TEs across histones
Renee Matthews
2026-01-30
Last updated: 2026-01-30
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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)
library(ChIPseeker)
library(ggVennDiagram)
library(smplot2)First steps: Pulling and overlapping each ROI set with specific families of TEs
repeatmasker <- read_delim("data/Other_paper_data/repeatmasker_20250911.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)
autosomes <- paste0("chr", 1:22)
repeatmasker_clean <- repeatmasker %>% mutate(
strand = ifelse(strand == "C", "-", "+")
) %>%
mutate(
start = genoStart + 1,
end = genoEnd)%>%
mutate(repFamily= str_remove(repFamily, "\\?$")) %>%
dplyr::filter(genoName %in% autosomes) %>%
mutate(RM_id=paste0(genoName,":",start,"-",end,":",id))
rpt_split <- split(repeatmasker_clean, repeatmasker_clean$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$milliDiv,
milliDel = df$milliDel,
milliIns = df$milliIns,
RM_id = df$RM_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)Pulling in ROI granges for H3K27ac and H3K9me3
peakAnnoList_H3K9me3 <- readRDS("data/motif_lists/H3K9me3_annotated_peaks.RDS")
peakAnnoList_H3K27ac <- readRDS("data/motif_lists/H3K27ac_annotated_peaks.RDS")
H3K9me3_lookup <- imap_dfr(peakAnnoList_H3K9me3[1:3], ~
tibble(Peakid = .x@anno$Peakid, cluster = .y)
)
H3K27ac_lookup <- imap_dfr(peakAnnoList_H3K27ac[1:3], ~
tibble(Peakid = .x@anno$Peakid, cluster = .y)
)
H3K9me3_sets_gr <- lapply(peakAnnoList_H3K9me3, function(df) {
as_granges(df)
})
H3K27ac_sets_gr <- lapply(peakAnnoList_H3K27ac, function(df) {
as_granges(df)
})H3K9me3_toplist <- readRDS( "data/DER_data/H3K9me3_toplist_nooutlier.RDS")
H3K27ac_toplist <- readRDS( "data/DER_data/H3K27ac_toplist.RDS")
H3K27ac_toptable_list <- bind_rows(H3K27ac_toplist, .id = "group")
H3K9me3_toptable_list <- bind_rows(H3K9me3_toplist, .id = "group")
K9me3_lfctable <- H3K9me3_toptable_list %>%
dplyr::select(group,genes, logFC) %>%
pivot_wider(.,id_cols = genes, names_from = group, values_from = logFC)
K27ac_lfctable <- H3K27ac_toptable_list %>%
dplyr::select(group,genes, logFC) %>%
pivot_wider(.,id_cols = genes, names_from = group, values_from = logFC)SINE
Overlapping SINE family with ROIs
sine_hits_H3K9me3 <- findOverlaps(H3K9me3_sets_gr$all_H3K9me3_regions, SINE_gr, ignore.strand = TRUE)
SINE_overlap_df_H3K9me3 <- tibble(
peak_row = queryHits(sine_hits_H3K9me3),
Peakid = H3K9me3_sets_gr$all_H3K9me3$Peakid[queryHits(sine_hits_H3K9me3)],
cluster = H3K9me3_sets_gr$all_H3K9me3$cluster[queryHits(sine_hits_H3K9me3)],
repClass = SINE_gr$repClass[subjectHits(sine_hits_H3K9me3)],
repName = SINE_gr$repName[subjectHits(sine_hits_H3K9me3)],
RM_id = SINE_gr$RM_id[subjectHits(sine_hits_H3K9me3)],
TE_type = ifelse(
SINE_gr$repFamily[subjectHits(sine_hits_H3K9me3)] == "SVA",
SINE_gr$repName[subjectHits(sine_hits_H3K9me3)],
SINE_gr$repFamily[subjectHits(sine_hits_H3K9me3)]
),
milliDiv = SINE_gr$milliDiv[subjectHits(sine_hits_H3K9me3)],
milliDel = SINE_gr$milliDel[subjectHits(sine_hits_H3K9me3)],
milliIns = SINE_gr$milliIns[subjectHits(sine_hits_H3K9me3)]
)
sine_hits_H3K27ac <- findOverlaps(H3K27ac_sets_gr$all_H3K27ac, SINE_gr, ignore.strand = TRUE)
SINE_overlap_df_H3K27ac <- tibble(
peak_row = queryHits(sine_hits_H3K27ac),
Peakid = H3K27ac_sets_gr$all_H3K27ac$Peakid[queryHits(sine_hits_H3K27ac)],
cluster = H3K27ac_sets_gr$all_H3K27ac$cluster[queryHits(sine_hits_H3K27ac)],
repClass = SINE_gr$repClass[subjectHits(sine_hits_H3K27ac)],
repName = SINE_gr$repName[subjectHits(sine_hits_H3K27ac)],
RM_id = SINE_gr$RM_id[subjectHits(sine_hits_H3K27ac)],
TE_type = ifelse(
SINE_gr$repFamily[subjectHits(sine_hits_H3K27ac)] == "SVA",
SINE_gr$repName[subjectHits(sine_hits_H3K27ac)],
SINE_gr$repFamily[subjectHits(sine_hits_H3K27ac)]
),
milliDiv = SINE_gr$milliDiv[subjectHits(sine_hits_H3K27ac)],
milliDel = SINE_gr$milliDel[subjectHits(sine_hits_H3K27ac)],
milliIns = SINE_gr$milliIns[subjectHits(sine_hits_H3K27ac)]
)
common_sine_RMs <- intersect(
SINE_overlap_df_H3K27ac$RM_id,
SINE_overlap_df_H3K9me3$RM_id
)
length(unique(common_sine_RMs))[1] 7675Visualizing the overlapping SINE families
H3K27ac_SINE_peakid <- SINE_overlap_df_H3K27ac %>%
dplyr::filter(RM_id %in% common_sine_RMs) %>%
left_join(., H3K27ac_lookup) %>%
distinct(Peakid)
H3K9me3_SINE_peakid <- SINE_overlap_df_H3K9me3 %>%
dplyr::filter(RM_id %in% common_sine_RMs) %>%
left_join(., H3K9me3_lookup) %>%
distinct(Peakid)I struggled with figuring out whether a TE is unique to a peak. I do have some SINE elements mapping to multiple peaks, but not a whole lot.
SINE_overlap_df_H3K9me3 %>%
count(RM_id, name = "n_peaks") %>%
count(n_peaks)# A tibble: 2 × 2
n_peaks n
<int> <int>
1 1 50658
2 2 361SINE_overlap_df_H3K9me3 %>%
count(RM_id) %>%
summarise(
min = min(n),
median = median(n),
mean = mean(n),
max = max(n)
)# A tibble: 1 × 4
min median mean max
<int> <int> <dbl> <int>
1 1 1 1.01 2SINE_overlap_df_H3K27ac %>%
count(RM_id, name = "n_peaks") %>%
count(n_peaks)# A tibble: 2 × 2
n_peaks n
<int> <int>
1 1 149643
2 2 1230SINE_overlap_df_H3K27ac %>%
count(RM_id) %>%
summarise(
min = min(n),
median = median(n),
mean = mean(n),
max = max(n)
)# A tibble: 1 × 4
min median mean max
<int> <int> <dbl> <int>
1 1 1 1.01 2Because of this many to many situation, I chose to average the log fold changes of the SINE elements overlapping multiple peaks.
### linking shared RM_id to Peakid by histone ROI for LFC plotting
##
SINE_K27ac_rmid_lfc <- data_frame(RM_id=common_sine_RMs) %>%
left_join(.,(SINE_overlap_df_H3K27ac %>%
dplyr::filter(RM_id %in% common_sine_RMs) %>%
distinct(RM_id,Peakid, .keep_all = TRUE) %>%
mutate(K27ac_Peakid=Peakid) %>%
dplyr::select(RM_id, K27ac_Peakid))) %>%
left_join(K27ac_lfctable, by =c("K27ac_Peakid"="genes")) %>%
group_by(RM_id) %>%
summarize(K27ac_24T=mean(H3K27ac_24T, na.rm=TRUE),
K27ac_24R=mean(H3K27ac_24R,na.rm=TRUE),
K27ac_144R=mean(H3K27ac_144R, na.rm=TRUE),
n_K27_peaks = dplyr::n(),
.groups="drop")
SINE_K9me3_rmid_lfc <- data_frame(RM_id=common_sine_RMs) %>%
left_join(.,(SINE_overlap_df_H3K9me3 %>%
dplyr::filter(RM_id %in% common_sine_RMs) %>%
distinct(RM_id,Peakid, .keep_all = TRUE) %>%
mutate(K9me3_Peakid=Peakid) %>%
dplyr::select(RM_id, K9me3_Peakid))) %>%
left_join(K9me3_lfctable, by =c("K9me3_Peakid"="genes")) %>%
group_by(RM_id) %>%
summarize(K9me3_24T=mean(H3K9me3_24T, na.rm=TRUE),
K9me3_24R=mean(H3K9me3_24R,na.rm=TRUE),
K9me3_144R=mean(H3K9me3_144R, na.rm=TRUE),
n_K27_peaks = dplyr::n(),
.groups="drop")
SINE_RM_lfc_concordance <- tibble(RM_id = common_sine_RMs) %>%
left_join(SINE_K27ac_rmid_lfc, by = "RM_id") %>%
left_join(SINE_K9me3_rmid_lfc, by = "RM_id")Now to try to visualize this concordance. I am specifically looking at TEs that up in one and down in another.
SINE_RM_lfc_concordance %>%
ggplot(., aes(x=K27ac_24T, y=K9me3_24T))+
geom_point(, alpha=0.2, size=1)+
geom_density_2d(color= "black")+
sm_statCorr(corr_method="spearman")+
geom_hline(yintercept = 0, linetype = "dashed", color="red") +
geom_vline(xintercept = 0, linetype = "dashed", color="red") +
labs(
x = "H3K27ac logFC (24T)",
y = "H3K9me3 logFC (24T)",
title = "Concordance of chromatin changes at shared SINEs for 24T"
) +
theme_classic()
| Version | Author | Date |
|---|---|---|
| 97aa671 | reneeisnowhere | 2026-01-30 |
SINE_RM_lfc_concordance %>%
ggplot(., aes(x=K27ac_24R, y=K9me3_24R))+
geom_point(, alpha=0.2, size=1)+
geom_density_2d(color= "black")+
sm_statCorr(corr_method="spearman")+
geom_hline(yintercept = 0, linetype = "dashed", color="red") +
geom_vline(xintercept = 0, linetype = "dashed", color="red") +
labs(
x = "H3K27ac logFC (24R)",
y = "H3K9me3 logFC (24R)",
title = "Concordance of chromatin changes at shared SINEs for 24R"
) +
theme_classic()
| Version | Author | Date |
|---|---|---|
| 97aa671 | reneeisnowhere | 2026-01-30 |
SINE_RM_lfc_concordance %>%
ggplot(., aes(x=K27ac_144R, y=K9me3_144R))+
geom_point(, alpha=0.2, size=1)+
geom_density_2d(color= "black")+
sm_statCorr(corr_method="spearman")+
geom_hline(yintercept = 0, linetype = "dashed", color="red") +
geom_vline(xintercept = 0, linetype = "dashed", color="red") +
labs(
x = "H3K27ac logFC (144R)",
y = "H3K9me3 logFC (144R)",
title = "Concordance of chromatin changes at shared SINEs for 144R"
) +
theme_classic()
| Version | Author | Date |
|---|---|---|
| 97aa671 | reneeisnowhere | 2026-01-30 |
LTR
Overlapping LTR family with ROIs
LTR_hits_H3K9me3 <- findOverlaps(H3K9me3_sets_gr$all_H3K9me3_regions, LTR_gr, ignore.strand = TRUE)
LTR_overlap_df_H3K9me3 <- tibble(
peak_row = queryHits(LTR_hits_H3K9me3),
Peakid = H3K9me3_sets_gr$all_H3K9me3$Peakid[queryHits(LTR_hits_H3K9me3)],
cluster = H3K9me3_sets_gr$all_H3K9me3$cluster[queryHits(LTR_hits_H3K9me3)],
repClass = LTR_gr$repClass[subjectHits(LTR_hits_H3K9me3)],
repName = LTR_gr$repName[subjectHits(LTR_hits_H3K9me3)],
RM_id = LTR_gr$RM_id[subjectHits(LTR_hits_H3K9me3)],
TE_type = ifelse(
LTR_gr$repFamily[subjectHits(LTR_hits_H3K9me3)] == "SVA",
LTR_gr$repName[subjectHits(LTR_hits_H3K9me3)],
LTR_gr$repFamily[subjectHits(LTR_hits_H3K9me3)]
),
milliDiv = LTR_gr$milliDiv[subjectHits(LTR_hits_H3K9me3)],
milliDel = LTR_gr$milliDel[subjectHits(LTR_hits_H3K9me3)],
milliIns = LTR_gr$milliIns[subjectHits(LTR_hits_H3K9me3)]
)
LTR_hits_H3K27ac <- findOverlaps(H3K27ac_sets_gr$all_H3K27ac, LTR_gr, ignore.strand = TRUE)
LTR_overlap_df_H3K27ac <- tibble(
peak_row = queryHits(LTR_hits_H3K27ac),
Peakid = H3K27ac_sets_gr$all_H3K27ac$Peakid[queryHits(LTR_hits_H3K27ac)],
cluster = H3K27ac_sets_gr$all_H3K27ac$cluster[queryHits(LTR_hits_H3K27ac)],
repClass = LTR_gr$repClass[subjectHits(LTR_hits_H3K27ac)],
repName = LTR_gr$repName[subjectHits(LTR_hits_H3K27ac)],
RM_id = LTR_gr$RM_id[subjectHits(LTR_hits_H3K27ac)],
TE_type = ifelse(
LTR_gr$repFamily[subjectHits(LTR_hits_H3K27ac)] == "SVA",
LTR_gr$repName[subjectHits(LTR_hits_H3K27ac)],
LTR_gr$repFamily[subjectHits(LTR_hits_H3K27ac)]
),
milliDiv = LTR_gr$milliDiv[subjectHits(LTR_hits_H3K27ac)],
milliDel = LTR_gr$milliDel[subjectHits(LTR_hits_H3K27ac)],
milliIns = LTR_gr$milliIns[subjectHits(LTR_hits_H3K27ac)]
)
common_LTR_RMs <- intersect(
LTR_overlap_df_H3K27ac$RM_id,
LTR_overlap_df_H3K9me3$RM_id
)
length(unique(common_LTR_RMs))[1] 5947Visualizing the overlapping LTR families
H3K27ac_LTR_peakid <- LTR_overlap_df_H3K27ac %>%
dplyr::filter(RM_id %in% common_LTR_RMs) %>%
left_join(., H3K27ac_lookup) %>%
distinct(Peakid)
H3K9me3_LTR_peakid <- LTR_overlap_df_H3K9me3 %>%
dplyr::filter(RM_id %in% common_LTR_RMs) %>%
left_join(., H3K9me3_lookup) %>%
distinct(Peakid)I struggled with figuring out whether a TE is unique to a peak. I do have some LTR elements mapping to multiple peaks, and more than SINEs most likely due to the increased TE lengths.
LTR_overlap_df_H3K9me3 %>%
count(RM_id, name = "n_peaks") %>%
count(n_peaks)# A tibble: 8 × 2
n_peaks n
<int> <int>
1 1 74479
2 2 4627
3 3 458
4 4 133
5 5 35
6 6 21
7 7 3
8 8 2LTR_overlap_df_H3K9me3 %>%
count(RM_id) %>%
summarise(
min = min(n),
median = median(n),
mean = mean(n),
max = max(n)
)# A tibble: 1 × 4
min median mean max
<int> <dbl> <dbl> <int>
1 1 1 1.08 8LTR_overlap_df_H3K27ac %>%
count(RM_id, name = "n_peaks") %>%
count(n_peaks)# A tibble: 4 × 2
n_peaks n
<int> <int>
1 1 35045
2 2 421
3 3 13
4 4 4LTR_overlap_df_H3K27ac %>%
count(RM_id) %>%
summarise(
min = min(n),
median = median(n),
mean = mean(n),
max = max(n)
)# A tibble: 1 × 4
min median mean max
<int> <int> <dbl> <int>
1 1 1 1.01 4Because of this many to many situation, I chose to average the log fold changes of the LTR elements overlapping multiple peaks.
### linking shared RM_id to Peakid by histone ROI for LFC plotting
##
K27ac_rmid_lfc <- data_frame(RM_id=common_LTR_RMs) %>%
left_join(.,(LTR_overlap_df_H3K27ac %>%
dplyr::filter(RM_id %in% common_LTR_RMs) %>%
distinct(RM_id,Peakid, .keep_all = TRUE))) %>%
mutate(K27ac_Peakid=Peakid) %>%
dplyr::select(RM_id, K27ac_Peakid) %>%
left_join(K27ac_lfctable, by =c("K27ac_Peakid"="genes")) %>%
left_join(., H3K27ac_lookup, by =c("K27ac_Peakid"="Peakid")) %>%
group_by(RM_id) %>%
summarize(K27ac_24T=mean(H3K27ac_24T, na.rm=TRUE),
K27ac_24R=mean(H3K27ac_24R,na.rm=TRUE),
K27ac_144R=mean(H3K27ac_144R, na.rm=TRUE),
n_K27ac_peaks = dplyr::n(),
cluster=dplyr::first(cluster),
.groups="drop") %>%
tidyr::replace_na(list(cluster = "NA"))
K9me3_rmid_lfc <- data_frame(RM_id=common_LTR_RMs) %>%
left_join(.,(LTR_overlap_df_H3K9me3 %>%
dplyr::filter(RM_id %in% common_LTR_RMs) %>%
distinct(RM_id,Peakid, .keep_all = TRUE) %>%
mutate(K9me3_Peakid=Peakid) %>%
dplyr::select(RM_id, K9me3_Peakid))) %>%
left_join(K9me3_lfctable, by =c("K9me3_Peakid"="genes")) %>%
group_by(RM_id) %>%
summarize(K9me3_24T=mean(H3K9me3_24T, na.rm=TRUE),
K9me3_24R=mean(H3K9me3_24R,na.rm=TRUE),
K9me3_144R=mean(H3K9me3_144R, na.rm=TRUE),
n_K9me3_peaks = dplyr::n(),
.groups="drop")
LTR_RM_lfc_concordance <- tibble(RM_id = common_LTR_RMs) %>%
left_join(K27ac_rmid_lfc, by = "RM_id") %>%
left_join(K9me3_rmid_lfc, by = "RM_id")Now to try to visualize this concordance. I am specifically looking at TEs that up in one and down in another.
LTR_RM_lfc_concordance %>%
ggplot(., aes(x=K27ac_24T, y=K9me3_24T, color=cluster))+
geom_point(, alpha=0.2, size=2)+
geom_density_2d(color= "black")+
# sm_statCorr(corr_method="spearman")+
geom_hline(yintercept = 0, linetype = "dashed", color="red") +
geom_vline(xintercept = 0, linetype = "dashed", color="red") +
labs(
x = "H3K27ac logFC (24T)",
y = "H3K9me3 logFC (24T)",
title = "Concordance of chromatin changes at shared LTRs for 24T"
) +
theme_classic()
| Version | Author | Date |
|---|---|---|
| 97aa671 | reneeisnowhere | 2026-01-30 |
LTR_RM_lfc_concordance %>%
ggplot(., aes(x=K27ac_24R, y=K9me3_24R, color=cluster))+
geom_point(, alpha=0.2, size=2)+
geom_density_2d(color= "black")+
# sm_statCorr(corr_method="spearman")+
geom_hline(yintercept = 0, linetype = "dashed", color="red") +
geom_vline(xintercept = 0, linetype = "dashed", color="red") +
labs(
x = "H3K27ac logFC (24R)",
y = "H3K9me3 logFC (24R)",
title = "Concordance of chromatin changes at shared LTRs for 24R"
) +
theme_classic()
| Version | Author | Date |
|---|---|---|
| 97aa671 | reneeisnowhere | 2026-01-30 |
LTR_RM_lfc_concordance %>%
ggplot(., aes(x=K27ac_144R, y=K9me3_144R, color=cluster))+
geom_point(, alpha=0.2, size=2)+
geom_density_2d(color= "black")+
# sm_statCorr(corr_method="spearman")+
geom_hline(yintercept = 0, linetype = "dashed", color="red") +
geom_vline(xintercept = 0, linetype = "dashed", color="red") +
labs(
x = "H3K27ac logFC (144R)",
y = "H3K9me3 logFC (144R)",
title = "Concordance of chromatin changes at shared LTRs for 144R"
) +
theme_classic()
| Version | Author | Date |
|---|---|---|
| 97aa671 | reneeisnowhere | 2026-01-30 |
LTR_RM_lfc_concordance %>%
ggplot(., aes(x=K27ac_24T, y=K27ac_24R, color=cluster))+
geom_point(, alpha=0.2, size=2)+
geom_density_2d(color= "black")+
# sm_statCorr(corr_method="spearman")+
geom_hline(yintercept = 0, linetype = "dashed", color="red") +
geom_vline(xintercept = 0, linetype = "dashed", color="red") +
labs(
x = "H3K27ac logFC (24T)",
y = "H3K9me3 logFC (24T)",
title = "Concordance of chromatin changes at shared LTRs for 24T v 24R H3K27ac"
) +
theme_classic()
LTR_RM_lfc_concordance %>%
ggplot(., aes(x=K27ac_24T, y=K27ac_144R, color=cluster))+
geom_point(, alpha=0.2, size=2)+
geom_density_2d(color= "black")+
# sm_statCorr(corr_method="spearman")+
geom_hline(yintercept = 0, linetype = "dashed", color="red") +
geom_vline(xintercept = 0, linetype = "dashed", color="red") +
labs(
x = "H3K27ac logFC (24T)",
y = "H3K9me3 logFC (24T)",
title = "Concordance of chromatin changes at shared LTRs for 24T v 144R H3K27ac"
) +
theme_classic()
LTR_RM_lfc_concordance %>%
ggplot(., aes(x=K27ac_24R, y=K27ac_144R, color=cluster))+
geom_point(, alpha=0.2, size=2)+
geom_density_2d(color= "black")+
# sm_statCorr(corr_method="spearman")+
geom_hline(yintercept = 0, linetype = "dashed", color="red") +
geom_vline(xintercept = 0, linetype = "dashed", color="red") +
labs(
x = "H3K27ac logFC (24T)",
y = "H3K9me3 logFC (24T)",
title = "Concordance of chromatin changes at shared LTRs for 24R v 144R H3K27ac"
) +
theme_classic()
K27ac_unfiltered_long <- K27ac_lfctable %>%
left_join(H3K27ac_lookup, by = c("genes" = "Peakid")) %>%
tidyr::replace_na(list(cluster = "NA")) %>%
dplyr::filter(cluster != "NA") %>%
pivot_longer(
cols = c("H3K27ac_24T", "H3K27ac_24R", "H3K27ac_144R"),
names_to = "group",
values_to = "LFC") %>%
mutate(group = factor(group, levels = c("H3K27ac_24T", "H3K27ac_24R", "H3K27ac_144R")))
K9me3_unfiltered_long <- K9me3_lfctable %>%
left_join(H3K9me3_lookup, by = c("genes" = "Peakid")) %>%
tidyr::replace_na(list(cluster = "NA")) %>%
dplyr::filter(cluster != "NA") %>%
pivot_longer(
cols = c("H3K9me3_24T", "H3K9me3_24R", "H3K9me3_144R"),
names_to = "group",
values_to = "LFC") %>%
mutate(group = factor(group, levels = c("H3K9me3_24T", "H3K9me3_24R", "H3K9me3_144R")))
K27ac_median_unfilt_lfc <- K27ac_unfiltered_long %>%
group_by(cluster, group) %>%
summarise(median_abs_LFC = median(abs(LFC), na.rm = TRUE),
.groups = "drop")%>%
mutate(time=str_remove(group,"H3K27ac"))%>%
mutate(time=factor(time, levels=c("_24T","_24R","_144R")))
K9me3_median_unfilt_lfc <- K9me3_unfiltered_long %>%
group_by(cluster, group) %>%
summarise(median_abs_LFC = median(abs(LFC), na.rm = TRUE),
.groups = "drop")%>%
mutate(time=str_remove(group,"H3K9me3")) %>%
mutate(time=factor(time, levels=c("_24T","_24R","_144R")))
K9me3_median_unfilt_lfc %>%
ggplot(., aes(x=group, y=median_abs_LFC, group=cluster, color=cluster))+
geom_point(size=4)+
geom_line(aes(alpha = 0.8, linewidth = 4))+
theme_bw()+
ggtitle("LFC across time for H3K27ac sets")
| Version | Author | Date |
|---|---|---|
| 97aa671 | reneeisnowhere | 2026-01-30 |
K27ac_median_LTR_lfc <- K27ac_unfiltered_long %>%
dplyr::filter(genes %in% H3K27ac_LTR_peakid$Peakid) %>%
group_by(cluster, group) %>%
summarise(median_abs_LFC = median(abs(LFC), na.rm = TRUE),
.groups = "drop") %>%
mutate(time=str_remove(group,"H3K27ac"))%>%
mutate(time=factor(time, levels=c("_24T","_24R","_144R")))
K9me3_median_LTR_lfc <- K9me3_unfiltered_long %>%
dplyr::filter(genes %in% H3K9me3_LTR_peakid$Peakid) %>%
group_by(cluster, group) %>%
summarise(median_abs_LFC = median(abs(LFC), na.rm = TRUE),
.groups = "drop") %>%
mutate(time=str_remove(group,"H3K9me3"))%>%
mutate(time=factor(time, levels=c("_24T","_24R","_144R")))
K9me3_median_unfilt_lfc %>%
ggplot(., aes(x=time, y=median_abs_LFC, group=cluster, color=cluster))+
geom_point(size=4)+
geom_line(aes(alpha = 0.8, linewidth = 4))+
geom_line(data=K27ac_median_unfilt_lfc, aes(x=time,y=median_abs_LFC, group=cluster,color=cluster), linetype=2, size = 1)+
geom_point(data=K27ac_median_unfilt_lfc, aes(x=time,y=median_abs_LFC, group=cluster,color=cluster), linetype=2, size = 1)+
theme_bw()+
ggtitle("H3K9me3 unfiltered solid line\nH3K27ac dotted line absolute LFC All regions")
| Version | Author | Date |
|---|---|---|
| 97aa671 | reneeisnowhere | 2026-01-30 |
K9me3_median_LTR_lfc %>%
ggplot(., aes(x=time, y=median_abs_LFC, group=cluster, color=cluster))+
geom_point(size=4)+
geom_line(aes(alpha = 0.8, linewidth = 4))+
geom_line(data=K27ac_median_LTR_lfc, aes(x=time,y=median_abs_LFC, group=cluster,color=cluster), linetype=2, size = 1)+
geom_point(data=K27ac_median_LTR_lfc, aes(x=time,y=median_abs_LFC, group=cluster,color=cluster), linetype=2, size = 1)+
theme_bw()+
ggtitle("H3K9me3 solid line\nH3K27ac dotted line absolute LFC overlapping LTR regions")
| Version | Author | Date |
|---|---|---|
| 97aa671 | reneeisnowhere | 2026-01-30 |
Now I am trying to break down by LTR families:
LTR_overlap_df_H3K27ac %>%
group_by(TE_type) %>%
tally() %>%
ungroup() %>%
DT::datatable(
rownames = FALSE,
caption = htmltools::tags$caption(
style = "caption-side: top; text-align: left;",
"H3K27ac LTR specific overlap counts by TE familiy"),
options = list(pageLength = 10,
autoWidth = TRUE,
dom = "tip"))# now I want to breakup common_LTR_RMs and plot by family of LTR.
K27_family_plot <- function(lfc_df,
split_list,
grp="K27ac",
repFamily,
repName){
rm_ids <- split_list[[repName]]$RM_id
lfc_df %>%
dplyr::filter(RM_id %in% rm_ids) %>%
dplyr::filter(cluster != "NA") %>%
pivot_longer(.,cols=c(starts_with(grp)), names_to="group", values_to = "LFC") %>%
group_by(cluster, group) %>%
summarise(median_abs_LFC = median(abs(LFC), na.rm = TRUE),
.groups = "drop")%>%
mutate(time=str_remove(group,grp))%>%
mutate(time=factor(time, levels=c("_24T","_24R","_144R"))) %>%
ggplot(., aes(x=time, y=median_abs_LFC, group=cluster, color=cluster))+
geom_point(size=4)+
geom_line(aes(alpha = 0.8, linewidth = 4))+
theme_bw()+
ggtitle(paste0(grp," absolute LFC overlapping ",repFamily,":",repName))
}names(LTR_split_df)[1] "ERV1" "ERVK" "ERVL" "ERVL-MaLR" "Gypsy" "LTR" K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","ERV1")
K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","ERVK")
K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","ERVL")
K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","ERVL-MaLR")
K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","Gypsy")
K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","LTR")
K27_K9_family_plot <- function(lfc_df,
split_list,
marks=c("K27ac","K9me3"),
repClass,
repFam){
rm_ids <- split_list[[repFam]]$RM_id
lfc_df %>%
dplyr::filter(RM_id %in% rm_ids,
cluster != "NA") %>%
pivot_longer(.,cols=tidyselect::matches(paste0("^(",paste(marks,collapse = "|"), ")")),
names_to="group",
values_to = "LFC") %>%
mutate(mark = stringr::str_extract(group, paste(marks, collapse="|")),
time = stringr::str_remove(group, paste(marks, collapse="|")),
time = factor(time, levels = c("_24T", "_24R", "_144R"))) %>%
group_by(cluster, mark ,time) %>%
summarise(median_abs_LFC = median(abs(LFC), na.rm = TRUE),
.groups = "drop")%>%
ggplot(., aes(x=time, y=median_abs_LFC, group=interaction(cluster,mark), color=cluster, linetype=mark, shape=mark))+
geom_point(size=4)+
geom_line(aes(alpha = 0.8, linewidth = 4))+
theme_bw()+
labs(title = paste0("Both histones, absolute LFC overlapping ",repClass,":",repFam),
y = "Median |LFC|",
x = "Time"
)
}K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"), "LTR",repFam="ERVL")
K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"), "LTR",repFam="ERVK")
K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"), "LTR",repFam="ERV1")
K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"), "LTR",repFam="ERVL-MaLR")
K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"), "LTR",repFam="Gypsy")
K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"), "LTR",repFam="LTR")
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] smplot2_0.2.5 ggVennDiagram_1.5.4 ChIPseeker_1.42.1
[4] DT_0.33 ggrepel_0.9.6 rtracklayer_1.66.0
[7] genomation_1.38.0 plyranges_1.26.0 GenomicRanges_1.58.0
[10] GenomeInfoDb_1.42.3 IRanges_2.40.1 S4Vectors_0.44.0
[13] BiocGenerics_0.52.0 lubridate_1.9.4 forcats_1.0.0
[16] stringr_1.5.1 dplyr_1.1.4 purrr_1.1.0
[19] readr_2.1.5 tidyr_1.3.1 tibble_3.3.0
[22] ggplot2_3.5.2 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.4.2
[2] later_1.4.2
[3] BiocIO_1.16.0
[4] bitops_1.0-9
[5] ggplotify_0.1.2
[6] R.oo_1.27.1
[7] XML_3.99-0.18
[8] rpart_4.1.24
[9] lifecycle_1.0.4
[10] rstatix_0.7.2
[11] rprojroot_2.1.1
[12] MASS_7.3-65
[13] vroom_1.6.5
[14] processx_3.8.6
[15] lattice_0.22-7
[16] crosstalk_1.2.2
[17] backports_1.5.0
[18] magrittr_2.0.3
[19] Hmisc_5.2-3
[20] sass_0.4.10
[21] rmarkdown_2.29
[22] jquerylib_0.1.4
[23] yaml_2.3.10
[24] plotrix_3.8-4
[25] httpuv_1.6.16
[26] ggtangle_0.0.7
[27] cowplot_1.2.0
[28] DBI_1.2.3
[29] RColorBrewer_1.1-3
[30] abind_1.4-8
[31] zlibbioc_1.52.0
[32] R.utils_2.13.0
[33] RCurl_1.98-1.17
[34] yulab.utils_0.2.1
[35] nnet_7.3-20
[36] rappdirs_0.3.3
[37] git2r_0.36.2
[38] GenomeInfoDbData_1.2.13
[39] enrichplot_1.26.6
[40] tidytree_0.4.6
[41] codetools_0.2-20
[42] DelayedArray_0.32.0
[43] DOSE_4.0.1
[44] tidyselect_1.2.1
[45] aplot_0.2.8
[46] UCSC.utils_1.2.0
[47] farver_2.1.2
[48] matrixStats_1.5.0
[49] base64enc_0.1-3
[50] GenomicAlignments_1.42.0
[51] jsonlite_2.0.0
[52] Formula_1.2-5
[53] tools_4.4.2
[54] treeio_1.30.0
[55] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[56] Rcpp_1.1.0
[57] glue_1.8.0
[58] gridExtra_2.3
[59] SparseArray_1.6.2
[60] mgcv_1.9-3
[61] xfun_0.52
[62] qvalue_2.38.0
[63] MatrixGenerics_1.18.1
[64] withr_3.0.2
[65] fastmap_1.2.0
[66] boot_1.3-32
[67] callr_3.7.6
[68] caTools_1.18.3
[69] digest_0.6.37
[70] timechange_0.3.0
[71] R6_2.6.1
[72] gridGraphics_0.5-1
[73] seqPattern_1.38.0
[74] colorspace_2.1-1
[75] GO.db_3.20.0
[76] gtools_3.9.5
[77] dichromat_2.0-0.1
[78] RSQLite_2.4.3
[79] R.methodsS3_1.8.2
[80] generics_0.1.4
[81] data.table_1.17.8
[82] httr_1.4.7
[83] htmlwidgets_1.6.4
[84] S4Arrays_1.6.0
[85] whisker_0.4.1
[86] pkgconfig_2.0.3
[87] gtable_0.3.6
[88] blob_1.2.4
[89] impute_1.80.0
[90] XVector_0.46.0
[91] htmltools_0.5.8.1
[92] carData_3.0-5
[93] pwr_1.3-0
[94] fgsea_1.32.4
[95] scales_1.4.0
[96] Biobase_2.66.0
[97] png_0.1-8
[98] ggfun_0.2.0
[99] knitr_1.50
[100] rstudioapi_0.17.1
[101] tzdb_0.5.0
[102] reshape2_1.4.4
[103] rjson_0.2.23
[104] checkmate_2.3.3
[105] nlme_3.1-168
[106] curl_7.0.0
[107] zoo_1.8-14
[108] cachem_1.1.0
[109] KernSmooth_2.23-26
[110] parallel_4.4.2
[111] foreign_0.8-90
[112] AnnotationDbi_1.68.0
[113] restfulr_0.0.16
[114] pillar_1.11.0
[115] vctrs_0.6.5
[116] gplots_3.2.0
[117] ggpubr_0.6.1
[118] promises_1.3.3
[119] car_3.1-3
[120] cluster_2.1.8.1
[121] htmlTable_2.4.3
[122] evaluate_1.0.5
[123] isoband_0.2.7
[124] GenomicFeatures_1.58.0
[125] cli_3.6.5
[126] compiler_4.4.2
[127] Rsamtools_2.22.0
[128] rlang_1.1.6
[129] crayon_1.5.3
[130] ggsignif_0.6.4
[131] labeling_0.4.3
[132] ps_1.9.1
[133] getPass_0.2-4
[134] plyr_1.8.9
[135] fs_1.6.6
[136] stringi_1.8.7
[137] gridBase_0.4-7
[138] BiocParallel_1.40.2
[139] Biostrings_2.74.1
[140] lazyeval_0.2.2
[141] GOSemSim_2.32.0
[142] Matrix_1.7-3
[143] BSgenome_1.74.0
[144] hms_1.1.3
[145] patchwork_1.3.2
[146] bit64_4.6.0-1
[147] KEGGREST_1.46.0
[148] SummarizedExperiment_1.36.0
[149] broom_1.0.9
[150] igraph_2.1.4
[151] memoise_2.0.1
[152] bslib_0.9.0
[153] ggtree_3.14.0
[154] fastmatch_1.1-6
[155] bit_4.6.0
[156] ape_5.8-1