H3K27me3 and TE overlap
Renee Matthews
2026-01-23
Last updated: 2026-01-23
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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" repeatmasker_clean <- repeatmasker %>% mutate(
strand = ifelse(strand == "C", "-", "+")
) %>%
mutate(
start = genoStart + 1,
end = genoEnd)%>%
mutate(repFamily= str_remove(repFamily, "\\?$"))
rpt_split <- split(repeatmasker_clean, repeatmasker_clean$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$SINE
LINE_gr <- rpt_split_gr_list$LINE
LTR_gr <- rpt_split_gr_list$LTR
SVA_gr <- rpt_split_gr_list$Retroposon
DNA_gr <- rpt_split_gr_list$DNAH3K27me3_summit_gr <- readRDS("data/RDS_files/H3K27me3_complete_summit_gr.RDS")
peakAnnoList_H3K27me3 <- readRDS("data/motif_lists/H3K27me3_annotated_peaks.RDS")
H3K27me3_lookup <- imap_dfr(peakAnnoList_H3K27me3[1:3], ~
tibble(Peakid = .x@anno$Peakid, cluster = .y)
)
##assigning Peakid as name of summit region
mcols(H3K27me3_summit_gr)$name <- mcols(H3K27me3_summit_gr)$Peakid
comparisons <- tibble(
cluster2 = c("Set_2"),
cluster1 = c("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
)
}SINE
Overlapping SINE family with summits to get a count
sine_hits <- findOverlaps(H3K27me3_summit_gr, SINE_gr, ignore.strand = TRUE)
SINE_overlap_df <- tibble(
summit_id = queryHits(sine_hits),
cluster = mcols(H3K27me3_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(H3K27me3_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") %>%
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))# ---- 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\nH3K27me3"
) +
theme_classic() +
facet_wrap(~comparison)
LINE
Overlapping LINE family with summits to get a count
LINE_hits <- findOverlaps(H3K27me3_summit_gr, LINE_gr, ignore.strand = TRUE)
LINE_overlap_df <- tibble(
summit_id = queryHits(LINE_hits),
cluster = mcols(H3K27me3_summit_gr)$cluster[queryHits(LINE_hits)],
TE_type = mcols(LINE_gr)$repFamily[subjectHits(LINE_hits)])
LINE_counts <- LINE_overlap_df %>%
count(cluster, TE_type, name = "count") %>%
mutate(status = "TE")
total_LINE_summits <- tibble(
cluster = mcols(H3K27me3_summit_gr)$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: 3 × 2
cluster total
<chr> <int>
1 Set_1 148911
2 Set_2 235
3 all_H3K27me3_regions 150464# ---- 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\nH3K27me3"
) +
theme_classic()+
facet_wrap(~comparison)
LTR
Overlapping LTR family summits to get a count
LTR_hits <- findOverlaps(H3K27me3_summit_gr, LTR_gr, ignore.strand = TRUE)
LTR_overlap_df <- tibble(
summit_id = queryHits(LTR_hits),
cluster = mcols(H3K27me3_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(H3K27me3_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: 3 × 2
cluster total
<chr> <int>
1 Set_1 148911
2 Set_2 235
3 all_H3K27me3_regions 150464# ---- 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\nH3K27me3"
) +
theme_classic()+
facet_wrap(~comparison)
### SVA
Overlapping SVA family with summits to get a count
SVA_hits <- findOverlaps(H3K27me3_summit_gr, SVA_gr, ignore.strand = TRUE)
SVA_overlap_df <- tibble(
summit_id = queryHits(SVA_hits),
cluster = mcols(H3K27me3_summit_gr)$cluster[queryHits(SVA_hits)],
TE_type = mcols(SVA_gr)$repName[subjectHits(SVA_hits)])
SVA_counts <- SVA_overlap_df %>%
count(cluster, TE_type, name = "count") %>%
mutate(status = "TE")
total_SVA_summits <- tibble(
cluster = mcols(H3K27me3_summit_gr)$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: 3 × 2
cluster total
<chr> <int>
1 Set_1 148911
2 Set_2 235
3 all_H3K27me3_regions 150464# ---- 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\nH3K27me3"
) +
theme_classic()+
facet_wrap(~comparison)
### DNA
Overlapping DNA family with summits to get a count
DNA_hits <- findOverlaps(H3K27me3_summit_gr, DNA_gr, ignore.strand = TRUE)
DNA_overlap_df <- tibble(
summit_id = queryHits(DNA_hits),
cluster = mcols(H3K27me3_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(H3K27me3_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: 3 × 2
cluster total
<chr> <int>
1 Set_1 148911
2 Set_2 235
3 all_H3K27me3_regions 150464# ---- 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\nH3K27me3"
) +
theme_classic()+
facet_wrap(~comparison)
peakAnnoList_H3K27me3 <- readRDS("data/motif_lists/H3K27me3_annotated_peaks.RDS")
out_dir <- "data/Bed_exports/H3K27me3_sets"
set_list <- names(peakAnnoList_H3K27me3)
for (group_name in set_list) {
cs <- peakAnnoList_H3K27me3[[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, "_H3K27me3.bed"))
# Export
export(gr, bed_file, format = "BED")
cat("Exported:", bed_file, "\n")
}
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] DT_0.33 ggrepel_0.9.6 rtracklayer_1.66.0
[4] genomation_1.38.0 plyranges_1.26.0 GenomicRanges_1.58.0
[7] GenomeInfoDb_1.42.3 IRanges_2.40.1 S4Vectors_0.44.0
[10] BiocGenerics_0.52.0 lubridate_1.9.4 forcats_1.0.0
[13] stringr_1.5.1 dplyr_1.1.4 purrr_1.1.0
[16] readr_2.1.5 tidyr_1.3.1 tibble_3.3.0
[19] ggplot2_3.5.2 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] bitops_1.0-9 rlang_1.1.6
[3] magrittr_2.0.3 git2r_0.36.2
[5] gridBase_0.4-7 matrixStats_1.5.0
[7] compiler_4.4.2 getPass_0.2-4
[9] callr_3.7.6 vctrs_0.6.5
[11] reshape2_1.4.4 pkgconfig_2.0.3
[13] crayon_1.5.3 fastmap_1.2.0
[15] XVector_0.46.0 labeling_0.4.3
[17] utf8_1.2.6 Rsamtools_2.22.0
[19] promises_1.3.3 rmarkdown_2.29
[21] tzdb_0.5.0 UCSC.utils_1.2.0
[23] ps_1.9.1 bit_4.6.0
[25] xfun_0.52 zlibbioc_1.52.0
[27] cachem_1.1.0 jsonlite_2.0.0
[29] later_1.4.2 DelayedArray_0.32.0
[31] BiocParallel_1.40.2 parallel_4.4.2
[33] R6_2.6.1 bslib_0.9.0
[35] stringi_1.8.7 RColorBrewer_1.1-3
[37] jquerylib_0.1.4 Rcpp_1.1.0
[39] SummarizedExperiment_1.36.0 knitr_1.50
[41] httpuv_1.6.16 Matrix_1.7-3
[43] timechange_0.3.0 tidyselect_1.2.1
[45] rstudioapi_0.17.1 dichromat_2.0-0.1
[47] abind_1.4-8 yaml_2.3.10
[49] seqPattern_1.38.0 codetools_0.2-20
[51] curl_7.0.0 processx_3.8.6
[53] lattice_0.22-7 plyr_1.8.9
[55] Biobase_2.66.0 withr_3.0.2
[57] evaluate_1.0.5 Biostrings_2.74.1
[59] pillar_1.11.0 MatrixGenerics_1.18.1
[61] whisker_0.4.1 KernSmooth_2.23-26
[63] generics_0.1.4 vroom_1.6.5
[65] rprojroot_2.1.1 RCurl_1.98-1.17
[67] hms_1.1.3 scales_1.4.0
[69] glue_1.8.0 tools_4.4.2
[71] BiocIO_1.16.0 data.table_1.17.8
[73] BSgenome_1.74.0 GenomicAlignments_1.42.0
[75] fs_1.6.6 XML_3.99-0.18
[77] impute_1.80.0 plotrix_3.8-4
[79] crosstalk_1.2.2 colorspace_2.1-1
[81] GenomeInfoDbData_1.2.13 restfulr_0.0.16
[83] cli_3.6.5 S4Arrays_1.6.0
[85] gtable_0.3.6 sass_0.4.10
[87] digest_0.6.37 SparseArray_1.6.2
[89] htmlwidgets_1.6.4 rjson_0.2.23
[91] farver_2.1.2 htmltools_0.5.8.1
[93] lifecycle_1.0.4 httr_1.4.7
[95] bit64_4.6.0-1