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📌 DNA damage response GO terms

library(tidyverse)
library(data.table)
library(ComplexHeatmap)
library(circlize)
library(grid)

### 📁 Define CorMotif GO Enrichment Files
go_files <- list(
  "Non response (0.1)"                         = "data/BP/CorMotif_Terms/GO_BP_Non_response_(0.1).csv",
  "CX-DOX mid-late response (0.1)"             = "data/BP/CorMotif_Terms/GO_BP_CX-DOX_mid-late_response_(0.1).csv",
  "DOX only mid-late (0.1)"                    = "data/BP/CorMotif_Terms/GO_BP_DOX_only_mid-late_(0.1).csv",
  "Non response (0.5)"                         = "data/BP/CorMotif_Terms/GO_BP_Non_response_(0.5).csv",
  "DOX specific response (0.5)"                = "data/BP/CorMotif_Terms/GO_BP_DOX_specific_response_(0.5).csv",
  "DOX only mid-late response (0.5)"           = "data/BP/CorMotif_Terms/GO_BP_DOX_only_mid-late_response_(0.5).csv",
  "CX total + DOX early response (0.5)"        = "data/BP/CorMotif_Terms/GO_BP_CX_total_+_DOX_early_response_(0.5).csv",
  "DOX early + CX-DOX mid-late response (0.5)" = "data/BP/CorMotif_Terms/GO_BP_DOX_early_+_CX-DOX_mid-late_response_(0.5).csv"
)

### 🧬 Define GO parent terms and map their children
parent_terms <- list(
  "GO:0006974" = "DNA damage response",
  "GO:0141112" = "broken chromosome clustering",
  "GO:0006281" = "DNA repair",
  "GO:0140861" = "DNA repair-dependent chromatin remodeling",
  "GO:0008630" = "intrinsic apoptotic signaling pathway in response to DNA damage",
  "GO:0042770" = "signal transduction in response to DNA damage",
  "GO:0009432" = "SOS response",
  "GO:0043247" = "telomere maintenance in response to DNA damage"
)

child_map <- list(
  "GO:0006281" = c("GO:0006284", "GO:0006307", "GO:0006302", "GO:0006298", "GO:0043504",
                   "GO:0006289", "GO:0006301", "GO:0006290", "GO:0000725", "GO:0000012"),
  "GO:0008630" = c("GO:0042771", "GO:1902230", "GO:1902231", "GO:1902229"),
  "GO:0042770" = c("GO:0000077", "GO:0030330", "GO:0042772", "GO:0044773",
                   "GO:2000002", "GO:2000003", "GO:2000001"),
  "GO:0043247" = c("GO:1904506", "GO:1904507", "GO:0031848", "GO:1904505", "GO:0097698")
)

ddr_descriptions <- unname(unlist(parent_terms))

### 🔁 Step 1: Extract best p-values from GO enrichment results
go_matrix_df <- map_dfr(names(go_files), function(cond) {
  file <- go_files[[cond]]
  df <- tryCatch(fread(file), error = function(e) return(data.table()))
  
  if (nrow(df) == 0 || !all(c("ID", "Description", "pvalue", "p.adjust") %in% colnames(df))) {
    message("⚠️ Skipping or padding malformed file: ", cond)
    return(tibble(Description = ddr_descriptions, pvalue = NA, p.adjust = NA, log10p = NA, Condition = cond))
  }
  
  df <- as_tibble(df) %>% dplyr::select(ID, Description, pvalue, p.adjust)
  
  results <- lapply(names(parent_terms), function(parent_id) {
    all_ids <- c(parent_id, child_map[[parent_id]])
    df_sub <- df %>% filter(ID %in% all_ids)
    
    if (nrow(df_sub) == 0) {
      tibble(Description = parent_terms[[parent_id]], pvalue = NA, p.adjust = NA, log10p = NA, Condition = cond)
    } else {
      best_row <- df_sub %>% slice_min(pvalue, n = 1)
      tibble(Description = parent_terms[[parent_id]],
             pvalue = best_row$pvalue,
             p.adjust = best_row$p.adjust,
             log10p = -log10(best_row$pvalue),
             Condition = cond)
    }
  })
  
  bind_rows(results)
})

### 🧱 Step 2: Construct log10(p) and raw p-value matrices
heatmap_data <- go_matrix_df %>%
  dplyr::select(Description, Condition, log10p) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = log10p) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

pval_matrix <- go_matrix_df %>%
  dplyr::select(Description, Condition, pvalue) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = pvalue) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

p_adj_matrix <- go_matrix_df %>%
  dplyr::select(Description, Condition, p.adjust) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = p.adjust) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

### ✅ Step 3: Pad missing columns
all_conditions <- names(go_files)
missing_cols <- setdiff(all_conditions, colnames(heatmap_data))
if (length(missing_cols) > 0) {
  for (cond in missing_cols) {
    heatmap_data[, cond] <- NA
    pval_matrix[, cond] <- NA
  }
  heatmap_data <- heatmap_data[, all_conditions]
  pval_matrix <- pval_matrix[, all_conditions]
}

### 🎨 Step 4: Define color palette
breaks <- seq(0, 20, by = 2.5)
palette <- colorRampPalette(c("white", "#fde0dd", "#fa9fb5", "#f768a1", "#c51b8a", "#7a0177", "#49006a"))(length(breaks))
col_fun <- colorRamp2(breaks, palette)

### 🔥 Step 5: Plot heatmap with stars for p.adjust < 0.05
ht <- Heatmap(
  heatmap_data,
  name = "-log10(p)",
  col = col_fun,
  na_col = "white",
  rect_gp = gpar(col = "black", lwd = 0.5),
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  row_names_gp = gpar(fontsize = 9),
  column_names_gp = gpar(fontsize = 9),
  column_names_rot = 45,
  row_names_max_width = max_text_width(rownames(heatmap_data), gp = gpar(fontsize = 9)),
  cell_fun = function(j, i, x, y, width, height, fill) {
    adj_p <- p_adj_matrix[i, j]  # Use p.adjust matrix for significance check
    if (!is.na(adj_p) && adj_p < 0.05) {
      grid.text("*", x, y, gp = gpar(fontsize = 12))
    }
  },
  heatmap_legend_param = list(
    title = "-log10(p value)",
    at = breaks,
    labels = as.character(breaks),
    legend_width = unit(5, "cm"),
    direction = "horizontal",
    title_gp = gpar(fontsize = 10, fontface = "bold"),
    labels_gp = gpar(fontsize = 9)
  )
)

### 🖼 Final Draw
draw(ht, heatmap_legend_side = "top")

Version Author Date
fce174d sayanpaul01 2025-06-03
af36d28 sayanpaul01 2025-06-01

📌 Ribosome biogenesis GO terms

library(tidyverse)
library(data.table)
library(ComplexHeatmap)
library(circlize)
library(grid)

### 📁 Define CorMotif GO Enrichment Files
go_files <- list(
  "Non response (0.1)"                         = "data/BP/CorMotif_Terms/GO_BP_Non_response_(0.1).csv",
  "CX-DOX mid-late response (0.1)"             = "data/BP/CorMotif_Terms/GO_BP_CX-DOX_mid-late_response_(0.1).csv",
  "DOX only mid-late (0.1)"                    = "data/BP/CorMotif_Terms/GO_BP_DOX_only_mid-late_(0.1).csv",
  "Non response (0.5)"                         = "data/BP/CorMotif_Terms/GO_BP_Non_response_(0.5).csv",
  "DOX specific response (0.5)"                = "data/BP/CorMotif_Terms/GO_BP_DOX_specific_response_(0.5).csv",
  "DOX only mid-late response (0.5)"           = "data/BP/CorMotif_Terms/GO_BP_DOX_only_mid-late_response_(0.5).csv",
  "CX total + DOX early response (0.5)"        = "data/BP/CorMotif_Terms/GO_BP_CX_total_+_DOX_early_response_(0.5).csv",
  "DOX early + CX-DOX mid-late response (0.5)" = "data/BP/CorMotif_Terms/GO_BP_DOX_early_+_CX-DOX_mid-late_response_(0.5).csv"
)

### 🧬 Ribosome Biogenesis GO Terms
parent_terms <- list(
  "GO:0042254" = "ribosome biogenesis",
  "GO:0090071" = "negative regulation of ribosome biogenesis",
  "GO:0042273" = "ribosomal large subunit biogenesis",
  "GO:0042274" = "ribosomal small subunit biogenesis",
  "GO:0000054" = "ribosomal subunit export from nucleus",
  "GO:0042255" = "ribosome assembly",
  "GO:0006364" = "rRNA processing"
)

child_map <- list(
  "GO:0090071" = c("GO:2000201", "GO:2000233"),
  "GO:0042273" = c("GO:0000470", "GO:0000027"),
  "GO:0042274" = c("GO:0030490", "GO:0000028", "GO:0140638"),
  "GO:0000054" = c("GO:2000201", "GO:2000202", "GO:2000200", "GO:0000055", "GO:0000056"),
  "GO:0042255" = c("GO:0042256", "GO:0061668", "GO:0000027", "GO:0000028"),
  "GO:0006364" = c(
    "GO:1901259", "GO:0000450", "GO:0000483", "GO:0002103", "GO:0000479", "GO:0000449",
    "GO:0000475", "GO:0000476", "GO:0000460", "GO:0000481", "GO:0000470", "GO:0030490",
    "GO:2000233", "GO:2000234", "GO:2000232", "GO:0031125", "GO:0000967", "GO:0000154", "GO:1990417"
  )
)

rbio_descriptions <- unname(unlist(parent_terms))

### 🔁 Step 1: Extract best p-values from GO enrichment results
go_matrix_df <- purrr::map_dfr(names(go_files), function(cond) {
  file <- go_files[[cond]]
  df <- tryCatch(fread(file), error = function(e) return(data.table()))

  if (nrow(df) == 0 || !all(c("ID", "Description", "pvalue", "p.adjust") %in% colnames(df))) {
    message("⚠️ Skipping or padding malformed file: ", cond)
    return(tibble(Description = rbio_descriptions, pvalue = NA, p.adjust = NA, log10p = NA, Condition = cond))
  }

  df <- as_tibble(df) %>% dplyr::select(ID, Description, pvalue, p.adjust)

  results <- lapply(names(parent_terms), function(parent_id) {
    all_ids <- c(parent_id, child_map[[parent_id]])
    df_sub <- df %>% dplyr::filter(ID %in% all_ids)

    if (nrow(df_sub) == 0) {
      tibble(Description = parent_terms[[parent_id]], pvalue = NA, p.adjust = NA, log10p = NA, Condition = cond)
    } else {
      best_row <- df_sub %>% dplyr::slice_min(pvalue, n = 1)
      tibble(
        Description = parent_terms[[parent_id]],
        pvalue = best_row$pvalue,
        p.adjust = best_row$p.adjust,
        log10p = -log10(best_row$pvalue),
        Condition = cond
      )
    }
  })

  bind_rows(results)
})

### 🧱 Step 2: Construct log10(p) and raw p-value matrices
heatmap_data <- go_matrix_df %>%
  dplyr::select(Description, Condition, log10p) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = log10p) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

pval_matrix <- go_matrix_df %>%
  dplyr::select(Description, Condition, pvalue) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = pvalue) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

p_adj_matrix <- go_matrix_df %>%
  dplyr::select(Description, Condition, p.adjust) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = p.adjust) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

### ✅ Step 3: Pad missing columns
all_conditions <- names(go_files)
missing_cols <- setdiff(all_conditions, colnames(heatmap_data))
if (length(missing_cols) > 0) {
  for (cond in missing_cols) {
    heatmap_data[, cond] <- NA
    pval_matrix[, cond] <- NA
  }
  heatmap_data <- heatmap_data[, all_conditions]
  pval_matrix <- pval_matrix[, all_conditions]
}

### 🎨 Step 4: Define color palette
breaks <- seq(0, 20, by = 2.5)
palette <- colorRampPalette(c("white", "#fde0dd", "#fa9fb5", "#f768a1", "#c51b8a", "#7a0177", "#49006a"))(length(breaks))
col_fun <- colorRamp2(breaks, palette)

### 🔥 Step 5: Plot heatmap with stars for p.adjust < 0.05
ht <- Heatmap(
  heatmap_data,
  name = "-log10(p)",
  col = col_fun,
  na_col = "white",
  rect_gp = gpar(col = "black", lwd = 0.5),
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  row_names_gp = gpar(fontsize = 9),
  column_names_gp = gpar(fontsize = 9),
  column_names_rot = 45,
  row_names_max_width = max_text_width(rownames(heatmap_data), gp = gpar(fontsize = 9)),
  cell_fun = function(j, i, x, y, width, height, fill) {
    adj_p <- p_adj_matrix[i, j]  # Use p.adjust matrix for significance check
    if (!is.na(adj_p) && adj_p < 0.05) {
      grid.text("*", x, y, gp = gpar(fontsize = 12))
    }
  },
  heatmap_legend_param = list(
    title = "-log10(p value)",
    at = breaks,
    labels = as.character(breaks),
    legend_width = unit(5, "cm"),
    direction = "horizontal",
    title_gp = gpar(fontsize = 10, fontface = "bold"),
    labels_gp = gpar(fontsize = 9)
  )
)

### 🖼 Final Draw
draw(ht, heatmap_legend_side = "top")

Version Author Date
fce174d sayanpaul01 2025-06-03
af36d28 sayanpaul01 2025-06-01

📌 G-Quadruplex GO terms

### 📦 Load Required Libraries
# 📦 Load Required Libraries
library(tidyverse)
library(data.table)
library(ComplexHeatmap)
library(circlize)
library(grid)

# 📁 Define GO Enrichment Files
go_files <- list(
  "Non response (0.1)"                         = "data/BP/CorMotif_Terms_Combined/GO_All_Non_response_(0.1).csv",
  "CX-DOX mid-late response (0.1)"             = "data/BP/CorMotif_Terms_Combined/GO_All_CX-DOX_mid-late_response_(0.1).csv",
  "DOX only mid-late (0.1)"                    = "data/BP/CorMotif_Terms_Combined/GO_All_DOX_only_mid-late_(0.1).csv",
  "Non response (0.5)"                         = "data/BP/CorMotif_Terms_Combined/GO_All_Non_response_(0.5).csv",
  "DOX specific response (0.5)"                = "data/BP/CorMotif_Terms_Combined/GO_All_DOX_specific_response_(0.5).csv",
  "DOX only mid-late response (0.5)"           = "data/BP/CorMotif_Terms_Combined/GO_All_DOX_only_mid-late_response_(0.5).csv",
  "CX total + DOX early response (0.5)"        = "data/BP/CorMotif_Terms_Combined/GO_All_CX_total_+_DOX_early_response_(0.5).csv",
  "DOX early + CX-DOX mid-late response (0.5)" = "data/BP/CorMotif_Terms_Combined/GO_All_DOX_early_+_CX-DOX_mid-late_response_(0.5).csv"
)

# 🧬 G4-Related GO Terms
g4_terms <- list(
  "GO:0051880" = "G-quadruplex DNA binding",
  "GO:0002151" = "G-quadruplex RNA binding",
  "GO:0071919" = "G-quadruplex DNA formation",
  "GO:1905493" = "Regulation of G-quadruplex DNA binding",
  "GO:0160225" = "G-quadruplex unwinding activity",
  "GO:0061849" = "Telomeric G-quadruplex DNA binding"
)

g4_descriptions <- unname(unlist(g4_terms))

# 🔁 Step 1: Extract best p-values and p.adjust values
go_matrix_df <- purrr::map_dfr(names(go_files), function(cond) {
  file <- go_files[[cond]]
  df <- tryCatch(fread(file), error = function(e) return(data.table()))

  if (nrow(df) == 0 || !all(c("ID", "Description", "pvalue", "p.adjust") %in% colnames(df))) {
    message("⚠️ Skipping or padding malformed file: ", cond)
    return(tibble(Description = g4_descriptions, pvalue = NA, padj = NA, log10p = NA, Condition = cond))
  }

  df <- as_tibble(df) %>% dplyr::select(ID, Description, pvalue, p.adjust)

  results <- lapply(names(g4_terms), function(go_id) {
    df_sub <- df %>% dplyr::filter(ID == go_id)
    if (nrow(df_sub) == 0) {
      tibble(Description = g4_terms[[go_id]], pvalue = NA, padj = NA, log10p = NA, Condition = cond)
    } else {
      best_row <- df_sub %>% dplyr::slice_min(pvalue, n = 1)
      tibble(
        Description = g4_terms[[go_id]],
        pvalue = best_row$pvalue,
        padj = best_row$p.adjust,
        log10p = -log10(best_row$pvalue),
        Condition = cond
      )
    }
  })

  bind_rows(results)
})

# 🧱 Step 2: Create heatmap and p-value matrices
heatmap_data <- go_matrix_df %>%
  dplyr::select(Description, Condition, log10p) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = log10p) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

pval_matrix <- go_matrix_df %>%
  dplyr::select(Description, Condition, pvalue) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = pvalue) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

padj_matrix <- go_matrix_df %>%
  dplyr::select(Description, Condition, padj) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = padj) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

# ✅ Step 3: Pad missing columns
all_conditions <- names(go_files)
missing_cols <- setdiff(all_conditions, colnames(heatmap_data))
if (length(missing_cols) > 0) {
  for (cond in missing_cols) {
    heatmap_data[, cond] <- NA
    pval_matrix[, cond] <- NA
    padj_matrix[, cond] <- NA
  }
  heatmap_data <- heatmap_data[, all_conditions]
  pval_matrix <- pval_matrix[, all_conditions]
  padj_matrix <- padj_matrix[, all_conditions]
}

# 🎨 Step 4: Color palette
breaks <- seq(0, 20, by = 2.5)
palette <- colorRampPalette(c("white", "#fde0dd", "#fa9fb5", "#f768a1", "#c51b8a", "#7a0177", "#49006a"))(length(breaks))
col_fun <- colorRamp2(breaks, palette)

# 🔥 Step 5: Plot heatmap
ht <- Heatmap(
  heatmap_data,
  name = "-log10(p)",
  col = col_fun,
  na_col = "white",
  rect_gp = gpar(col = "black", lwd = 0.5),
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  row_names_gp = gpar(fontsize = 9),
  column_names_gp = gpar(fontsize = 9),
  column_names_rot = 45,
  row_names_max_width = max_text_width(rownames(heatmap_data), gp = gpar(fontsize = 9)),
  cell_fun = function(j, i, x, y, width, height, fill) {
    raw_padj <- padj_matrix[i, j]
    if (!is.na(raw_padj) && raw_padj < 0.05) {
      grid.text("*", x, y, gp = gpar(fontsize = 12))
    }
  },
  heatmap_legend_param = list(
    title = "-log10(p value)",
    at = breaks,
    labels = as.character(breaks),
    legend_width = unit(5, "cm"),
    direction = "horizontal",
    title_gp = gpar(fontsize = 10, fontface = "bold"),
    labels_gp = gpar(fontsize = 9)
  )
)

# 🖼 Final Draw
draw(ht, heatmap_legend_side = "top")

Version Author Date
fce174d sayanpaul01 2025-06-03
af36d28 sayanpaul01 2025-06-01

📌 Transcription by RNA polymerase I

### 📦 Load Required Libraries
library(tidyverse)
library(data.table)
library(ComplexHeatmap)
library(circlize)
library(grid)

### 📁 Define GO Enrichment Files
go_files <- list(
  "Non response (0.1)"                         = "data/BP/CorMotif_Terms_Combined/GO_All_Non_response_(0.1).csv",
  "CX-DOX mid-late response (0.1)"             = "data/BP/CorMotif_Terms_Combined/GO_All_CX-DOX_mid-late_response_(0.1).csv",
  "DOX only mid-late (0.1)"                    = "data/BP/CorMotif_Terms_Combined/GO_All_DOX_only_mid-late_(0.1).csv",
  "Non response (0.5)"                         = "data/BP/CorMotif_Terms_Combined/GO_All_Non_response_(0.5).csv",
  "DOX specific response (0.5)"                = "data/BP/CorMotif_Terms_Combined/GO_All_DOX_specific_response_(0.5).csv",
  "DOX only mid-late response (0.5)"           = "data/BP/CorMotif_Terms_Combined/GO_All_DOX_only_mid-late_response_(0.5).csv",
  "CX total + DOX early response (0.5)"        = "data/BP/CorMotif_Terms_Combined/GO_All_CX_total_+_DOX_early_response_(0.5).csv",
  "DOX early + CX-DOX mid-late response (0.5)" = "data/BP/CorMotif_Terms_Combined/GO_All_DOX_early_+_CX-DOX_mid-late_response_(0.5).csv"
)

### 🔧 Parent GO Terms and their Children
parent_terms <- list(
  "GO:0006360" = "transcription by RNA polymerase I",
  "GO:0016479" = "negative regulation of transcription by RNA polymerase I",
  "GO:0042790" = "nucleolar large rRNA transcription by RNA polymerase I",
  "GO:0045943" = "positive regulation of transcription by RNA polymerase I",
  "GO:0006356" = "regulation of transcription by RNA polymerase I",
  "GO:0006363" = "termination of RNA polymerase I transcription",
  "GO:0006361" = "transcription initiation at RNA polymerase I promoter",
  "GO:0005736" = "RNA polymerase I complex"
)

rnapolI_descriptions <- unname(unlist(parent_terms))

### 🔁 Step 1: Extract best p-values
go_matrix_df <- purrr::map_dfr(names(go_files), function(cond) {
  file <- go_files[[cond]]
  df <- tryCatch(fread(file), error = function(e) return(data.table()))

  if (nrow(df) == 0 || !all(c("ID", "Description", "pvalue", "p.adjust") %in% colnames(df))) {
    message("⚠️ Skipping or padding malformed file: ", cond)
    return(tibble(Description = rnapolI_descriptions, pvalue = NA, padj = NA, log10p = NA, Condition = cond))
  }

  df <- as_tibble(df) %>% dplyr::select(ID, Description, pvalue, p.adjust)

  results <- lapply(names(parent_terms), function(go_id) {
    df_sub <- df %>% dplyr::filter(ID == go_id)
    if (nrow(df_sub) == 0) {
      tibble(Description = parent_terms[[go_id]], pvalue = NA, padj = NA, log10p = NA, Condition = cond)
    } else {
      best_row <- df_sub %>% dplyr::slice_min(pvalue, n = 1)
      tibble(
        Description = parent_terms[[go_id]],
        pvalue = best_row$pvalue,
        padj = best_row$p.adjust,
        log10p = -log10(best_row$pvalue),
        Condition = cond
      )
    }
  })

  bind_rows(results)
})

### 🧱 Step 2: Create heatmap and p-value matrices
heatmap_data <- go_matrix_df %>%
  dplyr::select(Description, Condition, log10p) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = log10p) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

padj_matrix <- go_matrix_df %>%
  dplyr::select(Description, Condition, padj) %>%
  tidyr::pivot_wider(names_from = Condition, values_from = padj) %>%
  tibble::column_to_rownames("Description") %>%
  as.matrix()

### ✅ Step 3: Pad missing columns
all_conditions <- names(go_files)
missing_cols <- setdiff(all_conditions, colnames(heatmap_data))
if (length(missing_cols) > 0) {
  for (cond in missing_cols) {
    heatmap_data[, cond] <- NA
    padj_matrix[, cond] <- NA
  }
  heatmap_data <- heatmap_data[, all_conditions]
  padj_matrix <- padj_matrix[, all_conditions]
}

### 🎨 Step 4: Color palette
breaks <- seq(0, 20, by = 2.5)
palette <- colorRampPalette(c("white", "#fde0dd", "#fa9fb5", "#f768a1", "#c51b8a", "#7a0177", "#49006a"))(length(breaks))
col_fun <- colorRamp2(breaks, palette)

### 🔥 Step 5: Plot heatmap
ht <- Heatmap(
  heatmap_data,
  name = "-log10(p)",
  col = col_fun,
  na_col = "white",
  rect_gp = gpar(col = "black", lwd = 0.5),
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  row_names_gp = gpar(fontsize = 9),
  column_names_gp = gpar(fontsize = 9),
  column_names_rot = 45,
  row_names_max_width = max_text_width(rownames(heatmap_data), gp = gpar(fontsize = 9)),
  cell_fun = function(j, i, x, y, width, height, fill) {
    adj_p <- padj_matrix[i, j]
    if (!is.na(adj_p) && adj_p < 0.05) {
      grid.text("*", x, y, gp = gpar(fontsize = 12))
    }
  },
  heatmap_legend_param = list(
    title = "-log10(p value)",
    at = breaks,
    labels = as.character(breaks),
    legend_width = unit(5, "cm"),
    direction = "horizontal",
    title_gp = gpar(fontsize = 10, fontface = "bold"),
    labels_gp = gpar(fontsize = 9)
  )
)

### 🖼 Final Draw
draw(ht, heatmap_legend_side = "top")

Version Author Date
af36d28 sayanpaul01 2025-06-01 

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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] circlize_0.4.16       ComplexHeatmap_2.18.0 data.table_1.17.0    
 [4] lubridate_1.9.4       forcats_1.0.0         stringr_1.5.1        
 [7] dplyr_1.1.4           purrr_1.0.4           readr_2.1.5          
[10] tidyr_1.3.1           tibble_3.2.1          ggplot2_3.5.2        
[13] tidyverse_2.0.0      

loaded via a namespace (and not attached):
 [1] shape_1.4.6.1       gtable_0.3.6        rjson_0.2.23       
 [4] xfun_0.52           bslib_0.9.0         GlobalOptions_0.1.2
 [7] tzdb_0.5.0          Cairo_1.6-2         vctrs_0.6.5        
[10] tools_4.3.0         generics_0.1.3      stats4_4.3.0       
[13] parallel_4.3.0      cluster_2.1.8.1     pkgconfig_2.0.3    
[16] RColorBrewer_1.1-3  S4Vectors_0.40.2    lifecycle_1.0.4    
[19] compiler_4.3.0      git2r_0.36.2        munsell_0.5.1      
[22] codetools_0.2-20    clue_0.3-66         httpuv_1.6.15      
[25] htmltools_0.5.8.1   sass_0.4.10         yaml_2.3.10        
[28] later_1.3.2         pillar_1.10.2       crayon_1.5.3       
[31] jquerylib_0.1.4     whisker_0.4.1       cachem_1.1.0       
[34] magick_2.8.6        iterators_1.0.14    foreach_1.5.2      
[37] tidyselect_1.2.1    digest_0.6.34       stringi_1.8.3      
[40] rprojroot_2.0.4     fastmap_1.2.0       colorspace_2.1-0   
[43] cli_3.6.1           magrittr_2.0.3      withr_3.0.2        
[46] scales_1.3.0        promises_1.3.2      timechange_0.3.0   
[49] rmarkdown_2.29      matrixStats_1.5.0   workflowr_1.7.1    
[52] png_0.1-8           GetoptLong_1.0.5    hms_1.1.3          
[55] evaluate_1.0.3      knitr_1.50          IRanges_2.36.0     
[58] doParallel_1.0.17   rlang_1.1.3         Rcpp_1.0.12        
[61] glue_1.7.0          BiocGenerics_0.48.1 rstudioapi_0.17.1  
[64] jsonlite_2.0.0      R6_2.6.1            fs_1.6.3