TE_analysis_DOX_DAR
Renee Matthews
2025-06-05
Last updated: 2025-06-18
Checks: 7 0
Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c78c973 | reneeisnowhere | 2025-06-18 | updates using all trts |
| html | af91ecf | reneeisnowhere | 2025-06-18 | Build site. |
| Rmd | 62bafc3 | reneeisnowhere | 2025-06-18 | adding 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)Loading repeatmasker data:
repeatmasker <- read.delim("data/other_papers/repeatmasker.tsv")Subsetting repeatmasker for analysis by class/family
reClass_list <- repeatmasker %>%
distinct(repClass)
Line_repeats <- repeatmasker %>%
dplyr::filter(repClass == "LINE") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
Sine_repeats <- repeatmasker %>%
dplyr::filter(repClass == "SINE") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
LTR_repeats <- repeatmasker %>%
dplyr::filter(repClass == "LTR") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
DNA_repeats <- repeatmasker %>%
dplyr::filter(repClass == "DNA") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
retroposon_repeats <- repeatmasker %>%
dplyr::filter(repClass == "Retroposon") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
all_TEs_gr <- repeatmasker %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)Initial overlaps of TEs vs all regions (155,557)
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)
Col_TSS_data_gr <- 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))Overlapping all peaks with TEs
overlap_TE_gr <- join_overlap_intersect(Col_TSS_data_gr,all_TEs_gr)
TE_peaks <- overlap_TE_gr %>%
as.data.frame() %>%
distinct(Peakid)
LINE_peaks <- overlap_TE_gr %>%
as.data.frame() %>%
dplyr::filter(repClass=="LINE") %>%
distinct(Peakid)
SINE_peaks <- overlap_TE_gr %>%
as.data.frame() %>%
dplyr::filter(repClass=="SINE") %>%
distinct(Peakid)
LTR_peaks <- overlap_TE_gr %>%
as.data.frame() %>%
dplyr::filter(repClass=="LTR") %>%
distinct(Peakid)
DNA_peaks <- overlap_TE_gr %>%
as.data.frame() %>%
dplyr::filter(repClass=="DNA") %>%
distinct(Peakid)
SVA_peaks <- overlap_TE_gr %>%
as.data.frame() %>%
dplyr::filter(repClass=="Retroposon") %>%
distinct(Peakid)
# join sig data by toptable information
all_results <- toptable_results %>%
imap(~ .x %>% tibble::rownames_to_column(var = "rowname") %>%
mutate(source = .y)) %>%
bind_rows()
annotated_peaks <- 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)) %>%
mutate(TE_status=if_else(genes %in% TE_peaks$Peakid,"TE_peak","not_TE_peak"),
LINE_status=if_else(genes %in% LINE_peaks$Peakid,"LINE_peak","not_LINE_peak"),
SINE_status=if_else(genes %in% SINE_peaks$Peakid,"SINE_peak","not_SINE_peak"),
LTR_status=if_else(genes %in% LTR_peaks$Peakid,"LTR_peak","not_LTR_peak"),
DNA_status=if_else(genes %in% DNA_peaks$Peakid,"DNA_peak","not_DNA_peak"),
SVA_status=if_else(genes %in% SVA_peaks$Peakid,"SVA_peak","not_SVA_peak")) %>%
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("genes"="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("genes"="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("genes"="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"))) DOX_3_TE_mat <- annotated_peaks %>%
group_by(TE_status,DOX_sig_3) %>%
tally() %>%
pivot_wider(., id_cols=TE_status,names_from = DOX_sig_3,values_from = n) %>%
column_to_rownames("TE_status") %>%
as.matrix()From the code above, #### DOX
# Vector of status-type column names in your data
status_columns <- c("TE_status", "SINE_status", "LINE_status", "LTR_status", "DNA_status", "SVA_status")
# Create a list of matrices, named by status type
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$", "", status_col)
expected_rows <- c(paste0(prefix, "_peak"), paste0("not_", prefix, "_peak"))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- annotated_peaks %>%
group_by(across(all_of(status_col)), DOX_sig_3) %>%
tally() %>%
pivot_wider(
names_from = DOX_sig_3,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
print(mat)
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
}) sig not_sig
TE_peak 2316 101833
not_TE_peak 1157 50251
sig not_sig
SINE_peak 1028 41766
not_SINE_peak 2445 110318
sig not_sig
LINE_peak 780 33372
not_LINE_peak 2693 118712
sig not_sig
LTR_peak 407 22282
not_LTR_peak 3066 129802
sig not_sig
DNA_peak 297 14796
not_DNA_peak 3176 137288
sig not_sig
SVA_peak 3 288
not_SVA_peak 3470 151796# Set names so you can easily refer to each status
names(DOX_3_status_matrices) <- status_columns
odds_ratio_results_DOX_3 <- map(DOX_3_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2))) return(NULL)
result <- epitools::oddsratio(mat, method = "wald")
or <- result$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$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})# Create a list of matrices, named by status type
DOX_24_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix, "_peak"), paste0("not_", prefix, "_peak"))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- annotated_peaks %>%
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()
print(mat)
# 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]
}) sig not_sig
TE_peak 44303 59846
not_TE_peak 20517 30891
sig not_sig
SINE_peak 19128 23666
not_SINE_peak 45692 67071
sig not_sig
LINE_peak 15208 18944
not_LINE_peak 49612 71793
sig not_sig
LTR_peak 9971 12718
not_LTR_peak 54849 78019
sig not_sig
DNA_peak 6778 8315
not_DNA_peak 58042 82422
sig not_sig
SVA_peak 79 212
not_SVA_peak 64741 90525# 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))) 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
# Vector of status-type column names in your data
status_columns <- c("TE_status", "SINE_status", "LINE_status", "LTR_status", "DNA_status", "SVA_status")
# Create a list of matrices, named by status type
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$", "", status_col)
expected_rows <- c(paste0(prefix, "_peak"), paste0("not_", prefix, "_peak"))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- annotated_peaks %>%
group_by(across(all_of(status_col)), EPI_sig_3) %>%
tally() %>%
pivot_wider(
names_from = EPI_sig_3,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
print(mat)
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
}) sig not_sig
TE_peak 9688 94461
not_TE_peak 4546 46862
sig not_sig
SINE_peak 4196 38598
not_SINE_peak 10038 102725
sig not_sig
LINE_peak 3031 31121
not_LINE_peak 11203 110202
sig not_sig
LTR_peak 1590 21099
not_LTR_peak 12644 120224
sig not_sig
DNA_peak 1265 13828
not_DNA_peak 12969 127495
sig not_sig
SVA_peak 32 259
not_SVA_peak 14202 141064# Set names so you can easily refer to each status
names(EPI_3_status_matrices) <- status_columns
odds_ratio_results_EPI_3 <- map(EPI_3_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2))) return(NULL)
result <- epitools::oddsratio(mat, method = "wald")
or <- result$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$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})# Create a list of matrices, named by status type
EPI_24_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix, "_peak"), paste0("not_", prefix, "_peak"))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- annotated_peaks %>%
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()
print(mat)
# 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]
}) sig not_sig
TE_peak 45302 58847
not_TE_peak 21199 30209
sig not_sig
SINE_peak 19441 23353
not_SINE_peak 47060 65703
sig not_sig
LINE_peak 15530 18622
not_LINE_peak 50971 70434
sig not_sig
LTR_peak 10179 12510
not_LTR_peak 56322 76546
sig not_sig
DNA_peak 6903 8190
not_DNA_peak 59598 80866
sig not_sig
SVA_peak 105 186
not_SVA_peak 66396 88870# 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))) 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
# Vector of status-type column names in your data
status_columns <- c("TE_status", "SINE_status", "LINE_status", "LTR_status", "DNA_status", "SVA_status")
# Create a list of matrices, named by status type
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$", "", status_col)
expected_rows <- c(paste0(prefix, "_peak"), paste0("not_", prefix, "_peak"))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- annotated_peaks %>%
group_by(across(all_of(status_col)), DNR_sig_3) %>%
tally() %>%
pivot_wider(
names_from = DNR_sig_3,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
print(mat)
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
}) sig not_sig
TE_peak 15675 88474
not_TE_peak 7063 44345
sig not_sig
SINE_peak 6517 36277
not_SINE_peak 16221 96542
sig not_sig
LINE_peak 4652 29500
not_LINE_peak 18086 103319
sig not_sig
LTR_peak 2529 20160
not_LTR_peak 20209 112659
sig not_sig
DNA_peak 1923 13170
not_DNA_peak 20815 119649
sig not_sig
SVA_peak 56 235
not_SVA_peak 22682 132584# Set names so you can easily refer to each status
names(DNR_3_status_matrices) <- status_columns
odds_ratio_results_DNR_3 <- map(DNR_3_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2))) return(NULL)
result <- epitools::oddsratio(mat, method = "wald")
or <- result$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$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})# Create a list of matrices, named by status type
DNR_24_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix, "_peak"), paste0("not_", prefix, "_peak"))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- annotated_peaks %>%
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()
print(mat)
# 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]
}) sig not_sig
TE_peak 54566 49583
not_TE_peak 25429 25979
sig not_sig
SINE_peak 22919 19875
not_SINE_peak 57076 55687
sig not_sig
LINE_peak 18231 15921
not_LINE_peak 61764 59641
sig not_sig
LTR_peak 12018 10671
not_LTR_peak 67977 64891
sig not_sig
DNA_peak 8057 7036
not_DNA_peak 71938 68526
sig not_sig
SVA_peak 122 169
not_SVA_peak 79873 75393# 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))) 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
# Vector of status-type column names in your data
status_columns <- c("TE_status", "SINE_status", "LINE_status", "LTR_status", "DNA_status", "SVA_status")
# Create a list of matrices, named by status type
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$", "", status_col)
expected_rows <- c(paste0(prefix, "_peak"), paste0("not_", prefix, "_peak"))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- annotated_peaks %>%
group_by(across(all_of(status_col)), MTX_sig_3) %>%
tally() %>%
pivot_wider(
names_from = MTX_sig_3,
values_from = n,
values_fill = list(n = 0)
) %>%
column_to_rownames(var = status_col) %>%
as.matrix()
print(mat)
# Fill missing expected rows
for (r in setdiff(expected_rows, rownames(mat))) {
mat <- rbind(mat, setNames(rep(0, length(expected_cols)), expected_cols))
rownames(mat)[nrow(mat)] <- r
}
# Fill missing expected columns
for (c in setdiff(expected_cols, colnames(mat))) {
mat <- cbind(mat, setNames(rep(0, nrow(mat)), c))
}
# Order
mat <- mat[expected_rows, expected_cols, drop = FALSE]
}) sig not_sig
TE_peak 568 103581
not_TE_peak 236 51172
sig not_sig
SINE_peak 220 42574
not_SINE_peak 584 112179
sig not_sig
LINE_peak 174 33978
not_LINE_peak 630 120775
sig not_sig
LTR_peak 114 22575
not_LTR_peak 690 132178
sig not_sig
DNA_peak 86 15007
not_DNA_peak 718 139746
sig not_sig
SVA_peak 1 290
not_SVA_peak 803 154463# Set names so you can easily refer to each status
names(MTX_3_status_matrices) <- status_columns
odds_ratio_results_MTX_3 <- map(MTX_3_status_matrices, function(mat) {
if (!all(dim(mat) == c(2, 2))) return(NULL)
result <- epitools::oddsratio(mat, method = "wald")
or <- result$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$p.value[2, "chi.square"]
} else {
NA_real_
}
list(
odds_ratio = or,
lower_ci = lower,
upper_ci = upper,
chi_sq_p = pval_chisq
)
})# Create a list of matrices, named by status type
MTX_24_status_matrices <- map(status_columns, function(status_col) {
# Extract prefix (e.g., "TE", "SINE") from column name like "TE_status"
prefix <- sub("_status$", "", status_col)
expected_rows <- c(paste0(prefix, "_peak"), paste0("not_", prefix, "_peak"))
expected_cols <- c("sig", "not_sig")
# Build matrix
mat <- annotated_peaks %>%
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()
print(mat)
# 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]
}) sig not_sig
TE_peak 16922 87227
not_TE_peak 7328 44080
sig not_sig
SINE_peak 7114 35680
not_SINE_peak 17136 95627
sig not_sig
LINE_peak 5596 28556
not_LINE_peak 18654 102751
sig not_sig
LTR_peak 3564 19125
not_LTR_peak 20686 112182
sig not_sig
DNA_peak 2445 12648
not_DNA_peak 21805 118659
sig not_sig
SVA_peak 17 274
not_SVA_peak 24233 131033# 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))) 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
)
})Combining the data and applying BH pvalue adjustments
# odds_ratio_results_DOX_3
# odds_ratio_results_DOX_24
# odds_ratio_results_DOX_24_sig_up
# odds_ratio_results_DOX_24_sig_down
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")
)
# saveRDS(combined_df,"data/Final_four_data/re_analysis/OR_results_TE_df_1bp_alltrt.RDS")TE_sig_mat <- 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()
col_fun_OR = colorRamp2(c(0,1,1.5,5), c("blueviolet","white","lightgreen","green3" ))
# TE_od_mat <-
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(TE_sig_mat[i, j]) && TE_sig_mat[i, j] < 0.05 && .[i, j] > 1) {
grid.text("*", x, y, gp = gpar(fontsize = 20))}})
| Version | Author | Date |
|---|---|---|
| af91ecf | reneeisnowhere | 2025-06-18 |
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_4.0.1
[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_0.1.4
[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_0.1.2
[59] ggtangle_0.0.6
[60] lattice_0.22-6
[61] cowplot_1.1.3
[62] KEGGREST_1.46.0
[63] magick_2.8.7
[64] ComplexHeatmap_2.22.0
[65] pillar_1.10.2
[66] knitr_1.50
[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_1.1.2
[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_1.27.1
[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_1.4.8
[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_0.2.6
[174] memoise_2.0.1
[175] GenomicAlignments_1.42.0
[176] cluster_2.1.8.1
[177] timechange_0.3.0