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Ignored files:
    Ignored:    .Rhistory
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Rmd 8d883d6 reneeisnowhere 2026-02-12 wflow_publish("analysis/H3K27ac_cisRE.Rmd")
html cdf088c reneeisnowhere 2026-02-12 Build site.
Rmd ea3b358 reneeisnowhere 2026-02-12 wflow_publish("analysis/H3K27ac_cisRE.Rmd") 
library(tidyverse)
library(GenomicRanges)
library(plyranges)
library(genomation)
library(readr)
library(rtracklayer)
library(stringr)
library(ggrepel)
library(DT)
library(readxl)
library(ChIPseeker)
SCREEN_hg38_cCREs <- import(
  "data/Bed_exports/cisRE/GRCh38-cCREs.bed",
  format = "BED",
  extraCols = c(
    DHS_ID = "character",
    cCRE_ID = "character",
    cCRE_class = "character"
  )
)

strand(SCREEN_hg38_cCREs) <- "*"
cCRE_split_1 <- split(SCREEN_hg38_cCREs, SCREEN_hg38_cCREs$cCRE_class)
names(cCRE_split)

outdir <- "data/Bed_exports/cisRE/by_class"
dir.create(outdir, showWarnings = FALSE, recursive = TRUE)

for (cls in names(cCRE_split_1)) {
  export(
    cCRE_split_1[[cls]],
    con = file.path(outdir, paste0("SCREEN_hg38_", cls, ".bed")),
    format = "BED"
  )
}
SCREEN_heart_hg38_cCREs <- import(
  "data/Other_paper_data/SCREEN_LVHEART_F_53_ENCFF417JSF_ENCFF651XRK_ENCFF320IPT_ENCFF440RUS.bed",
  format = "BED",
  extraCols = c(
    DHS_ID = "character",
    cCRE_ID = "character",
    cCRE_class = "character"
  )
)

strand(SCREEN_heart_hg38_cCREs) <- "*"
cCRE_split <- split(SCREEN_heart_hg38_cCREs, SCREEN_heart_hg38_cCREs$cCRE_ID)
names(cCRE_split)

outdir <- "data/Bed_exports/cisRE/heart_by_class"
dir.create(outdir, showWarnings = FALSE, recursive = TRUE)

for (cls in names(cCRE_split)) {
  export(
    cCRE_split[[cls]],
    con = file.path(outdir, paste0("SCREEN_heartLV_", cls, ".bed")),
    format = "BED"
  )
}

H3K27ac set enrichment

SCREEN_hg38_cCREs <- import(
  "data/Bed_exports/cisRE/GRCh38-cCREs.bed",
  format = "BED",
  extraCols = c(
    DHS_ID = "character",
    cCRE_ID = "character",
    cCRE_class = "character"))

H3K27ac_anno_ROIs <- readRDS("data/motif_lists/H3K27ac_annotated_peaks.RDS")
H3K27ac_toplist <- readRDS( "data/DER_data/H3K27ac_toplist.RDS")
H3K27ac_toptable_list <- bind_rows(H3K27ac_toplist, .id = "group")
## Loading single summit locations and adding in 
H3K27ac_summit_gr <- readRDS("data/RDS_files/H3K27ac_complete_summit_gr.RDS")

##assigning Peakid as name of summit region
 mcols(H3K27ac_summit_gr)$name <- mcols(H3K27ac_summit_gr)$Peakid
 

## Creating a lookup file if needed
H3K27ac_lookup <- imap_dfr(H3K27ac_anno_ROIs[1:3], ~
  tibble(Peakid = .x@anno$Peakid, cluster = .y)
)
# cRE_color_palette <- c("#E64B35FF", "#4DBBD5FF", "#00A087FF" ,"#3C5488FF", "#F39B7FFF", "#8491B4FF", "#91D1C2FF", "#DC0000FF", "#7E6148FF", "#B09C85FF" )
# 
# 
# cCRE_discrete_colors <- paletteer_d("ggsci::default_aaas")
H3K27ac_cre_hits <- findOverlaps(H3K27ac_summit_gr, SCREEN_hg38_cCREs, ignore.strand = TRUE)

H3K27ac_cre_df <- tibble(
  Peakid=mcols(H3K27ac_summit_gr)$Peakid[queryHits(H3K27ac_cre_hits)],
  cluster   = mcols(H3K27ac_summit_gr)$cluster[queryHits(H3K27ac_cre_hits)],
  cCRE_ID   = mcols(SCREEN_hg38_cCREs)$cCRE_ID[subjectHits(H3K27ac_cre_hits)],
  cCRE_class= mcols(SCREEN_hg38_cCREs)$cCRE_class[subjectHits(H3K27ac_cre_hits)])
scale_fill_cCREs <-  function(...){
  ggplot2:::manual_scale(
    'fill', 
    values = setNames(c( "#E64B35FF", "#4DBBD5FF", "#00A087FF" ,"#3C5488FF", "#F39B7FFF", "#8491B4FF", "#91D1C2FF", "#DC0000FF", "#7E6148FF", "#B09C85FF" ), c(unique(H3K27ac_cre_df$cCRE_class),"NA")), 
    ...
  )
}
H3K27ac_cre_anno <- H3K27ac_anno_ROIs$all_H3K27ac %>% as.data.frame() %>% 
  left_join(.,H3K27ac_cre_df ) %>% 
  dplyr::select(Peakid,cluster, cCRE_ID,cCRE_class) %>% 
  mutate(cluster = if_else(is.na(cluster), "not_assigned", cluster), 
         cCRE_ID=if_else(is.na(cCRE_ID),"NA",cCRE_ID),
         cCRE_class=if_else(is.na(cCRE_class),"NA",cCRE_class))

H3K27ac_cre_anno_full<- H3K27ac_cre_anno %>% rbind(H3K27ac_cre_anno %>% mutate(cluster="all_ROIs"))

H3K27ac_cre_counts <- H3K27ac_cre_anno_full %>% 
  count(cluster, cCRE_class) %>% 
  group_by(cluster) %>% 
  mutate(percent= n/sum(n)) %>% 
  dplyr::filter(cluster != "not_assigned")

 ggplot(H3K27ac_cre_counts, aes(x = cluster, y = percent, fill = cCRE_class)) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  labs(
    y = "Proportion of ROIs",
    x = "Set",
    fill = "cCRE class"
  ) +
  theme_bw()+
     ggtitle("H3K27ac ROI summits crossing :hg38 all cCREs ")+scale_fill_cCREs()

Version Author Date
cdf088c reneeisnowhere 2026-02-12 
SCREEN_heart_hg38_cCREs <- import(
  "data/Other_paper_data/SCREEN_LVHEART_F_53_ENCFF417JSF_ENCFF651XRK_ENCFF320IPT_ENCFF440RUS.bed",
  format = "BED",
  extraCols = c(
    DHS_ID = "character",
    cCRE_ID = "character",
    cCRE_class = "character"))
SCREEN_heart_hg38_cCREs %>% as.data.frame() %>% distinct(cCRE_ID)
     cCRE_ID
1  Low-DNase
2        PLS
3    CA-CTCF
4    CA-only
5       pELS
6       dELS
7 CA-H3K4me3
H3K27ac_heart_cre_hits <- findOverlaps(H3K27ac_summit_gr, SCREEN_heart_hg38_cCREs, ignore.strand = TRUE)

H3K27ac_heart_cre_df <- tibble(
  Peakid=mcols(H3K27ac_summit_gr)$Peakid[queryHits(H3K27ac_heart_cre_hits)],
  cluster   = mcols(H3K27ac_summit_gr)$cluster[queryHits(H3K27ac_heart_cre_hits)],
  cCRE_ID   = mcols(SCREEN_hg38_cCREs)$cCRE_ID[subjectHits(H3K27ac_heart_cre_hits)],
  cCRE_class= mcols(SCREEN_hg38_cCREs)$cCRE_class[subjectHits(H3K27ac_heart_cre_hits)])

H3K27ac_heart_cre_anno <- H3K27ac_anno_ROIs$all_H3K27ac %>% as.data.frame() %>% 
  left_join(.,H3K27ac_heart_cre_df ) %>% 
  dplyr::select(Peakid,cluster, cCRE_ID,cCRE_class) %>% 
  mutate(cluster = if_else(is.na(cluster), "not_assigned", cluster), 
         cCRE_ID=if_else(is.na(cCRE_ID),"NA",cCRE_ID),
         cCRE_class=if_else(is.na(cCRE_class),"NA",cCRE_class))
H3K27ac_heart_cre_counts <- H3K27ac_heart_cre_anno %>% 
  count(cluster, cCRE_class) %>% 
  group_by(cluster) %>% 
  mutate(percent= n/sum(n))

 ggplot(H3K27ac_heart_cre_counts, aes(x = cluster, y = percent, fill = cCRE_class)) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  labs(
    yy = "Proportion of ROIs",
    x = "Set",
    fill = "cCRE class"
  ) +
  theme_classic()+
     ggtitle("Heart-left ventricle cCREs")+scale_fill_cCREs()

Version Author Date
cdf088c reneeisnowhere 2026-02-12 
# all.equal(SCREEN_heart_hg38_cCREs, SCREEN_hg38_cCREs)
# 
# identical(
#   dplyr::arrange(SCREEN_heart_hg38_cCREs, across(everything())),
#   dplyr::arrange(SCREEN_hg38_cCREs, across(everything()))
# )
# 
# dplyr::left_join((SCREEN_heart_hg38_cCREs %>% as.data.frame()),(SCREEN_hg38_cCREs %>% as.data.frame())                 )

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    readxl_1.4.5         DT_0.33             
 [4] ggrepel_0.9.6        rtracklayer_1.66.0   genomation_1.38.0   
 [7] plyranges_1.26.0     GenomicRanges_1.58.0 GenomeInfoDb_1.42.3 
[10] IRanges_2.40.1       S4Vectors_0.44.0     BiocGenerics_0.52.0 
[13] lubridate_1.9.4      forcats_1.0.0        stringr_1.5.1       
[16] dplyr_1.1.4          purrr_1.1.0          readr_2.1.5         
[19] tidyr_1.3.1          tibble_3.3.0         ggplot2_3.5.2       
[22] tidyverse_2.0.0      workflowr_1.7.1     

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3                     
  [2] rstudioapi_0.17.1                      
  [3] jsonlite_2.0.0                         
  [4] magrittr_2.0.3                         
  [5] ggtangle_0.0.7                         
  [6] GenomicFeatures_1.58.0                 
  [7] farver_2.1.2                           
  [8] rmarkdown_2.29                         
  [9] fs_1.6.6                               
 [10] BiocIO_1.16.0                          
 [11] zlibbioc_1.52.0                        
 [12] vctrs_0.6.5                            
 [13] memoise_2.0.1                          
 [14] Rsamtools_2.22.0                       
 [15] RCurl_1.98-1.17                        
 [16] ggtree_3.14.0                          
 [17] htmltools_0.5.8.1                      
 [18] S4Arrays_1.6.0                         
 [19] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [20] plotrix_3.8-4                          
 [21] curl_7.0.0                             
 [22] cellranger_1.1.0                       
 [23] SparseArray_1.6.2                      
 [24] gridGraphics_0.5-1                     
 [25] sass_0.4.10                            
 [26] KernSmooth_2.23-26                     
 [27] bslib_0.9.0                            
 [28] htmlwidgets_1.6.4                      
 [29] plyr_1.8.9                             
 [30] impute_1.80.0                          
 [31] cachem_1.1.0                           
 [32] GenomicAlignments_1.42.0               
 [33] igraph_2.1.4                           
 [34] whisker_0.4.1                          
 [35] lifecycle_1.0.4                        
 [36] pkgconfig_2.0.3                        
 [37] Matrix_1.7-3                           
 [38] R6_2.6.1                               
 [39] fastmap_1.2.0                          
 [40] GenomeInfoDbData_1.2.13                
 [41] MatrixGenerics_1.18.1                  
 [42] enrichplot_1.26.6                      
 [43] digest_0.6.37                          
 [44] aplot_0.2.8                            
 [45] colorspace_2.1-1                       
 [46] patchwork_1.3.2                        
 [47] AnnotationDbi_1.68.0                   
 [48] ps_1.9.1                               
 [49] rprojroot_2.1.1                        
 [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] XVector_0.46.0                         
 [90] pillar_1.11.0                          
 [91] yulab.utils_0.2.1                      
 [92] later_1.4.2                            
 [93] splines_4.4.2                          
 [94] treeio_1.30.0                          
 [95] lattice_0.22-7                         
 [96] bit_4.6.0                              
 [97] tidyselect_1.2.1                       
 [98] GO.db_3.20.0                           
 [99] Biostrings_2.74.1                      
[100] knitr_1.50                             
[101] git2r_0.36.2                           
[102] SummarizedExperiment_1.36.0            
[103] xfun_0.52                              
[104] Biobase_2.66.0                         
[105] matrixStats_1.5.0                      
[106] stringi_1.8.7                          
[107] UCSC.utils_1.2.0                       
[108] lazyeval_0.2.2                         
[109] boot_1.3-32                            
[110] ggfun_0.2.0                            
[111] yaml_2.3.10                            
[112] evaluate_1.0.5                         
[113] codetools_0.2-20                       
[114] qvalue_2.38.0                          
[115] ggplotify_0.1.2                        
[116] cli_3.6.5                              
[117] processx_3.8.6                         
[118] jquerylib_0.1.4                        
[119] dichromat_2.0-0.1                      
[120] Rcpp_1.1.0                             
[121] png_0.1-8                              
[122] XML_3.99-0.18                          
[123] parallel_4.4.2                         
[124] blob_1.2.4                             
[125] DOSE_4.0.1                             
[126] bitops_1.0-9                           
[127] tidytree_0.4.6                         
[128] scales_1.4.0                           
[129] crayon_1.5.3                           
[130] rlang_1.1.6                            
[131] fastmatch_1.1-6                        
[132] cowplot_1.2.0                          
[133] KEGGREST_1.46.0