] tools_4.4.2
[68] ape_5.8-1
[69] httpuv_1.6.16
[70] R.oo_1.27.1
[71] glue_1.8.0
[72] restfulr_0.0.16
[73] callr_3.7.6
[74] nlme_3.1-168
[75] GOSemSim_2.32.0
[76] promises_1.3.3
[77] getPass_0.2-4
[78] gridBase_0.4-7
[79] reshape2_1.4.4
[80] fgsea_1.32.4
[81] generics_0.1.4
[82] gtable_0.3.6
[83] BSgenome_1.74.0
[84] tzdb_0.5.0
[85] R.methodsS3_1.8.2
[86] seqPattern_1.38.0
[87] data.table_1.17.8
[88] hms_1.1.3
[89] utf8_1.2.6
[90] XVector_0.46.0
[91] pillar_1.11.0
[92] yulab.utils_0.2.1
[93] vroom_1.6.5
[94] later_1.4.2
[95] splines_4.4.2
[96] treeio_1.30.0
[97] lattice_0.22-7
[98] bit_4.6.0
[99] tidyselect_1.2.1
[100] GO.db_3.20.0
[101] Biostrings_2.74.1
[102] knitr_1.50
[103] git2r_0.36.2
[104] SummarizedExperiment_1.36.0
[105] xfun_0.52
[106] Biobase_2.66.0
[107] matrixStats_1.5.0
[108] stringi_1.8.7
[109] UCSC.utils_1.2.0
[110] lazyeval_0.2.2
[111] boot_1.3-32
[112] ggfun_0.2.0
[113] yaml_2.3.10
[114] evaluate_1.0.5
[115] codetools_0.2-20
[116] qvalue_2.38.0
[117] ggplotify_0.1.2
[118] cli_3.6.5
[119] processx_3.8.6
[120] jquerylib_0.1.4
[121] dichromat_2.0-0.1
[122] Rcpp_1.1.0
[123] png_0.1-8
[124] XML_3.99-0.18
[125] parallel_4.4.2
[126] blob_1.2.4
[127] DOSE_4.0.1
[128] bitops_1.0-9
[129] tidytree_0.4.6
[130] scales_1.4.0
[131] crayon_1.5.3
[132] rlang_1.1.6
[133] fastmatch_1.1-6
[134] cowplot_1.2.0
[135] KEGGREST_1.46.0