Peak Calling cutoff (TOP2B and P53)

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Last updated: 2025-08-24

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Knit directory: ChIPSeq_project/

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📌 TOP2B broad peaks (before deduplication)

library(tidyverse)

Warning: package 'tidyverse' was built under R version 4.3.2

Warning: package 'tidyr' was built under R version 4.3.3

Warning: package 'readr' was built under R version 4.3.3

Warning: package 'purrr' was built under R version 4.3.3

Warning: package 'dplyr' was built under R version 4.3.2

Warning: package 'stringr' was built under R version 4.3.2

Warning: package 'lubridate' was built under R version 4.3.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.2     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(readr)
library(scales)   # <- needed for label_number()

Warning: package 'scales' was built under R version 4.3.2

Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

# --- metadata ---
metadata <- tribble(
  ~Sample,           ~Sample_Det,
  "MCW_SP_ChIP27",   "Ind1_VEH_TOP2B",
  "MCW_SP_ChIP28",   "Ind1_DOX_TOP2B",
  "MCW_SP_ChIP31",   "Ind2_VEH_TOP2B",
  "MCW_SP_ChIP32",   "Ind2_DOX_TOP2B",
  "MCW_SP_ChIP39",   "Ind3_VEH_TOP2B",
  "MCW_SP_ChIP40",   "Ind3_DOX_TOP2B",
  "MCW_SP_ChIP43",   "Ind4_VEH_TOP2B",
  "MCW_SP_ChIP44",   "Ind4_DOX_TOP2B",
  "MCW_SP_ChIP51",   "Ind5_VEH_TOP2B",
  "MCW_SP_ChIP52",   "Ind5_DOX_TOP2B",
  "MCW_SP_ChIP55",   "Ind6_VEH_TOP2B",
  "MCW_SP_ChIP56",   "Ind6_DOX_TOP2B"
) |> mutate(
  Sample     = factor(Sample,     levels = Sample),
  Sample_Det = factor(Sample_Det, levels = Sample_Det)
)

# --- load all cutoff analysis files ---
files <- list.files("data/macs3_broad_out_TOP2B",
                    pattern = "_cutoff_analysis\\.txt$", full.names = TRUE)

df <- files |>
  set_names() |>
  map_dfr(~ read_delim(.x, delim = "\t", show_col_types = FALSE), .id = "filepath") |>
  mutate(Sample = basename(filepath) |> str_remove("_cutoff_analysis\\.txt")) |>
  left_join(metadata, by = "Sample") |>
  mutate(Sample_Det = factor(Sample_Det, levels = levels(metadata$Sample_Det))) |>
  arrange(Sample_Det, qscore)

# Common x-scale (guarantees tick at 0)
x_fixed <- scale_x_continuous(
  limits = c(0, 7),
  breaks = 0:7,
  labels = as.character(0:7),
  expand = c(0, 0)
)

# ---- Plot with linear y-axis ----
p_linear <- ggplot(df, aes(qscore, npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  x_fixed +
  scale_y_continuous(labels = scales::label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (linear)") +
  theme_minimal(base_size = 12) +
  theme(strip.text = element_text(face = "bold", size = 10),
        panel.grid.minor = element_blank())

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

# ---- Plot with log10 y-axis ----
p_log <- df |>
  mutate(npeaks = ifelse(npeaks <= 0, NA, npeaks)) |>
  ggplot(aes(qscore, npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  x_fixed +
  scale_y_log10(labels = scales::label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (log10)") +
  theme_minimal(base_size = 12) +
  theme(strip.text = element_text(face = "bold", size = 10),
        panel.grid.minor = element_blank())

# ---- Print both ----
p_linear

p_log

📌 TOP2B narrow peaks (before deduplication)

library(tidyverse)
library(readr)

# --- metadata ---
metadata <- tribble(
  ~Sample,           ~Sample_Det,
  "MCW_SP_ChIP27",   "Ind1_VEH_TOP2B",
  "MCW_SP_ChIP28",   "Ind1_DOX_TOP2B",
  "MCW_SP_ChIP31",   "Ind2_VEH_TOP2B",
  "MCW_SP_ChIP32",   "Ind2_DOX_TOP2B",
  "MCW_SP_ChIP39",   "Ind3_VEH_TOP2B",
  "MCW_SP_ChIP40",   "Ind3_DOX_TOP2B",
  "MCW_SP_ChIP43",   "Ind4_VEH_TOP2B",
  "MCW_SP_ChIP44",   "Ind4_DOX_TOP2B",
  "MCW_SP_ChIP51",   "Ind5_VEH_TOP2B",
  "MCW_SP_ChIP52",   "Ind5_DOX_TOP2B",
  "MCW_SP_ChIP55",   "Ind6_VEH_TOP2B",
  "MCW_SP_ChIP56",   "Ind6_DOX_TOP2B"
)

# --- load all cutoff analysis files ---
files <- list.files("data/macs3_narrow_out_TOP2B", pattern = "_cutoff_analysis\\.txt$", full.names = TRUE)

df <- files %>%
  set_names() %>%
  map_dfr(~ read_delim(.x, delim = "\t", show_col_types = FALSE), .id = "filepath") %>%
  mutate(Sample = basename(filepath) %>% str_remove("_cutoff_analysis\\.txt")) %>%
  left_join(metadata, by = "Sample")

# ---- Plot with linear y-axis ----
p_linear <- df %>%
  ggplot(aes(x = qscore, y = npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  scale_x_continuous(
    limits = c(0, 7),
    breaks = 0:7,
    labels = as.character(0:7),   # ensures "0" shows up
    expand = c(0, 0)
  ) +
  scale_y_continuous(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (linear)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- Plot with log10 y-axis ----
p_log <- df %>%
  ggplot(aes(x = qscore, y = npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  scale_x_continuous(
    limits = c(0, 7),
    breaks = 0:7,
    labels = as.character(0:7),
    expand = c(0, 0)
  ) +
  scale_y_log10(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (log10)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- Print both ----
p_linear

p_log

📌 P53 narrow peaks (before deduplication)

library(tidyverse)
library(readr)
library(scales)

# --- metadata for p53 ---
metadata_p53 <- tribble(
  ~Sample,           ~Sample_Det,
  "MCW_SP_ChIP29",   "Ind1_VEH_p53",
  "MCW_SP_ChIP30",   "Ind1_DOX_p53",
  "MCW_SP_ChIP33",   "Ind2_VEH_p53",
  "MCW_SP_ChIP34",   "Ind2_DOX_p53",
  "MCW_SP_ChIP41",   "Ind3_VEH_p53",
  "MCW_SP_ChIP42",   "Ind3_DOX_p53",
  "MCW_SP_ChIP45",   "Ind4_VEH_p53",
  "MCW_SP_ChIP46",   "Ind4_DOX_p53",
  "MCW_SP_ChIP53",   "Ind5_VEH_p53",
  "MCW_SP_ChIP54",   "Ind5_DOX_p53",
  "MCW_SP_ChIP57",   "Ind6_VEH_p53",
  "MCW_SP_ChIP58",   "Ind6_DOX_p53"
) %>%
  mutate(
    Sample     = factor(Sample,     levels = Sample),
    Sample_Det = factor(Sample_Det, levels = Sample_Det)
  )

# --- load all cutoff-analysis files (edit path if needed) ---
data_dir <- "data/macs3_narrow_out_P53"   # <— change if your folder name differs
files <- list.files(data_dir, pattern = "_cutoff_analysis\\.txt$", full.names = TRUE)

df_p53 <- files %>%
  set_names() %>%
  map_dfr(~ read_delim(.x, delim = "\t", show_col_types = FALSE), .id = "filepath") %>%
  mutate(Sample = basename(filepath) %>% str_remove("_cutoff_analysis\\.txt")) %>%
  left_join(metadata_p53, by = "Sample") %>%
  mutate(Sample_Det = factor(Sample_Det, levels = levels(metadata_p53$Sample_Det))) %>%
  arrange(Sample_Det, qscore)

# --------- common x scale (forces 0..7 with a printed 0) ----------
x_fixed <- scale_x_continuous(
  limits = c(0, 7),
  breaks = 0:7,
  labels = as.character(0:7),
  expand = c(0, 0)
)

# ---- linear y ----
p_linear_p53 <- ggplot(df_p53, aes(qscore, npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  x_fixed +
  scale_y_continuous(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (linear)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- log10 y ----
p_log_p53 <- ggplot(df_p53, aes(qscore, npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  x_fixed +
  scale_y_log10(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (log10)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- show both ----
p_linear_p53

p_log_p53

📌 TOP2B broad peaks (after deduplication)

library(tidyverse)
library(readr)

# --- metadata ---
metadata <- tribble(
  ~Sample,           ~Sample_Det,
  "MCW_SP_ChIP27",   "Ind1_VEH_TOP2B",
  "MCW_SP_ChIP28",   "Ind1_DOX_TOP2B",
  "MCW_SP_ChIP31",   "Ind2_VEH_TOP2B",
  "MCW_SP_ChIP32",   "Ind2_DOX_TOP2B",
  "MCW_SP_ChIP39",   "Ind3_VEH_TOP2B",
  "MCW_SP_ChIP40",   "Ind3_DOX_TOP2B",
  "MCW_SP_ChIP43",   "Ind4_VEH_TOP2B",
  "MCW_SP_ChIP44",   "Ind4_DOX_TOP2B",
  "MCW_SP_ChIP51",   "Ind5_VEH_TOP2B",
  "MCW_SP_ChIP52",   "Ind5_DOX_TOP2B",
  "MCW_SP_ChIP55",   "Ind6_VEH_TOP2B",
  "MCW_SP_ChIP56",   "Ind6_DOX_TOP2B"
)

# --- load all cutoff analysis files ---
files <- list.files("data/macs3_broad_out_TOP2B_dedup", pattern = "_cutoff_analysis\\.txt$", full.names = TRUE)

df <- files %>%
  set_names() %>%
  map_dfr(~ read_delim(.x, delim = "\t", show_col_types = FALSE), .id = "filepath") %>%
  mutate(Sample = basename(filepath) %>% str_remove("_cutoff_analysis\\.txt")) %>%
  left_join(metadata, by = "Sample")

# ---- Plot with linear y-axis ----
p_linear <- df %>%
  ggplot(aes(x = qscore, y = npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  scale_x_continuous(
    limits = c(0, 7),
    breaks = 0:7,
    labels = as.character(0:7),   # ensures "0" shows up
    expand = c(0, 0)
  ) +
  scale_y_continuous(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (linear)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- Plot with log10 y-axis ----
p_log <- df %>%
  ggplot(aes(x = qscore, y = npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  scale_x_continuous(
    limits = c(0, 7),
    breaks = 0:7,
    labels = as.character(0:7),
    expand = c(0, 0)
  ) +
  scale_y_log10(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (log10)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- Print both ----
p_linear

p_log

📌 TOP2B narrow peaks (after deduplication)

library(tidyverse)
library(readr)

# --- metadata ---
metadata <- tribble(
  ~Sample,           ~Sample_Det,
  "MCW_SP_ChIP27",   "Ind1_VEH_TOP2B",
  "MCW_SP_ChIP28",   "Ind1_DOX_TOP2B",
  "MCW_SP_ChIP31",   "Ind2_VEH_TOP2B",
  "MCW_SP_ChIP32",   "Ind2_DOX_TOP2B",
  "MCW_SP_ChIP39",   "Ind3_VEH_TOP2B",
  "MCW_SP_ChIP40",   "Ind3_DOX_TOP2B",
  "MCW_SP_ChIP43",   "Ind4_VEH_TOP2B",
  "MCW_SP_ChIP44",   "Ind4_DOX_TOP2B",
  "MCW_SP_ChIP51",   "Ind5_VEH_TOP2B",
  "MCW_SP_ChIP52",   "Ind5_DOX_TOP2B",
  "MCW_SP_ChIP55",   "Ind6_VEH_TOP2B",
  "MCW_SP_ChIP56",   "Ind6_DOX_TOP2B"
)

# --- load all cutoff analysis files ---
files <- list.files("data/macs3_narrow_out_TOP2B_dedup", pattern = "_cutoff_analysis\\.txt$", full.names = TRUE)

df <- files %>%
  set_names() %>%
  map_dfr(~ read_delim(.x, delim = "\t", show_col_types = FALSE), .id = "filepath") %>%
  mutate(Sample = basename(filepath) %>% str_remove("_cutoff_analysis\\.txt")) %>%
  left_join(metadata, by = "Sample")

# ---- Plot with linear y-axis ----
p_linear <- df %>%
  ggplot(aes(x = qscore, y = npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  scale_x_continuous(
    limits = c(0, 7),
    breaks = 0:7,
    labels = as.character(0:7),   # ensures "0" shows up
    expand = c(0, 0)
  ) +
  scale_y_continuous(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (linear)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- Plot with log10 y-axis ----
p_log <- df %>%
  ggplot(aes(x = qscore, y = npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  scale_x_continuous(
    limits = c(0, 7),
    breaks = 0:7,
    labels = as.character(0:7),
    expand = c(0, 0)
  ) +
  scale_y_log10(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (log10)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- Print both ----
p_linear

p_log

📌 P53 narrow peaks (after deduplication)

library(tidyverse)
library(readr)
library(scales)

# --- metadata for p53 ---
metadata_p53 <- tribble(
  ~Sample,           ~Sample_Det,
  "MCW_SP_ChIP29",   "Ind1_VEH_p53",
  "MCW_SP_ChIP30",   "Ind1_DOX_p53",
  "MCW_SP_ChIP33",   "Ind2_VEH_p53",
  "MCW_SP_ChIP34",   "Ind2_DOX_p53",
  "MCW_SP_ChIP41",   "Ind3_VEH_p53",
  "MCW_SP_ChIP42",   "Ind3_DOX_p53",
  "MCW_SP_ChIP45",   "Ind4_VEH_p53",
  "MCW_SP_ChIP46",   "Ind4_DOX_p53",
  "MCW_SP_ChIP53",   "Ind5_VEH_p53",
  "MCW_SP_ChIP54",   "Ind5_DOX_p53",
  "MCW_SP_ChIP57",   "Ind6_VEH_p53",
  "MCW_SP_ChIP58",   "Ind6_DOX_p53"
) %>%
  mutate(
    Sample     = factor(Sample,     levels = Sample),
    Sample_Det = factor(Sample_Det, levels = Sample_Det)
  )

# --- load all cutoff-analysis files (edit path if needed) ---
data_dir <- "data/macs3_narrow_out_P53_dedup"   # <— change if your folder name differs
files <- list.files(data_dir, pattern = "_cutoff_analysis\\.txt$", full.names = TRUE)

df_p53 <- files %>%
  set_names() %>%
  map_dfr(~ read_delim(.x, delim = "\t", show_col_types = FALSE), .id = "filepath") %>%
  mutate(Sample = basename(filepath) %>% str_remove("_cutoff_analysis\\.txt")) %>%
  left_join(metadata_p53, by = "Sample") %>%
  mutate(Sample_Det = factor(Sample_Det, levels = levels(metadata_p53$Sample_Det))) %>%
  arrange(Sample_Det, qscore)

# --------- common x scale (forces 0..7 with a printed 0) ----------
x_fixed <- scale_x_continuous(
  limits = c(0, 7),
  breaks = 0:7,
  labels = as.character(0:7),
  expand = c(0, 0)
)

# ---- linear y ----
p_linear_p53 <- ggplot(df_p53, aes(qscore, npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  x_fixed +
  scale_y_continuous(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (linear)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- log10 y ----
p_log_p53 <- ggplot(df_p53, aes(qscore, npeaks, group = Sample)) +
  geom_line(size = 0.8, color = "#d95f02") +
  facet_wrap(~ Sample_Det, scales = "free_y", ncol = 6) +
  x_fixed +
  scale_y_log10(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Peak Counts (log10)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- show both ----
p_linear_p53

p_log_p53

Session information

sessionInfo()

R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] scales_1.3.0    lubridate_1.9.4 forcats_1.0.0   stringr_1.5.1  
 [5] dplyr_1.1.4     purrr_1.0.4     readr_2.1.5     tidyr_1.3.1    
 [9] tibble_3.2.1    ggplot2_3.5.2   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] sass_0.4.10       generics_0.1.3    stringi_1.8.3     hms_1.1.3        
 [5] digest_0.6.34     magrittr_2.0.3    evaluate_1.0.3    grid_4.3.0       
 [9] timechange_0.3.0  fastmap_1.2.0     rprojroot_2.0.4   workflowr_1.7.1  
[13] jsonlite_2.0.0    promises_1.3.2    jquerylib_0.1.4   cli_3.6.1        
[17] crayon_1.5.3      rlang_1.1.3       bit64_4.6.0-1     munsell_0.5.1    
[21] withr_3.0.2       cachem_1.1.0      yaml_2.3.10       parallel_4.3.0   
[25] tools_4.3.0       tzdb_0.5.0        colorspace_2.1-0  httpuv_1.6.15    
[29] vctrs_0.6.5       R6_2.6.1          lifecycle_1.0.4   git2r_0.36.2     
[33] bit_4.6.0         fs_1.6.3          vroom_1.6.5       pkgconfig_2.0.3  
[37] pillar_1.10.2     bslib_0.9.0       later_1.3.2       gtable_0.3.6     
[41] glue_1.7.0        Rcpp_1.0.12       xfun_0.52         tidyselect_1.2.1 
[45] rstudioapi_0.17.1 knitr_1.50        farver_2.1.2      htmltools_0.5.8.1
[49] labeling_0.4.3    rmarkdown_2.29    compiler_4.3.0