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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

Version Author Date
9c77a6c sayanpaul01 2025-08-24 
p_log

Version Author Date
9c77a6c sayanpaul01 2025-08-24

📌 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

Version Author Date
9c77a6c sayanpaul01 2025-08-24 
p_log

Version Author Date
9c77a6c sayanpaul01 2025-08-24

📌 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

Version Author Date
9c77a6c sayanpaul01 2025-08-24 
p_log_p53

Version Author Date
9c77a6c sayanpaul01 2025-08-24

📌 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

Version Author Date
9c77a6c sayanpaul01 2025-08-24 
p_log

Version Author Date
9c77a6c sayanpaul01 2025-08-24

📌 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

Version Author Date
9c77a6c sayanpaul01 2025-08-24 
p_log

Version Author Date
9c77a6c sayanpaul01 2025-08-24

📌 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

Version Author Date
9c77a6c sayanpaul01 2025-08-24 
p_log_p53

Version Author Date
9c77a6c sayanpaul01 2025-08-24

📌 TOP2B broad peaks length (before deduplication)

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

# --- 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 (lpeaks) ----
p_linear <- ggplot(df, aes(qscore, lpeaks, 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 = "Total Peak Length (bp, 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 (lpeaks) ----
p_log <- df |>
  mutate(lpeaks = ifelse(lpeaks <= 0, NA, lpeaks)) |>
  ggplot(aes(qscore, lpeaks, 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 = "Total Peak Length (bp, log10)") +
  theme_minimal(base_size = 12) +
  theme(strip.text = element_text(face = "bold", size = 10),
        panel.grid.minor = element_blank())

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

Version Author Date
bc515e6 sayanpaul01 2025-08-31 
p_log

Version Author Date
bc515e6 sayanpaul01 2025-08-31

📌 TOP2B narrow peaks length (before deduplication)

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

# --- 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 = lpeaks, 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_continuous(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Total Peak Length (bp, 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 %>%
  mutate(lpeaks = ifelse(lpeaks <= 0, NA, lpeaks)) %>%
  ggplot(aes(x = qscore, y = lpeaks, 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 = "Total Peak Length (bp, log10)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

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

Version Author Date
bc515e6 sayanpaul01 2025-08-31 
p_log

Version Author Date
bc515e6 sayanpaul01 2025-08-31

📌 P53 narrow peaks length (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 ---
data_dir <- "data/macs3_narrow_out_P53"   # adjust if needed
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, lpeaks, 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 = "Total Peak Length (bp, 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 <- df_p53 %>%
  mutate(lpeaks = ifelse(lpeaks <= 0, NA, lpeaks)) %>%
  ggplot(aes(qscore, lpeaks, 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 = "Total Peak Length (bp, 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

Version Author Date
bc515e6 sayanpaul01 2025-08-31 
p_log_p53

Version Author Date
bc515e6 sayanpaul01 2025-08-31

📌 TOP2B broad peaks length (after deduplication)

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

# --- 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 = lpeaks, 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_continuous(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Total Peak Length (bp, 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 %>%
  mutate(lpeaks = ifelse(lpeaks <= 0, NA, lpeaks)) %>%
  ggplot(aes(x = qscore, y = lpeaks, 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 = "Total Peak Length (bp, log10)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

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

Version Author Date
bc515e6 sayanpaul01 2025-08-31 
p_log

Version Author Date
bc515e6 sayanpaul01 2025-08-31

📌 TOP2B narrow peaks length (after deduplication)

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

# --- 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 = lpeaks, 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_continuous(labels = label_number(big.mark = ",")) +
  labs(x = "Q Score", y = "Total Peak Length (bp, 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 %>%
  mutate(lpeaks = ifelse(lpeaks <= 0, NA, lpeaks)) %>%
  ggplot(aes(x = qscore, y = lpeaks, 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 = "Total Peak Length (bp, log10)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

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

Version Author Date
bc515e6 sayanpaul01 2025-08-31 
p_log

Version Author Date
bc515e6 sayanpaul01 2025-08-31

📌 P53 narrow peaks length (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 ---
data_dir <- "data/macs3_narrow_out_P53_dedup"
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 (lpeaks) ----
p_linear_p53 <- ggplot(df_p53, aes(qscore, lpeaks, 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 = "Total Peak Length (bp, linear)") +
  theme_minimal(base_size = 12) +
  theme(
    strip.text = element_text(face = "bold", size = 10),
    panel.grid.minor = element_blank()
  )

# ---- log10 y (lpeaks) ----
p_log_p53 <- df_p53 %>%
  mutate(lpeaks = ifelse(lpeaks <= 0, NA, lpeaks)) %>%
  ggplot(aes(qscore, lpeaks, 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 = "Total Peak Length (bp, 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

Version Author Date
bc515e6 sayanpaul01 2025-08-31 
p_log_p53

Version Author Date
bc515e6 sayanpaul01 2025-08-31

📌 Elbow cutoff TOP2B broad peaks (before deduplication)

suppressPackageStartupMessages({
  library(tidyverse)
  library(readr)
  library(scales)
  library(stringr)
})

# ---- Load data ----
df <- read_csv("data/ELBOW_TOP2B_broad.csv", show_col_types = FALSE)

# ---- Ensure numeric + derive Individual ID and ordered Tx ----
num_cols <- c("Elbow_cutoff_Qscore", "NPeaks_at_cutoff", "LPeaks_at_cutoff")
df <- df %>% mutate(across(all_of(num_cols), as.numeric))

# Robust 'Ind' derivation
if ("Ind_ab" %in% names(df)) {
  df <- df %>% mutate(Ind = str_extract(Ind_ab, "^Ind\\d+"))
} else if ("Sample Det" %in% names(df)) {
  df <- df %>% mutate(Ind = str_extract(`Sample Det`, "^Ind\\d+"))
} else {
  df <- df %>% mutate(Ind = Sample)
}

# Order Tx: VEH → DOX
df <- df %>% mutate(Tx = factor(Tx, levels = c("VEH", "DOX")))

# ---- Long format for plotting ----
df_long <- df %>%
  select(Sample, Ind, Tx, Elbow_cutoff_Qscore, NPeaks_at_cutoff, LPeaks_at_cutoff) %>%
  pivot_longer(
    cols = c(NPeaks_at_cutoff, LPeaks_at_cutoff, Elbow_cutoff_Qscore),
    names_to = "metric", values_to = "value"
  ) %>%
  mutate(metric = recode(
    metric,
    NPeaks_at_cutoff     = "NPeaks at elbow",
    LPeaks_at_cutoff     = "LPeaks at elbow (bp)",
    Elbow_cutoff_Qscore  = "Elbow cutoff (−log10 q)"
  ))

# ---- Color palette (Okabe–Ito for up to 8 individuals) ----
okabe_ito <- c(
  "#E69F00", "#56B4E9", "#009E73",
  "#F0E442", "#0072B2", "#D55E00",
  "#CC79A7", "#999999"
)
n_ind <- df %>% distinct(Ind) %>% nrow()

col_scale <- if (n_ind <= length(okabe_ito)) {
  scale_color_manual(values = okabe_ito)
} else {
  scale_color_viridis_d(option = "C", end = 0.95)
}

# ---- Plot: Paired slopes VEH → DOX per individual ----
p_slope <- ggplot(df_long, aes(x = Tx, y = value, group = Ind, color = Ind)) +
  geom_line(linewidth = 1.2, alpha = 0.9) +
  geom_point(size = 3) +
  facet_wrap(~ metric, scales = "free_y", ncol = 3) +
  scale_y_continuous(labels = label_number(big.mark = ",")) +
  col_scale +
  labs(
    title = "Elbow cutoff TOP2B broad peaks (before dedup)",
    x = NULL, y = NULL, color = "Individual"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    legend.position = "bottom",
    legend.text = element_text(size = 10),
    strip.text = element_text(face = "bold")
  )

# ---- Show plot ----
print(p_slope)

📌 Elbow cutoff TOP2B narrow peaks (before deduplication)

suppressPackageStartupMessages({
  library(tidyverse)
  library(readr)
  library(scales)
  library(stringr)
})

# ---- Load data ----
df <- read_csv("data/ELBOW_TOP2B_narrow.csv", show_col_types = FALSE)

# ---- Harmonize column names (supports both naming schemes) ----
if ("Elbow_qscore" %in% names(df)) df <- df %>% rename(Elbow_cutoff_Qscore = Elbow_qscore)
if ("NPeaks_at_elbow" %in% names(df)) df <- df %>% rename(NPeaks_at_cutoff = NPeaks_at_elbow)
if ("LPeaks_at_elbow" %in% names(df)) df <- df %>% rename(LPeaks_at_cutoff = LPeaks_at_elbow)

# ---- Ensure numeric + derive Individual ID and ordered Tx ----
num_cols <- c("Elbow_cutoff_Qscore", "NPeaks_at_cutoff", "LPeaks_at_cutoff")
df <- df %>% mutate(across(all_of(num_cols), as.numeric))

# Robust 'Ind' derivation
if ("Ind_ab" %in% names(df)) {
  df <- df %>% mutate(Ind = str_extract(Ind_ab, "^Ind\\d+"))
} else if ("Sample Det" %in% names(df)) {
  df <- df %>% mutate(Ind = str_extract(`Sample Det`, "^Ind\\d+"))
} else {
  df <- df %>% mutate(Ind = Sample)
}

# Order Tx: VEH → DOX (handle both Tx or Treatment columns)
if ("Tx" %in% names(df)) {
  df <- df %>% mutate(Tx = factor(Tx, levels = c("VEH","DOX")))
} else if ("Treatment" %in% names(df)) {
  df <- df %>% mutate(Tx = factor(Treatment, levels = c("VEH_TOP2B","DOX_TOP2B")))
}

# ---- Long format for plotting ----
df_long <- df %>%
  select(Sample, Ind, Tx, Elbow_cutoff_Qscore, NPeaks_at_cutoff, LPeaks_at_cutoff) %>%
  pivot_longer(
    cols = c(NPeaks_at_cutoff, LPeaks_at_cutoff, Elbow_cutoff_Qscore),
    names_to = "metric", values_to = "value"
  ) %>%
  mutate(metric = recode(
    metric,
    NPeaks_at_cutoff     = "NPeaks at elbow",
    LPeaks_at_cutoff     = "LPeaks at elbow (bp)",
    Elbow_cutoff_Qscore  = "Elbow cutoff (−log10 q)"
  ))

# ---- Color palette (Okabe–Ito up to 8 individuals, else viridis) ----
okabe_ito <- c("#E69F00","#56B4E9","#009E73","#F0E442","#0072B2","#D55E00","#CC79A7","#999999")
n_ind <- df %>% distinct(Ind) %>% nrow()
col_scale <- if (n_ind <= length(okabe_ito)) {
  scale_color_manual(values = okabe_ito)
} else {
  scale_color_viridis_d(option = "C", end = 0.95)
}

# ---- Plot: Paired slopes VEH → DOX per individual ----
p_slope <- ggplot(df_long, aes(x = Tx, y = value, group = Ind, color = Ind)) +
  geom_line(linewidth = 1.2, alpha = 0.9) +
  geom_point(size = 3) +
  facet_wrap(~ metric, scales = "free_y", ncol = 3) +
  scale_y_continuous(labels = label_number(big.mark = ",")) +
  col_scale +
  labs(
    title = "Elbow cutoff TOP2B narrow peaks (before dedup)",
    x = NULL, y = NULL, color = "Individual"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    legend.position = "bottom",
    legend.text = element_text(size = 10),
    strip.text = element_text(face = "bold")
  )

print(p_slope)


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    whisker_0.4.1     promises_1.3.2    jquerylib_0.1.4  
[17] cli_3.6.1         crayon_1.5.3      rlang_1.1.3       bit64_4.6.0-1    
[21] munsell_0.5.1     withr_3.0.2       cachem_1.1.0      yaml_2.3.10      
[25] parallel_4.3.0    tools_4.3.0       tzdb_0.5.0        colorspace_2.1-0 
[29] httpuv_1.6.15     vctrs_0.6.5       R6_2.6.1          lifecycle_1.0.4  
[33] git2r_0.36.2      bit_4.6.0         fs_1.6.3          vroom_1.6.5      
[37] pkgconfig_2.0.3   pillar_1.10.2     bslib_0.9.0       later_1.3.2      
[41] gtable_0.3.6      glue_1.7.0        Rcpp_1.0.12       xfun_0.52        
[45] tidyselect_1.2.1  rstudioapi_0.17.1 knitr_1.50        farver_2.1.2     
[49] htmltools_0.5.8.1 labeling_0.4.3    rmarkdown_2.29    compiler_4.3.0