TEs across histones

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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)

cluster_colors <- c("Set_1" = "#d93e40","Set_2" = "#1c9f50","Set_3" = "#3570b3","NA"  ="grey70")

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] 7675

Visualizing 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   361

SINE_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     2

SINE_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   1230

SINE_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     2

Because 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()

Past versions of "SINE K9K27ac plot-1.png"

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()

Past versions of "SINE K9K27ac plot-2.png"

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()

Past versions of "SINE K9K27ac plot-3.png"

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] 5947

Visualizing 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     2

LTR_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     8

LTR_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     4

LTR_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     4

Because 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")) %>% 
  left_join(., H3K9me3_lookup,by =c("K9me3_Peakid"="Peakid")) %>% 
  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),
            cluster=dplyr::first(cluster),
            n_K9me3_peaks = dplyr::n(),
            .groups="drop") %>% 
   tidyr::replace_na(list(cluster = "NA"))



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.x))+
  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()+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of "LTR K9K27ac plot-1.png"

Version	Author	Date
97aa671	reneeisnowhere	2026-01-30

LTR_RM_lfc_concordance %>% 
  ggplot(., aes(x=K27ac_24R, y=K9me3_24R, color=cluster.x))+
  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()+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of "LTR K9K27ac plot-2.png"

Version	Author	Date
97aa671	reneeisnowhere	2026-01-30

LTR_RM_lfc_concordance %>% 
  ggplot(., aes(x=K27ac_144R, y=K9me3_144R, color=cluster.x))+
  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()+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of "LTR K9K27ac plot-3.png"

Version	Author	Date
97aa671	reneeisnowhere	2026-01-30

LTR_RM_lfc_concordance %>% 
  ggplot(., aes(x=K27ac_24T, y=K27ac_24R, color=cluster.x))+
  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 = "H3K27ac logFC (24R)",
    title = "Concordance of chromatin changes at shared LTRs for 24T v 24R H3K27ac"
  ) +
  theme_classic()+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of "LTR K9K27ac plot-4.png"

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

LTR_RM_lfc_concordance %>% 
  ggplot(., aes(x=K27ac_24T, y=K27ac_144R, color=cluster.x))+
  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 = "H3K27ac logFC (144R)",
    title = "Concordance of chromatin changes at shared LTRs for 24T v 144R H3K27ac"
  ) +
  theme_classic()+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of "LTR K9K27ac plot-5.png"

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

LTR_RM_lfc_concordance %>% 
  ggplot(., aes(x=K27ac_24R, y=K27ac_144R, color=cluster.x))+
  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 = "H3K27ac logFC (144)",
    title = "Concordance of chromatin changes at shared LTRs for 24R v 144R H3K27ac"
  ) +
  theme_classic()+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of "LTR K9K27ac plot-6.png"

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

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 H3K9me3 sets")+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of unnamed-chunk-8-1.png

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")+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of unnamed-chunk-8-2.png

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")+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of unnamed-chunk-8-3.png

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")+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","ERVK")+
  scale_color_manual(values = cluster_colors, drop = FALSE)+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of unnamed-chunk-9-2.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","ERVL")+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of unnamed-chunk-9-3.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","ERVL-MaLR")+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of unnamed-chunk-9-4.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","Gypsy")+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of unnamed-chunk-9-5.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_family_plot(K27ac_rmid_lfc, LTR_split_df,"K27ac","LTR","LTR")+
  scale_color_manual(values = cluster_colors, drop = FALSE)

Past versions of unnamed-chunk-9-6.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_K9_family_plot <- function(lfc_df,
                            split_list, 
                            marks=c("K27ac","K9me3"),
                            time_levels = c("_24T", "_24R", "_144R"),
                            repClass,
                            repFam){

  if (!repFam %in% names(split_list)) {
  stop("repFam not found in split_list: ", repFam)}
  
  rm_ids <- split_list[[repFam]]$RM_id
  
  lfc_df %>% 
    dplyr::filter(RM_id %in% rm_ids) %>% 
    dplyr::rename(
    cluster_K27ac = tidyselect::any_of("cluster.x"),
    cluster_K9me3 = tidyselect::any_of("cluster.y")) %>% 
      tidyr::pivot_longer(
      cols = tidyselect::matches(paste0("^(", paste(marks, collapse = "|"), ")")),
      names_to = "group",
      values_to = "LFC"
    ) %>%
    dplyr::mutate(
      mark = stringr::str_extract(group, paste(marks, collapse = "|")),
      time = stringr::str_remove(group, paste(marks, collapse = "|")),
      time = factor(time, levels = time_levels),

      cluster = dplyr::case_when(
        mark == "K27ac" ~ cluster_K27ac,
        mark == "K9me3" ~ cluster_K9me3
      )
    ) %>%
    dplyr::filter(!is.na(cluster)) %>%
    dplyr::group_by(cluster, mark, time) %>%
    dplyr::summarise(
      median_abs_LFC = median(abs(LFC), na.rm = TRUE),
      n_RM = dplyr::n_distinct(RM_id),
      .groups = "drop") %>% 
  ggplot(., aes(x = time,
      y = median_abs_LFC,
      color = cluster,
      linetype = mark,
      shape = mark,
      group = interaction(cluster, mark))) +
    geom_line(linewidth = 1.1, alpha = 0.85) +
    geom_point(size = 3) +
    scale_linetype_manual(
  values = c("K9me3"  = "solid","K27ac" = "dotted"))+
    scale_color_manual(values = cluster_colors, drop = FALSE)+
    theme_bw() +
    labs(
      y = "Median |LFC|",
      x = "Time",
      title = paste0("Both histones, absolute LFC overlapping ",repClass,":",repFam))
}

K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"),time_levels = c("_24T", "_24R", "_144R"), "LTR",repFam="ERVL")

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"),time_levels = c("_24T", "_24R", "_144R"), "LTR",repFam="ERVK")

Past versions of unnamed-chunk-10-2.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"),time_levels = c("_24T", "_24R", "_144R"), "LTR",repFam="ERV1")

Past versions of unnamed-chunk-10-3.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"),time_levels = c("_24T", "_24R", "_144R"), "LTR",repFam="ERVL-MaLR")

Past versions of unnamed-chunk-10-4.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"),time_levels = c("_24T", "_24R", "_144R"), "LTR",repFam="Gypsy")

Past versions of unnamed-chunk-10-5.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

K27_K9_family_plot(LTR_RM_lfc_concordance, LTR_split_df,marks=c("K27ac","K9me3"),time_levels = c("_24T", "_24R", "_144R"), "LTR",repFam="LTR")

Past versions of unnamed-chunk-10-6.png

Version	Author	Date
29dda48	reneeisnowhere	2026-01-30

rm_id_test <- LTR_split_df$ERV1$RM_id
LTR_RM_lfc_concordance %>% 
  dplyr::filter(RM_id %in% rm_id_test) %>% 
                
    dplyr::rename("cluster_K27ac"=cluster.x,
                  "cluster_K9me3"=cluster.y) %>% 
pivot_longer(
    cols = tidyselect::matches("^(K27ac|K9me3)"),
    names_to = "group",
    values_to = "LFC"
  ) %>% 
  mutate(
    mark = stringr::str_extract(group, "K27ac|K9me3"),
    time = stringr::str_remove(group, "K27ac|K9me3"),
    time = factor(time, levels = c("_24T","_24R","_144R")),

    ## mark-aware cluster assignment
    cluster = dplyr::case_when(
      mark == "K27ac" ~ cluster_K27ac,
      mark == "K9me3" ~ cluster_K9me3
    )
  ) %>% 
  filter(!is.na(cluster)) %>%   # only drop when THAT mark has no cluster
  group_by(cluster, mark, time) %>%
  summarise(
    median_abs_LFC = median(abs(LFC), na.rm = TRUE),
    n_RM = dplyr::n_distinct(RM_id),
    .groups = "drop"
  ) %>% 
   ggplot(.,  aes(
    x = time,
    y = median_abs_LFC,
    color = cluster,
    linetype = mark,
    shape = mark,
    group = interaction(cluster, mark))) +
  geom_line(linewidth = 1.1, alpha = 0.85) +
  geom_point(size = 3) +
  theme_bw() +
  labs(
    y = "Median |LFC|",
    x = "Time",
    title = "Shared LTR:ERV1: mark-specific cluster behavior"
  )

Session information

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