TSS and CUG overlap
Renee Matthews
2025-05-07
Last updated: 2025-05-15
Checks: 7 0
Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b62ef0b | reneeisnowhere | 2025-05-15 | updates and verification of runs |
| html | 5e6e462 | reneeisnowhere | 2025-05-07 | Build site. |
| Rmd | 2db35c7 | reneeisnowhere | 2025-05-07 | updates to analysis |
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(edgeR)
library(ggfortify)
library(limma)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(biomaRt)
library(eulerr)
library(smplot2)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(epitools)
library(circlize)Getting TSS locations for all genes:
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
tss_gr <- transcripts(txdb)
tss_gr <- resize(tss_gr, width = 1, fix = "start") # TSS is start for both strandsLoading CpG-island locations from UCSC and converting to granges
session <- browserSession("UCSC")
genome(session) <- "hg38"
cpg_table <- getTable(ucscTableQuery(session, track = "CpG Islands"))
cpg_gr <- GRanges(seqnames = cpg_table$chrom,
ranges = IRanges(start = cpg_table$chromStart + 1, end = cpg_table$chromEnd),
strand = "*")Collapsed_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt",
delim = "\t",
escape_double = FALSE,
trim_ws = TRUE)
Motif_list_gr <- readRDS("data/Final_four_data/re_analysis/Motif_list_granges.RDS")
##order specific
df_list <- plyr::llply(Motif_list_gr, as.data.frame)
### no change motif_list_gr names so they do not overwrite the dataframes
names(Motif_list_gr) <- paste0(names(Motif_list_gr), "_gr")
list2env(Motif_list_gr,envir= .GlobalEnv)<environment: R_GlobalEnv>list2env(df_list,envir= .GlobalEnv)<environment: R_GlobalEnv>final_peaks <- Collapsed_peaks %>%
dplyr::filter(Peakid %in% mcols(all_regions_gr)$Peakid) %>%
GRanges()Assess the overlap between my data sets
peaks_tss_annotated <- final_peaks %>%
join_overlap_left(tss_gr) %>%
mutate(TSS_status = ifelse(is.na(tx_id), "non-TSS", "TSS")) olap <- findOverlaps(final_peaks, cpg_gr)
peak_cpg_status <- rep("non-CpG", length(final_peaks))
# Mark peaks that overlap CpG islands
peak_cpg_status[unique(queryHits(olap))] <- "CpG"
final_peaks$CpG_status <- peak_cpg_statusCPG_TSS_status_df <- final_peaks %>%
as.data.frame() %>%
dplyr::select(Peakid,CpG_status) %>%
left_join(.,(peaks_tss_annotated %>%
as.data.frame() %>%
dplyr::select(Peakid,TSS_status) %>%
distinct()), by=c("Peakid"="Peakid")) %>%
mutate(mrc = case_when(
Peakid %in% EAR_open$Peakid ~ "EAR_open",
Peakid %in% EAR_close$Peakid ~ "EAR_close",
Peakid %in% ESR_open$Peakid ~ "ESR_open",
Peakid %in% ESR_close$Peakid ~ "ESR_close",
Peakid %in% ESR_opcl$Peakid ~ "ESR_opcl",
Peakid %in% LR_open$Peakid ~ "LR_open",
Peakid %in% LR_close$Peakid ~ "LR_close",
Peakid %in% NR$Peakid ~ "NR",
Peakid %in% ESR_clop$Peakid ~ "ESR_clop",
TRUE ~ "not_mrc"
)) CpG_mat <- CPG_TSS_status_df %>%
mutate(mrc=factor(mrc, levels = c("EAR_open","ESR_open","LR_open","ESR_opcl", "EAR_close","ESR_close","LR_close","ESR_clop", "NR"))) %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(Peakid) %>%
summarise(CpG_status = ifelse(any(CpG_status == "CpG"), "CpG_peak", "not_CpG_peak"), mrc=unique(mrc)) %>%
ungroup() %>%
group_by(CpG_status, mrc) %>%
tally %>%
mutate(mrc=factor(mrc, levels = c("EAR_open","ESR_open","LR_open","ESR_opcl", "EAR_close","ESR_close","LR_close","ESR_clop", "NR"))) %>%
pivot_wider(id_cols = mrc, names_from = CpG_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
CpG_mat CpG_peak not_CpG_peak
EAR_open 1316 3583
ESR_open 783 5494
LR_open 788 24829
ESR_opcl 2 201
EAR_close 37 3038
ESR_close 351 7583
LR_close 1549 17061
NR 13639 71515TSS_mat <- CPG_TSS_status_df %>%
mutate(mrc=factor(mrc, levels = c("EAR_open","ESR_open","LR_open","ESR_opcl", "EAR_close","ESR_close","LR_close","ESR_clop", "NR"))) %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(Peakid) %>%
summarise(TSS_status = ifelse(any(TSS_status == "TSS"), "TSS_peak", "not_TSS_peak"), mrc=unique(mrc)) %>%
ungroup() %>%
group_by(TSS_status, mrc) %>%
tally %>%
mutate(mrc=factor(mrc, levels = c("EAR_open","ESR_open","LR_open","ESR_opcl", "EAR_close","ESR_close","LR_close","ESR_clop", "NR"))) %>%
pivot_wider(id_cols = mrc, names_from = TSS_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
TSS_mat TSS_peak not_TSS_peak
EAR_open 1337 3562
ESR_open 578 5699
LR_open 1445 24172
ESR_opcl 10 193
EAR_close 159 2916
ESR_close 665 7269
LR_close 2071 16539
ESR_clop 25 689
NR 15183 69971odds ratio results
matrix_list <- list("CpG"=CpG_mat,"TSS"=TSS_mat)
results_or <- data.frame(Matrix_Name = character(),
Row_Compared = character(),
Odds_Ratio = numeric(),
Lower_CI = numeric(),
Upper_CI = numeric(),
P_Value = numeric(),
stringsAsFactors = FALSE)
# Loop through each matrix in the list
for (matrix_name in names(matrix_list)) {
current_matrix <- matrix_list[[matrix_name]]
n_rows <- nrow(current_matrix)
# Loop through each row of the current matrix (except the last row)
for (i in 1:(n_rows - 1)) {
# Perform odds ratio test between row i and the last row using epitools
test_result <- tryCatch({
contingency_table <- rbind(current_matrix[i, ], current_matrix[n_rows, ])
# Check if any row in the contingency table contains only zeros
if (any(rowSums(contingency_table) == 0)) {
stop("Contingency table contains empty rows.")
}
oddsratio_result <- oddsratio(contingency_table)
# Ensure the oddsratio result has at least 2 rows
if (nrow(oddsratio_result$measure) < 2) {
stop("oddsratio result does not have enough data.")
}
list(oddsratio = oddsratio_result, p.value = oddsratio_result$p.value[2,"chi.square"])
}, error = function(e) {
cat("Error in odds ratio test for row", i, "in matrix", matrix_name, ":", e$message, "\n")
return(NULL)
})
# Only store the result if test_result is valid (i.e., not NULL)
if (!is.null(test_result)) {
or_value <- test_result$oddsratio$measure[2, "estimate"]
lower_ci <- test_result$oddsratio$measure[2, "lower"]
upper_ci <- test_result$oddsratio$measure[2, "upper"]
p_value <- test_result$oddsratio$p.value[2,"chi.square"]
# Check if the values are numeric and valid (not NA)
if (!is.na(or_value) && !is.na(lower_ci) && !is.na(upper_ci) && !is.na(p_value)) {
# Store the results in the dataframe
results_or <- rbind(results_or, data.frame(Matrix_Name = matrix_name,
Row_Compared = rownames(current_matrix)[i],
Odds_Ratio = or_value,
Lower_CI = lower_ci,
Upper_CI = upper_ci,
P_Value = p_value))
}
}
}
}
# Print the resulting dataframe
print(results_or) %>%
kable(., caption = "Odd ratio results and significance values of TSS and CpG enrichment compared to No response group") %>%
kable_paper("striped", full_width = TRUE) %>%
kable_styling(full_width = FALSE, font_size = 14) %>%
scroll_box(width = "100%", height = "400px") Matrix_Name Row_Compared Odds_Ratio Lower_CI Upper_CI
estimate CpG EAR_open 1.92597777 1.802855929 2.05633456
estimate1 CpG ESR_open 0.74741887 0.691509768 0.80677202
estimate2 CpG LR_open 0.16644962 0.154603250 0.17896478
estimate3 CpG ESR_opcl 0.05619206 0.008668747 0.17429955
estimate4 CpG EAR_close 0.06414412 0.045525224 0.08733262
estimate5 CpG ESR_close 0.24281621 0.217444012 0.27021472
estimate6 CpG LR_close 0.47610262 0.450407703 0.50292664
estimate7 TSS EAR_open 1.72991067 1.620164455 1.84599655
estimate8 TSS ESR_open 0.46752209 0.427988953 0.50971415
estimate9 TSS LR_open 0.27553001 0.260470339 0.29126056
estimate10 TSS ESR_opcl 0.24250730 0.119554510 0.43398244
estimate11 TSS EAR_close 0.25154837 0.213387053 0.29427899
estimate12 TSS ESR_close 0.42171208 0.388410369 0.45705409
estimate13 TSS LR_close 0.57711710 0.549429034 0.60591901
estimate14 TSS ESR_clop 0.16828570 0.109862297 0.24523928
P_Value
estimate 1.450783e-87
estimate1 1.072886e-13
estimate2 0.000000e+00
estimate3 5.291085e-09
estimate4 3.803627e-110
estimate5 3.973011e-168
estimate6 2.364549e-159
estimate7 3.683755e-62
estimate8 3.264184e-68
estimate9 0.000000e+00
estimate10 1.580337e-06
estimate11 5.555494e-74
estimate12 9.408337e-102
estimate13 1.306899e-109
estimate14 1.731729e-23| Matrix_Name | Row_Compared | Odds_Ratio | Lower_CI | Upper_CI | P_Value | |
|---|---|---|---|---|---|---|
| estimate | CpG | EAR_open | 1.9259778 | 1.8028559 | 2.0563346 | 0.0e+00 |
| estimate1 | CpG | ESR_open | 0.7474189 | 0.6915098 | 0.8067720 | 0.0e+00 |
| estimate2 | CpG | LR_open | 0.1664496 | 0.1546032 | 0.1789648 | 0.0e+00 |
| estimate3 | CpG | ESR_opcl | 0.0561921 | 0.0086687 | 0.1742996 | 0.0e+00 |
| estimate4 | CpG | EAR_close | 0.0641441 | 0.0455252 | 0.0873326 | 0.0e+00 |
| estimate5 | CpG | ESR_close | 0.2428162 | 0.2174440 | 0.2702147 | 0.0e+00 |
| estimate6 | CpG | LR_close | 0.4761026 | 0.4504077 | 0.5029266 | 0.0e+00 |
| estimate7 | TSS | EAR_open | 1.7299107 | 1.6201645 | 1.8459966 | 0.0e+00 |
| estimate8 | TSS | ESR_open | 0.4675221 | 0.4279890 | 0.5097142 | 0.0e+00 |
| estimate9 | TSS | LR_open | 0.2755300 | 0.2604703 | 0.2912606 | 0.0e+00 |
| estimate10 | TSS | ESR_opcl | 0.2425073 | 0.1195545 | 0.4339824 | 1.6e-06 |
| estimate11 | TSS | EAR_close | 0.2515484 | 0.2133871 | 0.2942790 | 0.0e+00 |
| estimate12 | TSS | ESR_close | 0.4217121 | 0.3884104 | 0.4570541 | 0.0e+00 |
| estimate13 | TSS | LR_close | 0.5771171 | 0.5494290 | 0.6059190 | 0.0e+00 |
| estimate14 | TSS | ESR_clop | 0.1682857 | 0.1098623 | 0.2452393 | 0.0e+00 |
col_fun_OR = colorRamp2(c(0,1,1.5,5), c("blueviolet","white","lightgreen","green3" ))
sig_mat_OR <- results_or %>%
as.data.frame() %>%
dplyr::select( Matrix_Name,Row_Compared,P_Value) %>%
group_by(Row_Compared) %>%
mutate(rank_val=rank(P_Value, ties.method = "first")) %>%
mutate(BH_correction= p.adjust(P_Value,method= "BH")) %>%
pivot_wider(., id_cols = Matrix_Name, names_from = Row_Compared, values_from = BH_correction) %>%
dplyr::select(Matrix_Name,EAR_open,ESR_open,LR_open,ESR_opcl,EAR_close,ESR_close,LR_close,ESR_clop) %>%
column_to_rownames("Matrix_Name") %>%
as.matrix()
# saveRDS(results_or,"data/Final_four_data/re_analysis/OR_results_TSS_CpG_df_1bp.RDS")results_or %>%
as.data.frame() %>%
dplyr::select( Matrix_Name,Row_Compared,Odds_Ratio) %>%
pivot_wider(., id_cols = Matrix_Name, names_from = Row_Compared, values_from = Odds_Ratio) %>%
dplyr::select(Matrix_Name,EAR_open,ESR_open,LR_open,ESR_opcl,EAR_close,ESR_close,LR_close,ESR_clop) %>%
column_to_rownames("Matrix_Name") %>%
as.matrix() %>%
ComplexHeatmap::Heatmap(. ,col = col_fun_OR,
cluster_rows=FALSE,
cluster_columns=FALSE,
column_names_side = "top",
column_names_rot = 45,
# na_col = "black",
cell_fun = function(j, i, x, y, width, height, fill) {if (!is.na(sig_mat_OR[i, j]) && sig_mat_OR[i, j] < 0.05 && .[i, j] > 1) {
grid.text("*", x, y, gp = gpar(fontsize = 20))}})
| Version | Author | Date |
|---|---|---|
| 5e6e462 | reneeisnowhere | 2025-05-07 |
note, this is corrected for multiple testing across TSS and CUG tests only in each motif cluster.
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
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] circlize_0.4.16
[2] epitools_0.5-10.1
[3] ggrepel_0.9.6
[4] plyranges_1.26.0
[5] ggsignif_0.6.4
[6] genomation_1.38.0
[7] smplot2_0.2.5
[8] eulerr_7.0.2
[9] biomaRt_2.62.1
[10] devtools_2.4.5
[11] usethis_3.1.0
[12] ggpubr_0.6.0
[13] BiocParallel_1.40.0
[14] scales_1.3.0
[15] VennDiagram_1.7.3
[16] futile.logger_1.4.3
[17] gridExtra_2.3
[18] ggfortify_0.4.17
[19] edgeR_4.4.2
[20] limma_3.62.2
[21] rtracklayer_1.66.0
[22] org.Hs.eg.db_3.20.0
[23] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[24] GenomicFeatures_1.58.0
[25] AnnotationDbi_1.68.0
[26] Biobase_2.66.0
[27] GenomicRanges_1.58.0
[28] GenomeInfoDb_1.42.3
[29] IRanges_2.40.1
[30] S4Vectors_0.44.0
[31] BiocGenerics_0.52.0
[32] ChIPseeker_1.42.1
[33] RColorBrewer_1.1-3
[34] broom_1.0.7
[35] kableExtra_1.4.0
[36] lubridate_1.9.4
[37] forcats_1.0.0
[38] stringr_1.5.1
[39] dplyr_1.1.4
[40] purrr_1.0.4
[41] readr_2.1.5
[42] tidyr_1.3.1
[43] tibble_3.2.1
[44] ggplot2_3.5.1
[45] tidyverse_2.0.0
[46] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.5
[2] matrixStats_1.5.0
[3] bitops_1.0-9
[4] enrichplot_1.26.6
[5] doParallel_1.0.17
[6] httr_1.4.7
[7] profvis_0.4.0
[8] tools_4.4.2
[9] backports_1.5.0
[10] R6_2.6.1
[11] lazyeval_0.2.2
[12] GetoptLong_1.0.5
[13] urlchecker_1.0.1
[14] withr_3.0.2
[15] prettyunits_1.2.0
[16] cli_3.6.4
[17] formatR_1.14
[18] Cairo_1.6-2
[19] sass_0.4.9
[20] Rsamtools_2.22.0
[21] systemfonts_1.2.1
[22] yulab.utils_0.2.0
[23] foreign_0.8-88
[24] DOSE_4.0.0
[25] svglite_2.1.3
[26] R.utils_2.13.0
[27] sessioninfo_1.2.3
[28] plotrix_3.8-4
[29] BSgenome_1.74.0
[30] pwr_1.3-0
[31] impute_1.80.0
[32] rstudioapi_0.17.1
[33] RSQLite_2.3.9
[34] shape_1.4.6.1
[35] generics_0.1.3
[36] gridGraphics_0.5-1
[37] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[38] BiocIO_1.16.0
[39] vroom_1.6.5
[40] gtools_3.9.5
[41] car_3.1-3
[42] GO.db_3.20.0
[43] Matrix_1.7-3
[44] abind_1.4-8
[45] R.methodsS3_1.8.2
[46] lifecycle_1.0.4
[47] whisker_0.4.1
[48] yaml_2.3.10
[49] carData_3.0-5
[50] SummarizedExperiment_1.36.0
[51] gplots_3.2.0
[52] qvalue_2.38.0
[53] SparseArray_1.6.2
[54] BiocFileCache_2.14.0
[55] blob_1.2.4
[56] promises_1.3.2
[57] crayon_1.5.3
[58] miniUI_0.1.1.1
[59] ggtangle_0.0.6
[60] lattice_0.22-6
[61] cowplot_1.1.3
[62] KEGGREST_1.46.0
[63] magick_2.8.5
[64] ComplexHeatmap_2.22.0
[65] pillar_1.10.1
[66] knitr_1.49
[67] fgsea_1.32.2
[68] rjson_0.2.23
[69] boot_1.3-31
[70] codetools_0.2-20
[71] fastmatch_1.1-6
[72] glue_1.8.0
[73] getPass_0.2-4
[74] ggfun_0.1.8
[75] data.table_1.17.0
[76] remotes_2.5.0
[77] vctrs_0.6.5
[78] png_0.1-8
[79] treeio_1.30.0
[80] gtable_0.3.6
[81] cachem_1.1.0
[82] xfun_0.51
[83] S4Arrays_1.6.0
[84] mime_0.12
[85] iterators_1.0.14
[86] statmod_1.5.0
[87] ellipsis_0.3.2
[88] nlme_3.1-167
[89] ggtree_3.14.0
[90] bit64_4.6.0-1
[91] filelock_1.0.3
[92] progress_1.2.3
[93] rprojroot_2.0.4
[94] bslib_0.9.0
[95] rpart_4.1.24
[96] KernSmooth_2.23-26
[97] Hmisc_5.2-2
[98] colorspace_2.1-1
[99] DBI_1.2.3
[100] seqPattern_1.38.0
[101] nnet_7.3-20
[102] tidyselect_1.2.1
[103] processx_3.8.6
[104] bit_4.6.0
[105] compiler_4.4.2
[106] curl_6.2.1
[107] git2r_0.35.0
[108] httr2_1.1.1
[109] htmlTable_2.4.3
[110] xml2_1.3.7
[111] DelayedArray_0.32.0
[112] checkmate_2.3.2
[113] caTools_1.18.3
[114] callr_3.7.6
[115] rappdirs_0.3.3
[116] digest_0.6.37
[117] rmarkdown_2.29
[118] XVector_0.46.0
[119] base64enc_0.1-3
[120] htmltools_0.5.8.1
[121] pkgconfig_2.0.3
[122] MatrixGenerics_1.18.1
[123] dbplyr_2.5.0
[124] fastmap_1.2.0
[125] GlobalOptions_0.1.2
[126] rlang_1.1.5
[127] htmlwidgets_1.6.4
[128] UCSC.utils_1.2.0
[129] shiny_1.10.0
[130] farver_2.1.2
[131] jquerylib_0.1.4
[132] zoo_1.8-13
[133] jsonlite_1.9.1
[134] GOSemSim_2.32.0
[135] R.oo_1.27.0
[136] RCurl_1.98-1.16
[137] magrittr_2.0.3
[138] Formula_1.2-5
[139] GenomeInfoDbData_1.2.13
[140] ggplotify_0.1.2
[141] patchwork_1.3.0
[142] munsell_0.5.1
[143] Rcpp_1.0.14
[144] ape_5.8-1
[145] stringi_1.8.4
[146] zlibbioc_1.52.0
[147] plyr_1.8.9
[148] pkgbuild_1.4.6
[149] parallel_4.4.2
[150] Biostrings_2.74.1
[151] splines_4.4.2
[152] hms_1.1.3
[153] locfit_1.5-9.12
[154] ps_1.9.0
[155] igraph_2.1.4
[156] reshape2_1.4.4
[157] pkgload_1.4.0
[158] futile.options_1.0.1
[159] XML_3.99-0.18
[160] evaluate_1.0.3
[161] lambda.r_1.2.4
[162] foreach_1.5.2
[163] tzdb_0.4.0
[164] httpuv_1.6.15
[165] clue_0.3-66
[166] gridBase_0.4-7
[167] xtable_1.8-4
[168] restfulr_0.0.15
[169] tidytree_0.4.6
[170] rstatix_0.7.2
[171] later_1.4.1
[172] viridisLite_0.4.2
[173] aplot_0.2.5
[174] memoise_2.0.1
[175] GenomicAlignments_1.42.0
[176] cluster_2.1.8.1
[177] timechange_0.3.0