Peak Cut Off
Steven Yu
2025-07-14
Last updated: 2025-08-12
Checks: 7 0
Knit directory: DXR_continue/
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Peak Anaylsis
Loading Packages
library(tidyverse)
library(readr)
library(edgeR)
library(ComplexHeatmap)
library(data.table)
library(dplyr)
library(stringr)
library(ggplot2)
library(viridis)
library(DT)
library(kableExtra)
library(genomation)
library(GenomicRanges)
library(chromVAR) ## For FRiP analysis and differential analysis
library(DESeq2) ## For differential analysis section
library(ggpubr) ## For customizing figures
library(corrplot) ## For correlation plot
library(ggpmisc)
library(gcplyr)
library(Rsubread)Data Initialization
sampleinfo <- read_delim("data/sample_info.tsv", delim = "\t")Functions
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
pca_plot <-
function(df,
col_var = NULL,
shape_var = NULL,
title = "") {
ggplot(df) + geom_point(aes_string(
x = "PC1",
y = "PC2",
color = col_var,
shape = shape_var
),
size = 5) +
labs(title = title, x = "PC 1", y = "PC 2") +
scale_color_manual(values = c(
"#8B006D",
"#DF707E",
"#F1B72B",
"#3386DD",
"#707031",
"#41B333"
))
}
pca_var_plot <- function(pca) {
# x: class == prcomp
pca.var <- pca$sdev ^ 2
pca.prop <- pca.var / sum(pca.var)
var.plot <-
qplot(PC, prop, data = data.frame(PC = 1:length(pca.prop),
prop = pca.prop)) +
labs(title = 'Variance contributed by each PC',
x = 'PC', y = 'Proportion of variance')
plot(var.plot)
}
calc_pca <- function(x) {
# Performs principal components analysis with prcomp
# x: a sample-by-gene numeric matrix
prcomp(x, scale. = TRUE, retx = TRUE)
}
get_regr_pval <- function(mod) {
# Returns the p-value for the Fstatistic of a linear model
# mod: class lm
stopifnot(class(mod) == "lm")
fstat <- summary(mod)$fstatistic
pval <- 1 - pf(fstat[1], fstat[2], fstat[3])
return(pval)
}
plot_versus_pc <- function(df, pc_num, fac) {
# df: data.frame
# pc_num: numeric, specific PC for plotting
# fac: column name of df for plotting against PC
pc_char <- paste0("PC", pc_num)
# Calculate F-statistic p-value for linear model
pval <- get_regr_pval(lm(df[, pc_char] ~ df[, fac]))
if (is.numeric(df[, f])) {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_point() +
geom_smooth(method = "lm") + labs(title = sprintf("p-val: %.2f", pval))
} else {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_boxplot() +
labs(title = sprintf("p-val: %.2f", pval))
}
}
x_axis_labels = function(labels, every_nth = 1, ...) {
axis(side = 1,
at = seq_along(labels),
labels = F)
text(
x = (seq_along(labels))[seq_len(every_nth) == 1],
y = par("usr")[3] - 0.075 * (par("usr")[4] - par("usr")[3]),
labels = labels[seq_len(every_nth) == 1],
xpd = TRUE,
...
)
}Peak Widths
Peak Width Function
histone_list <- c("H3K27ac", "H3K27me3", "H3K9me3", "H3K36me3")
get_peak_widths_long_split <- function(histone, var) {
path <- file.path("data/peaks", histone)
if (var == "all") {
file_list = list.files(path = path, pattern = "FINAL_merged.bed", full.names = TRUE)
} else if (histone == "H3K27ac"){
file_list = list.files(path = path, pattern = "picard_peaks.narrowPeak$", full.names = TRUE)
} else {
file_list = list.files(path = path, pattern = paste0(var,"_peaks.(narrowPeak|broadPeak)$", sep=""), full.names = TRUE)
}
process_peak <- function(file_path, label) {
filename <- basename(file_path)
peak_df <- fread(file_path, header = FALSE) %>%
transmute(
row = row_number(),
width = abs(V3 - V2),
file = paste0(histone, "-", filename),
group = label
)
return(peak_df)
}
peak_list <- list(map(file_list, process_peak, label = var)) %>% flatten()
bind_rows(peak_list)
}Broad Visualization
all_peak_widths <- purrr::map_df(histone_list, get_peak_widths_long_split, var = "picard_broad")
anno_peak_width_long <- all_peak_widths %>%
mutate(file= gsub("_picard_broad_peaks.broadPeak","",file)) %>%
separate_wider_delim(., cols=file, delim="-",names=c("histone","sample")) %>%
left_join(sampleinfo, by =c("sample"="Library ID"))
anno_peak_width_long %>%
# distinct(histone)
ggplot(.,aes(x=histone, y = width, fill = histone))+
geom_violin()+
# scale_fill_viridis_a(discrete = TRUE, begin = 0.1, end = 0.55, option = "magma", alpha = 0.8) +
# scale_color_viridis_a(discrete = TRUE, begin = 0.1, end = 0.9) +
scale_y_continuous(trans = "log", breaks = c(400, 3000, 22000)) +
theme_bw(base_size = 18) +
ylab("Width of Peaks") +
xlab("")+
ggtitle("Width of all peaks with Broad Peaks")
| Version | Author | Date |
|---|---|---|
| fee3a00 | infurnoheat | 2025-07-30 |
Broad as Narrow Visualization
all_peak_widths <- purrr::map_df(histone_list, get_peak_widths_long_split, var = "picard_narrow")
anno_peak_width_long <- all_peak_widths %>%
mutate(file= gsub("_picard_narrow_peaks.narrowPeak","",file)) %>%
separate_wider_delim(., cols=file, delim="-",names=c("histone","sample")) %>%
left_join(sampleinfo, by =c("sample"="Library ID"))
anno_peak_width_long %>%
# distinct(histone)
ggplot(.,aes(x=histone, y = width, fill = histone))+
geom_violin()+
# scale_fill_viridis_a(discrete = TRUE, begin = 0.1, end = 0.55, option = "magma", alpha = 0.8) +
# scale_color_viridis_a(discrete = TRUE, begin = 0.1, end = 0.9) +
scale_y_continuous(trans = "log", breaks = c(400, 3000, 22000)) +
theme_bw(base_size = 18) +
ylab("Width of Peaks") +
xlab("")+
ggtitle("Width of all peaks with Broad as Narrow")
| Version | Author | Date |
|---|---|---|
| fee3a00 | infurnoheat | 2025-07-30 |
Broad as Narrow Stringent Visualization
all_peak_widths <- purrr::map_df(histone_list, get_peak_widths_long_split, var = "1e3_narrow")
anno_peak_width_long <- all_peak_widths %>%
mutate(file= gsub("_1e3_narrow_peaks.narrowPeak","",file)) %>%
separate_wider_delim(., cols=file, delim="-",names=c("histone","sample")) %>%
left_join(sampleinfo, by =c("sample"="Library ID"))
anno_peak_width_long %>%
# distinct(histone)
ggplot(.,aes(x=histone, y = width, fill = histone))+
geom_violin()+
# scale_fill_viridis_a(discrete = TRUE, begin = 0.1, end = 0.55, option = "magma", alpha = 0.8) +
# scale_color_viridis_a(discrete = TRUE, begin = 0.1, end = 0.9) +
scale_y_continuous(trans = "log", breaks = c(400, 3000, 22000)) +
theme_bw(base_size = 18) +
ylab("Width of Peaks") +
xlab("")+
ggtitle("Width of all peaks with Broad as Stringent Narrow")
| Version | Author | Date |
|---|---|---|
| fee3a00 | infurnoheat | 2025-07-30 |
Broad as Stringent Narrow without Picard
all_peak_widths <- purrr::map_df(histone_list, get_peak_widths_long_split, var = "FINAL")
anno_peak_width_long <- all_peak_widths %>%
mutate(file= gsub("_picard_narrow_peaks.narrowPeak","",file)) %>%
separate_wider_delim(., cols=file, delim="-",names=c("histone","sample")) %>%
left_join(sampleinfo, by =c("sample"="Library ID"))
anno_peak_width_long %>%
# distinct(histone)
ggplot(.,aes(x=histone, y = width, fill = histone))+
geom_violin()+
# scale_fill_viridis_a(discrete = TRUE, begin = 0.1, end = 0.55, option = "magma", alpha = 0.8) +
# scale_color_viridis_a(discrete = TRUE, begin = 0.1, end = 0.9) +
scale_y_continuous(trans = "log", breaks = c(400, 3000, 22000)) +
theme_bw(base_size = 18) +
ylab("Width of Peaks") +
xlab("")+
ggtitle("0.001 Narrow Without Picard")
| Version | Author | Date |
|---|---|---|
| fee3a00 | infurnoheat | 2025-07-30 |
all_peak_widths <- purrr::map_df(histone_list, get_peak_widths_long_split, var = "all")
anno_peak_width_long <- all_peak_widths %>%
mutate(file= gsub("_FINAL_merged.bed","",file)) %>%
separate_wider_delim(., cols=file, delim="-",names=c("histone","sample")) %>%
left_join(sampleinfo, by =c("sample"="Library ID"))
anno_peak_width_long %>%
# distinct(histone)
ggplot(.,aes(x=histone, y = width, fill = histone))+
geom_violin()+
# scale_fill_viridis_a(discrete = TRUE, begin = 0.1, end = 0.55, option = "magma", alpha = 0.8) +
# scale_color_viridis_a(discrete = TRUE, begin = 0.1, end = 0.9) +
scale_y_continuous(trans = "log", breaks = c(400, 3000, 22000)) +
theme_bw(base_size = 18) +
ylab("Width of Peaks") +
xlab("")+
ggtitle("Merged")
| Version | Author | Date |
|---|---|---|
| fee3a00 | infurnoheat | 2025-07-30 |
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] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] Rsubread_2.20.0 gcplyr_1.12.0
[3] ggpmisc_0.6.2 ggpp_0.5.9
[5] corrplot_0.95 ggpubr_0.6.1
[7] DESeq2_1.46.0 SummarizedExperiment_1.36.0
[9] Biobase_2.66.0 MatrixGenerics_1.18.1
[11] matrixStats_1.5.0 chromVAR_1.28.0
[13] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
[15] IRanges_2.40.1 S4Vectors_0.44.0
[17] BiocGenerics_0.52.0 genomation_1.38.0
[19] kableExtra_1.4.0 DT_0.33
[21] viridis_0.6.5 viridisLite_0.4.2
[23] data.table_1.17.8 ComplexHeatmap_2.22.0
[25] edgeR_4.4.2 limma_3.62.2
[27] lubridate_1.9.4 forcats_1.0.0
[29] stringr_1.5.1 dplyr_1.1.4
[31] purrr_1.1.0 readr_2.1.5
[33] tidyr_1.3.1 tibble_3.3.0
[35] ggplot2_3.5.2 tidyverse_2.0.0
[37] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.4.2 later_1.4.2
[3] BiocIO_1.16.0 bitops_1.0-9
[5] R.oo_1.27.1 XML_3.99-0.18
[7] DirichletMultinomial_1.48.0 lifecycle_1.0.4
[9] rstatix_0.7.2 pwalign_1.2.0
[11] doParallel_1.0.17 rprojroot_2.1.0
[13] vroom_1.6.5 MASS_7.3-65
[15] processx_3.8.6 lattice_0.22-7
[17] backports_1.5.0 magrittr_2.0.3
[19] plotly_4.11.0 sass_0.4.10
[21] rmarkdown_2.29 jquerylib_0.1.4
[23] yaml_2.3.10 plotrix_3.8-4
[25] httpuv_1.6.16 DBI_1.2.3
[27] CNEr_1.42.0 RColorBrewer_1.1-3
[29] abind_1.4-8 zlibbioc_1.52.0
[31] R.utils_2.13.0 RCurl_1.98-1.17
[33] git2r_0.36.2 circlize_0.4.16
[35] GenomeInfoDbData_1.2.13 seqLogo_1.72.0
[37] MatrixModels_0.5-4 annotate_1.84.0
[39] svglite_2.2.1 codetools_0.2-20
[41] DelayedArray_0.32.0 xml2_1.3.8
[43] tidyselect_1.2.1 shape_1.4.6.1
[45] UCSC.utils_1.2.0 farver_2.1.2
[47] GenomicAlignments_1.42.0 jsonlite_2.0.0
[49] GetoptLong_1.0.5 Formula_1.2-5
[51] survival_3.8-3 iterators_1.0.14
[53] systemfonts_1.2.3 foreach_1.5.2
[55] tools_4.4.2 TFMPvalue_0.0.9
[57] Rcpp_1.1.0 glue_1.8.0
[59] gridExtra_2.3 SparseArray_1.6.2
[61] xfun_0.52 withr_3.0.2
[63] fastmap_1.2.0 SparseM_1.84-2
[65] callr_3.7.6 caTools_1.18.3
[67] digest_0.6.37 timechange_0.3.0
[69] R6_2.6.1 mime_0.13
[71] seqPattern_1.38.0 textshaping_1.0.1
[73] colorspace_2.1-1 GO.db_3.20.0
[75] gtools_3.9.5 poweRlaw_1.0.0
[77] dichromat_2.0-0.1 RSQLite_2.4.2
[79] R.methodsS3_1.8.2 generics_0.1.4
[81] rtracklayer_1.66.0 httr_1.4.7
[83] htmlwidgets_1.6.4 S4Arrays_1.6.0
[85] TFBSTools_1.44.0 whisker_0.4.1
[87] pkgconfig_2.0.3 gtable_0.3.6
[89] blob_1.2.4 impute_1.80.0
[91] XVector_0.46.0 htmltools_0.5.8.1
[93] carData_3.0-5 clue_0.3-66
[95] scales_1.4.0 png_0.1-8
[97] knitr_1.50 rstudioapi_0.17.1
[99] tzdb_0.5.0 reshape2_1.4.4
[101] rjson_0.2.23 curl_6.4.0
[103] cachem_1.1.0 GlobalOptions_0.1.2
[105] KernSmooth_2.23-26 parallel_4.4.2
[107] miniUI_0.1.2 AnnotationDbi_1.68.0
[109] restfulr_0.0.16 pillar_1.11.0
[111] vctrs_0.6.5 promises_1.3.3
[113] car_3.1-3 xtable_1.8-4
[115] cluster_2.1.8.1 evaluate_1.0.4
[117] cli_3.6.5 locfit_1.5-9.12
[119] compiler_4.4.2 Rsamtools_2.22.0
[121] rlang_1.1.6 crayon_1.5.3
[123] ggsignif_0.6.4 ps_1.9.1
[125] getPass_0.2-4 plyr_1.8.9
[127] fs_1.6.6 stringi_1.8.7
[129] gridBase_0.4-7 BiocParallel_1.40.2
[131] Biostrings_2.74.1 lazyeval_0.2.2
[133] quantreg_6.1 Matrix_1.7-3
[135] BSgenome_1.74.0 hms_1.1.3
[137] bit64_4.6.0-1 KEGGREST_1.46.0
[139] statmod_1.5.0 shiny_1.11.1
[141] broom_1.0.9 memoise_2.0.1
[143] bslib_0.9.0 bit_4.6.0
[145] polynom_1.4-1