TSS and CUG overlap
Renee Matthews
2025-05-07
Last updated: 2025-06-18
Checks: 7 0
Knit directory: ATAC_learning/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20231016) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version c78c973. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .RData
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: analysis/H3K27ac_integration_noM.Rmd
Ignored: data/ACresp_SNP_table.csv
Ignored: data/ARR_SNP_table.csv
Ignored: data/All_merged_peaks.tsv
Ignored: data/CAD_gwas_dataframe.RDS
Ignored: data/CTX_SNP_table.csv
Ignored: data/Collapsed_expressed_NG_peak_table.csv
Ignored: data/DEG_toplist_sep_n45.RDS
Ignored: data/FRiP_first_run.txt
Ignored: data/Final_four_data/
Ignored: data/Frip_1_reads.csv
Ignored: data/Frip_2_reads.csv
Ignored: data/Frip_3_reads.csv
Ignored: data/Frip_4_reads.csv
Ignored: data/Frip_5_reads.csv
Ignored: data/Frip_6_reads.csv
Ignored: data/GO_KEGG_analysis/
Ignored: data/HF_SNP_table.csv
Ignored: data/Ind1_75DA24h_dedup_peaks.csv
Ignored: data/Ind1_TSS_peaks.RDS
Ignored: data/Ind1_firstfragment_files.txt
Ignored: data/Ind1_fragment_files.txt
Ignored: data/Ind1_peaks_list.RDS
Ignored: data/Ind1_summary.txt
Ignored: data/Ind2_TSS_peaks.RDS
Ignored: data/Ind2_fragment_files.txt
Ignored: data/Ind2_peaks_list.RDS
Ignored: data/Ind2_summary.txt
Ignored: data/Ind3_TSS_peaks.RDS
Ignored: data/Ind3_fragment_files.txt
Ignored: data/Ind3_peaks_list.RDS
Ignored: data/Ind3_summary.txt
Ignored: data/Ind4_79B24h_dedup_peaks.csv
Ignored: data/Ind4_TSS_peaks.RDS
Ignored: data/Ind4_V24h_fraglength.txt
Ignored: data/Ind4_fragment_files.txt
Ignored: data/Ind4_fragment_filesN.txt
Ignored: data/Ind4_peaks_list.RDS
Ignored: data/Ind4_summary.txt
Ignored: data/Ind5_TSS_peaks.RDS
Ignored: data/Ind5_fragment_files.txt
Ignored: data/Ind5_fragment_filesN.txt
Ignored: data/Ind5_peaks_list.RDS
Ignored: data/Ind5_summary.txt
Ignored: data/Ind6_TSS_peaks.RDS
Ignored: data/Ind6_fragment_files.txt
Ignored: data/Ind6_peaks_list.RDS
Ignored: data/Ind6_summary.txt
Ignored: data/Knowles_4.RDS
Ignored: data/Knowles_5.RDS
Ignored: data/Knowles_6.RDS
Ignored: data/LiSiLTDNRe_TE_df.RDS
Ignored: data/MI_gwas.RDS
Ignored: data/SNP_GWAS_PEAK_MRC_id
Ignored: data/SNP_GWAS_PEAK_MRC_id.csv
Ignored: data/SNP_gene_cat_list.tsv
Ignored: data/SNP_supp_schneider.RDS
Ignored: data/TE_info/
Ignored: data/TFmapnames.RDS
Ignored: data/all_TSSE_scores.RDS
Ignored: data/all_four_filtered_counts.txt
Ignored: data/aln_run1_results.txt
Ignored: data/anno_ind1_DA24h.RDS
Ignored: data/anno_ind4_V24h.RDS
Ignored: data/annotated_gwas_SNPS.csv
Ignored: data/background_n45_he_peaks.RDS
Ignored: data/cardiac_muscle_FRIP.csv
Ignored: data/cardiomyocyte_FRIP.csv
Ignored: data/col_ng_peak.csv
Ignored: data/cormotif_full_4_run.RDS
Ignored: data/cormotif_full_4_run_he.RDS
Ignored: data/cormotif_full_6_run.RDS
Ignored: data/cormotif_full_6_run_he.RDS
Ignored: data/cormotif_probability_45_list.csv
Ignored: data/cormotif_probability_45_list_he.csv
Ignored: data/cormotif_probability_all_6_list.csv
Ignored: data/cormotif_probability_all_6_list_he.csv
Ignored: data/datasave.RDS
Ignored: data/embryo_heart_FRIP.csv
Ignored: data/enhancer_list_ENCFF126UHK.bed
Ignored: data/enhancerdata/
Ignored: data/filt_Peaks_efit2.RDS
Ignored: data/filt_Peaks_efit2_bl.RDS
Ignored: data/filt_Peaks_efit2_n45.RDS
Ignored: data/first_Peaksummarycounts.csv
Ignored: data/first_run_frag_counts.txt
Ignored: data/full_bedfiles/
Ignored: data/gene_ref.csv
Ignored: data/gwas_1_dataframe.RDS
Ignored: data/gwas_2_dataframe.RDS
Ignored: data/gwas_3_dataframe.RDS
Ignored: data/gwas_4_dataframe.RDS
Ignored: data/gwas_5_dataframe.RDS
Ignored: data/high_conf_peak_counts.csv
Ignored: data/high_conf_peak_counts.txt
Ignored: data/high_conf_peaks_bl_counts.txt
Ignored: data/high_conf_peaks_counts.txt
Ignored: data/hits_files/
Ignored: data/hyper_files/
Ignored: data/hypo_files/
Ignored: data/ind1_DA24hpeaks.RDS
Ignored: data/ind1_TSSE.RDS
Ignored: data/ind2_TSSE.RDS
Ignored: data/ind3_TSSE.RDS
Ignored: data/ind4_TSSE.RDS
Ignored: data/ind4_V24hpeaks.RDS
Ignored: data/ind5_TSSE.RDS
Ignored: data/ind6_TSSE.RDS
Ignored: data/initial_complete_stats_run1.txt
Ignored: data/left_ventricle_FRIP.csv
Ignored: data/median_24_lfc.RDS
Ignored: data/median_3_lfc.RDS
Ignored: data/mergedPeads.gff
Ignored: data/mergedPeaks.gff
Ignored: data/motif_list_full
Ignored: data/motif_list_n45
Ignored: data/motif_list_n45.RDS
Ignored: data/multiqc_fastqc_run1.txt
Ignored: data/multiqc_fastqc_run2.txt
Ignored: data/multiqc_genestat_run1.txt
Ignored: data/multiqc_genestat_run2.txt
Ignored: data/my_hc_filt_counts.RDS
Ignored: data/my_hc_filt_counts_n45.RDS
Ignored: data/n45_bedfiles/
Ignored: data/n45_files
Ignored: data/other_papers/
Ignored: data/peakAnnoList_1.RDS
Ignored: data/peakAnnoList_2.RDS
Ignored: data/peakAnnoList_24_full.RDS
Ignored: data/peakAnnoList_24_n45.RDS
Ignored: data/peakAnnoList_3.RDS
Ignored: data/peakAnnoList_3_full.RDS
Ignored: data/peakAnnoList_3_n45.RDS
Ignored: data/peakAnnoList_4.RDS
Ignored: data/peakAnnoList_5.RDS
Ignored: data/peakAnnoList_6.RDS
Ignored: data/peakAnnoList_Eight.RDS
Ignored: data/peakAnnoList_full_motif.RDS
Ignored: data/peakAnnoList_n45_motif.RDS
Ignored: data/siglist_full.RDS
Ignored: data/siglist_n45.RDS
Ignored: data/summarized_peaks_dataframe.txt
Ignored: data/summary_peakIDandReHeat.csv
Ignored: data/test.list.RDS
Ignored: data/testnames.txt
Ignored: data/toplist_6.RDS
Ignored: data/toplist_full.RDS
Ignored: data/toplist_full_DAR_6.RDS
Ignored: data/toplist_n45.RDS
Ignored: data/trimmed_seq_length.csv
Ignored: data/unclassified_full_set_peaks.RDS
Ignored: data/unclassified_n45_set_peaks.RDS
Ignored: data/xstreme/
Untracked files:
Untracked: RNA_seq_integration.Rmd
Untracked: Rplot.pdf
Untracked: Sig_meta
Untracked: analysis/.gitignore
Untracked: analysis/AC_shared_analysis.Rmd
Untracked: analysis/Cormotif_analysis_testing diff.Rmd
Untracked: analysis/Diagnosis-tmm.Rmd
Untracked: analysis/Expressed_RNA_associations.Rmd
Untracked: analysis/GO_analysis_DAR.Rmd
Untracked: analysis/Jaspar_motif_DAR.Rmd
Untracked: analysis/LFC_corr.Rmd
Untracked: analysis/SNP_TAD_peaks.Rmd
Untracked: analysis/SVA.Rmd
Untracked: analysis/Tan2020.Rmd
Untracked: analysis/Top2B_analysis.Rmd
Untracked: analysis/making_master_peaks_list.Rmd
Untracked: analysis/my_hc_filt_counts.csv
Untracked: code/Concatenations_for_export.R
Untracked: code/IGV_snapshot_code.R
Untracked: code/LongDARlist.R
Untracked: code/just_for_Fun.R
Untracked: my_plot.pdf
Untracked: my_plot.png
Untracked: output/cormotif_probability_45_list.csv
Untracked: output/cormotif_probability_all_6_list.csv
Untracked: setup.RData
Unstaged changes:
Modified: ATAC_learning.Rproj
Modified: analysis/AF_HF_SNPs.Rmd
Modified: analysis/Cardiotox_SNPs.Rmd
Modified: analysis/Cormotif_analysis.Rmd
Modified: analysis/DEG_analysis.Rmd
Modified: analysis/H3K27ac_initial_QC.Rmd
Modified: analysis/Jaspar_motif.Rmd
Modified: analysis/Jaspar_motif_ff.Rmd
Modified: analysis/TE_analysis_norm.Rmd
Modified: analysis/final_four_analysis.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/TSS_and_CUG_ALL_DAR.Rmd)
and HTML (docs/TSS_and_CUG_ALL_DAR.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c78c973 | reneeisnowhere | 2025-06-18 | updates using all trts |
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$chromEnd),
strand = "*")Loading region data
Collapsed_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt",
delim = "\t",
escape_double = FALSE,
trim_ws = TRUE)
toptable_results <- readRDS("data/Final_four_data/re_analysis/Toptable_results.RDS")
all_regions <- toptable_results$DOX_24$genes
Collapsed_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt",
delim = "\t",
escape_double = FALSE,
trim_ws = TRUE)
final_peaks <- Collapsed_peaks %>%
dplyr::filter(chr != "chrY") %>%
dplyr::filter(Peakid %in% all_regions) %>%
GRanges()
all_results <- toptable_results %>%
imap(~ .x %>% tibble::rownames_to_column(var = "rowname") %>%
mutate(source = .y)) %>%
bind_rows()
my_DOX_data <- all_results %>%
dplyr::filter(source=="DOX_3"|source=="DOX_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))
my_EPI_data <- all_results %>%
dplyr::filter(source=="EPI_3"|source=="EPI_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))
my_DNR_data <- all_results %>%
dplyr::filter(source=="DNR_3"|source=="DNR_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))
my_MTX_data <- all_results %>%
dplyr::filter(source=="MTX_3"|source=="MTX_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))Assess the overlap between my data sets
peaks_tss_annotated <- final_peaks %>%
join_overlap_left(tss_gr) %>%
mutate(TSS_status = ifelse(is.na(tx_id), "non-TSS", "TSS")) olap <- findOverlaps(final_peaks, cpg_gr)
peak_cpg_status <- rep("non-CpG", length(final_peaks))
# Mark peaks that overlap CpG islands
peak_cpg_status[unique(queryHits(olap))] <- "CpG"
final_peaks$CpG_status <- peak_cpg_statusall_results <- toptable_results %>%
imap(~ .x %>% tibble::rownames_to_column(var = "rowname") %>%
mutate(source = .y)) %>%
bind_rows()
CPG_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")) %>%
left_join(.,my_DOX_data,by=c("Peakid"="genes")) %>%
mutate(DOX_sig_3=if_else(adj.P.Val_DOX_3<0.05,"sig","not_sig"),
DOX_sig_24=if_else(adj.P.Val_DOX_24<0.05,"sig","not_sig")) %>%
mutate(DOX_sig_3=factor(DOX_sig_3,levels=c("sig","not_sig")),
DOX_sig_24=factor(DOX_sig_24,levels=c("sig","not_sig"))) %>%
left_join(.,my_EPI_data,by=c("Peakid"="genes")) %>%
mutate(EPI_sig_3=if_else(adj.P.Val_EPI_3<0.05,"sig","not_sig"),
EPI_sig_24=if_else(adj.P.Val_EPI_24<0.05,"sig","not_sig")) %>%
mutate(EPI_sig_3=factor(EPI_sig_3,levels=c("sig","not_sig")),
EPI_sig_24=factor(EPI_sig_24,levels=c("sig","not_sig"))) %>%
left_join(.,my_DNR_data,by=c("Peakid"="genes")) %>%
mutate(DNR_sig_3=if_else(adj.P.Val_DNR_3<0.05,"sig","not_sig"),
DNR_sig_24=if_else(adj.P.Val_DNR_24<0.05,"sig","not_sig")) %>%
mutate(DNR_sig_3=factor(DNR_sig_3,levels=c("sig","not_sig")),
DNR_sig_24=factor(DNR_sig_24,levels=c("sig","not_sig"))) %>%
left_join(.,my_MTX_data,by=c("Peakid"="genes")) %>%
mutate(MTX_sig_3=if_else(adj.P.Val_MTX_3<0.05,"sig","not_sig"),
MTX_sig_24=if_else(adj.P.Val_MTX_24<0.05,"sig","not_sig")) %>%
mutate(MTX_sig_3=factor(MTX_sig_3,levels=c("sig","not_sig")),
MTX_sig_24=factor(MTX_sig_24,levels=c("sig","not_sig")))
CPG_TSS_status_df %>%
group_by(CpG_status,DOX_sig_3) %>%
tally()# A tibble: 4 × 3
# Groups: CpG_status [2]
CpG_status DOX_sig_3 n
<chr> <fct> <int>
1 CpG sig 377
2 CpG not_sig 18392
3 non-CpG sig 3096
4 non-CpG not_sig 133692DOX
status_columns <- c("CpG_status", "TSS_status")
DOX_3_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix), paste0("non-", prefix))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- CPG_TSS_status_df %>%
group_by(across(all_of(status_col)), DOX_sig_3) %>%
tally() %>%
pivot_wider(
names_from = DOX_sig_3,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
})
# Set names so you can easily refer to each status
names(DOX_3_status_matrices) <- status_columns
odds_ratio_results_DOX_3 <- map(DOX_3_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2)) || any(!is.finite(mat)) || sum(mat) == 0 || any(rowSums(mat) == 0) || any(colSums(mat) == 0)) {
return(NULL)
}
result <- epitools::oddsratio(mat, method = "wald")
or <- result$measure[2, "estimate"]
lower <- result$measure[2, "lower"]
upper <- result$measure[2, "upper"]
pval_chisq <- if("chi.square" %in% colnames(result$p.value) && nrow(result$p.value) >= 2) {
result$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})DOX_24_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix), paste0("non-", prefix))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- CPG_TSS_status_df %>%
group_by(across(all_of(status_col)), DOX_sig_24) %>%
tally() %>%
pivot_wider(
names_from = DOX_sig_24,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
})
# Set names so you can easily refer to each status
names(DOX_24_status_matrices) <- status_columns
odds_ratio_results_DOX_24 <- map(DOX_24_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2)) || any(!is.finite(mat)) || sum(mat) == 0 || any(rowSums(mat) == 0) || any(colSums(mat) == 0)) {
return(NULL)
}
result <- epitools::oddsratio(mat, method = "wald")
or <- result$measure[2, "estimate"]
lower <- result$measure[2, "lower"]
upper <- result$measure[2, "upper"]
pval_chisq <- if("chi.square" %in% colnames(result$p.value) && nrow(result$p.value) >= 2) {
result$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})EPI
status_columns <- c("CpG_status", "TSS_status")
EPI_3_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix), paste0("non-", prefix))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- CPG_TSS_status_df %>%
group_by(across(all_of(status_col)), EPI_sig_3) %>%
tally() %>%
pivot_wider(
names_from = EPI_sig_3,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
})
# Set names so you can easily refer to each status
names(EPI_3_status_matrices) <- status_columns
odds_ratio_results_EPI_3 <- map(EPI_3_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2)) || any(!is.finite(mat)) || sum(mat) == 0 || any(rowSums(mat) == 0) || any(colSums(mat) == 0)) {
return(NULL)
}
result <- epitools::oddsratio(mat, method = "wald")
or <- result$measure[2, "estimate"]
lower <- result$measure[2, "lower"]
upper <- result$measure[2, "upper"]
pval_chisq <- if("chi.square" %in% colnames(result$p.value) && nrow(result$p.value) >= 2) {
result$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})EPI_24_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix), paste0("non-", prefix))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- CPG_TSS_status_df %>%
group_by(across(all_of(status_col)), EPI_sig_24) %>%
tally() %>%
pivot_wider(
names_from = EPI_sig_24,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
})
# Set names so you can easily refer to each status
names(EPI_24_status_matrices) <- status_columns
odds_ratio_results_EPI_24 <- map(EPI_24_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2)) || any(!is.finite(mat)) || sum(mat) == 0 || any(rowSums(mat) == 0) || any(colSums(mat) == 0)) {
return(NULL)
}
result <- epitools::oddsratio(mat, method = "wald")
or <- result$measure[2, "estimate"]
lower <- result$measure[2, "lower"]
upper <- result$measure[2, "upper"]
pval_chisq <- if("chi.square" %in% colnames(result$p.value) && nrow(result$p.value) >= 2) {
result$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})DNR
status_columns <- c("CpG_status", "TSS_status")
DNR_3_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix), paste0("non-", prefix))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- CPG_TSS_status_df %>%
group_by(across(all_of(status_col)), DNR_sig_3) %>%
tally() %>%
pivot_wider(
names_from = DNR_sig_3,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
})
# Set names so you can easily refer to each status
names(DNR_3_status_matrices) <- status_columns
odds_ratio_results_DNR_3 <- map(DNR_3_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2)) || any(!is.finite(mat)) || sum(mat) == 0 || any(rowSums(mat) == 0) || any(colSums(mat) == 0)) {
return(NULL)
}
result <- epitools::oddsratio(mat, method = "wald")
or <- result$measure[2, "estimate"]
lower <- result$measure[2, "lower"]
upper <- result$measure[2, "upper"]
pval_chisq <- if("chi.square" %in% colnames(result$p.value) && nrow(result$p.value) >= 2) {
result$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})DNR_24_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix), paste0("non-", prefix))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- CPG_TSS_status_df %>%
group_by(across(all_of(status_col)), DNR_sig_24) %>%
tally() %>%
pivot_wider(
names_from = DNR_sig_24,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
})
# Set names so you can easily refer to each status
names(DNR_24_status_matrices) <- status_columns
odds_ratio_results_DNR_24 <- map(DNR_24_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2)) || any(!is.finite(mat)) || sum(mat) == 0 || any(rowSums(mat) == 0) || any(colSums(mat) == 0)) {
return(NULL)
}
result <- epitools::oddsratio(mat, method = "wald")
or <- result$measure[2, "estimate"]
lower <- result$measure[2, "lower"]
upper <- result$measure[2, "upper"]
pval_chisq <- if("chi.square" %in% colnames(result$p.value) && nrow(result$p.value) >= 2) {
result$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})MTX
status_columns <- c("CpG_status", "TSS_status")
MTX_3_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix), paste0("non-", prefix))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- CPG_TSS_status_df %>%
group_by(across(all_of(status_col)), MTX_sig_3) %>%
tally() %>%
pivot_wider(
names_from = MTX_sig_3,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
})
# Set names so you can easily refer to each status
names(MTX_3_status_matrices) <- status_columns
odds_ratio_results_MTX_3 <- map(MTX_3_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2)) || any(!is.finite(mat)) || sum(mat) == 0 || any(rowSums(mat) == 0) || any(colSums(mat) == 0)) {
return(NULL)
}
result <- epitools::oddsratio(mat, method = "wald")
or <- result$measure[2, "estimate"]
lower <- result$measure[2, "lower"]
upper <- result$measure[2, "upper"]
pval_chisq <- if("chi.square" %in% colnames(result$p.value) && nrow(result$p.value) >= 2) {
result$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})MTX_24_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix), paste0("non-", prefix))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- CPG_TSS_status_df %>%
group_by(across(all_of(status_col)), MTX_sig_24) %>%
tally() %>%
pivot_wider(
names_from = MTX_sig_24,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
})
# Set names so you can easily refer to each status
names(MTX_24_status_matrices) <- status_columns
odds_ratio_results_MTX_24 <- map(MTX_24_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2)) || any(!is.finite(mat)) || sum(mat) == 0 || any(rowSums(mat) == 0) || any(colSums(mat) == 0)) {
return(NULL)
}
result <- epitools::oddsratio(mat, method = "wald")
or <- result$measure[2, "estimate"]
lower <- result$measure[2, "lower"]
upper <- result$measure[2, "upper"]
pval_chisq <- if("chi.square" %in% colnames(result$p.value) && nrow(result$p.value) >= 2) {
result$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})odds ratio results
col_fun_OR = colorRamp2(c(0,1,1.5,5), c("blueviolet","white","lightgreen","green3" ))
combined_df <- bind_rows(
map_dfr(odds_ratio_results_DOX_3, ~as.data.frame(.x), .id = "status") %>% mutate(source = "DOX_3hr"),
map_dfr(odds_ratio_results_DOX_24, ~as.data.frame(.x), .id = "status") %>% mutate(source = "DOX_24hr"),
map_dfr(odds_ratio_results_EPI_3, ~as.data.frame(.x), .id = "status") %>% mutate(source = "EPI_3hr"),
map_dfr(odds_ratio_results_EPI_24, ~as.data.frame(.x), .id = "status") %>% mutate(source = "EPI_24hr"),
map_dfr(odds_ratio_results_DNR_3, ~as.data.frame(.x), .id = "status") %>% mutate(source = "DNR_3hr"),
map_dfr(odds_ratio_results_DNR_24, ~as.data.frame(.x), .id = "status") %>% mutate(source = "DNR_24hr"),
map_dfr(odds_ratio_results_MTX_3, ~as.data.frame(.x), .id = "status") %>% mutate(source = "MTX_3hr"),
map_dfr(odds_ratio_results_MTX_24, ~as.data.frame(.x), .id = "status") %>% mutate(source = "MTX_24hr"))
sig_mat_OR <-combined_df %>%
dplyr::select( status,source,chi_sq_p) %>%
group_by(source) %>%
mutate(rank_val=rank(chi_sq_p, ties.method = "first")) %>%
mutate(BH_correction= p.adjust(chi_sq_p,method= "BH")) %>%
pivot_wider(., id_cols = status, names_from = source, values_from = BH_correction) %>%
column_to_rownames("status") %>%
as.matrix()
# saveRDS(combined_df,"data/Final_four_data/re_analysis/OR_results_TSS_CpG_df_1bp_ALLtrt.RDS")combined_df %>%
dplyr::select(status, source, odds_ratio) %>%
group_by(source) %>%
pivot_wider(., id_cols = status, names_from = source, values_from = odds_ratio) %>%
column_to_rownames("status") %>%
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))}})
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] utf8_1.2.6
[11] R6_2.6.1
[12] lazyeval_0.2.2
[13] GetoptLong_1.0.5
[14] urlchecker_1.0.1
[15] withr_3.0.2
[16] prettyunits_1.2.0
[17] cli_3.6.4
[18] formatR_1.14
[19] Cairo_1.6-2
[20] sass_0.4.9
[21] Rsamtools_2.22.0
[22] systemfonts_1.2.1
[23] yulab.utils_0.2.0
[24] foreign_0.8-88
[25] DOSE_4.0.1
[26] svglite_2.1.3
[27] R.utils_2.13.0
[28] sessioninfo_1.2.3
[29] plotrix_3.8-4
[30] BSgenome_1.74.0
[31] pwr_1.3-0
[32] impute_1.80.0
[33] rstudioapi_0.17.1
[34] RSQLite_2.3.9
[35] shape_1.4.6.1
[36] generics_0.1.4
[37] gridGraphics_0.5-1
[38] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[39] BiocIO_1.16.0
[40] vroom_1.6.5
[41] gtools_3.9.5
[42] car_3.1-3
[43] GO.db_3.20.0
[44] Matrix_1.7-3
[45] abind_1.4-8
[46] R.methodsS3_1.8.2
[47] lifecycle_1.0.4
[48] whisker_0.4.1
[49] yaml_2.3.10
[50] carData_3.0-5
[51] SummarizedExperiment_1.36.0
[52] gplots_3.2.0
[53] qvalue_2.38.0
[54] SparseArray_1.6.2
[55] BiocFileCache_2.14.0
[56] blob_1.2.4
[57] promises_1.3.2
[58] crayon_1.5.3
[59] miniUI_0.1.2
[60] ggtangle_0.0.6
[61] lattice_0.22-6
[62] cowplot_1.1.3
[63] KEGGREST_1.46.0
[64] magick_2.8.7
[65] ComplexHeatmap_2.22.0
[66] pillar_1.10.2
[67] knitr_1.50
[68] fgsea_1.32.2
[69] rjson_0.2.23
[70] boot_1.3-31
[71] codetools_0.2-20
[72] fastmatch_1.1-6
[73] glue_1.8.0
[74] getPass_0.2-4
[75] ggfun_0.1.8
[76] data.table_1.17.0
[77] remotes_2.5.0
[78] vctrs_0.6.5
[79] png_0.1-8
[80] treeio_1.30.0
[81] gtable_0.3.6
[82] cachem_1.1.0
[83] xfun_0.51
[84] S4Arrays_1.6.0
[85] mime_0.12
[86] iterators_1.0.14
[87] statmod_1.5.0
[88] ellipsis_0.3.2
[89] nlme_3.1-167
[90] ggtree_3.14.0
[91] bit64_4.6.0-1
[92] filelock_1.0.3
[93] progress_1.2.3
[94] rprojroot_2.0.4
[95] bslib_0.9.0
[96] rpart_4.1.24
[97] KernSmooth_2.23-26
[98] Hmisc_5.2-2
[99] colorspace_2.1-1
[100] DBI_1.2.3
[101] seqPattern_1.38.0
[102] nnet_7.3-20
[103] tidyselect_1.2.1
[104] processx_3.8.6
[105] bit_4.6.0
[106] compiler_4.4.2
[107] curl_6.2.1
[108] git2r_0.35.0
[109] httr2_1.1.2
[110] htmlTable_2.4.3
[111] xml2_1.3.7
[112] DelayedArray_0.32.0
[113] checkmate_2.3.2
[114] caTools_1.18.3
[115] callr_3.7.6
[116] rappdirs_0.3.3
[117] digest_0.6.37
[118] rmarkdown_2.29
[119] XVector_0.46.0
[120] base64enc_0.1-3
[121] htmltools_0.5.8.1
[122] pkgconfig_2.0.3
[123] MatrixGenerics_1.18.1
[124] dbplyr_2.5.0
[125] fastmap_1.2.0
[126] GlobalOptions_0.1.2
[127] rlang_1.1.5
[128] htmlwidgets_1.6.4
[129] UCSC.utils_1.2.0
[130] shiny_1.10.0
[131] farver_2.1.2
[132] jquerylib_0.1.4
[133] zoo_1.8-13
[134] jsonlite_1.9.1
[135] GOSemSim_2.32.0
[136] R.oo_1.27.1
[137] RCurl_1.98-1.16
[138] magrittr_2.0.3
[139] Formula_1.2-5
[140] GenomeInfoDbData_1.2.13
[141] ggplotify_0.1.2
[142] patchwork_1.3.0
[143] munsell_0.5.1
[144] Rcpp_1.0.14
[145] ape_5.8-1
[146] stringi_1.8.4
[147] zlibbioc_1.52.0
[148] plyr_1.8.9
[149] pkgbuild_1.4.8
[150] parallel_4.4.2
[151] Biostrings_2.74.1
[152] splines_4.4.2
[153] hms_1.1.3
[154] locfit_1.5-9.12
[155] ps_1.9.0
[156] igraph_2.1.4
[157] reshape2_1.4.4
[158] pkgload_1.4.0
[159] futile.options_1.0.1
[160] XML_3.99-0.18
[161] evaluate_1.0.3
[162] lambda.r_1.2.4
[163] foreach_1.5.2
[164] tzdb_0.4.0
[165] httpuv_1.6.15
[166] clue_0.3-66
[167] gridBase_0.4-7
[168] xtable_1.8-4
[169] restfulr_0.0.15
[170] tidytree_0.4.6
[171] rstatix_0.7.2
[172] later_1.4.1
[173] viridisLite_0.4.2
[174] aplot_0.2.6
[175] memoise_2.0.1
[176] GenomicAlignments_1.42.0
[177] cluster_2.1.8.1
[178] timechange_0.3.0