Figure_S17

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Last updated: 2025-08-13

Checks: 6 1

Knit directory: Paul_CX_2025/

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📌 Load Required Libraries

library(tidyverse) 
library(ggfortify)
library(ggplot2)
library(cluster)
library(edgeR)
library(limma)
library(Homo.sapiens)
library(BiocParallel)
library(qvalue)
library(pheatmap)
library(clusterProfiler)
library(AnnotationDbi)
library(org.Hs.eg.db)
library(RColorBrewer)
library(readr)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(DOSE)

# Load UCSC transcript database
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene

📌 0.1 Micromolar

📌 Read and Process Data

# Load the saved datasets
prob_1_0.1 <- read.csv("data/prob_1_0.1.csv")$Entrez_ID
prob_2_0.1 <- read.csv("data/prob_2_0.1.csv")$Entrez_ID
prob_3_0.1 <- read.csv("data/prob_3_0.1.csv")$Entrez_ID
prob_1_0.5 <- read.csv("data/prob_1_0.5.csv")$Entrez_ID
prob_2_0.5 <- read.csv("data/prob_2_0.5.csv")$Entrez_ID
prob_3_0.5 <- read.csv("data/prob_3_0.5.csv")$Entrez_ID
prob_4_0.5 <- read.csv("data/prob_4_0.5.csv")$Entrez_ID
prob_5_0.5 <- read.csv("data/prob_5_0.5.csv")$Entrez_ID


CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
background<-as.character(CX_0.1_3$Entrez_ID)

📌 Non response (0.1 µM)

📌 GO Enrichment Clusterprofiler

# 📦 Load Libraries
library(tidyverse)
library(patchwork)

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

library(data.table)

Warning: package 'data.table' was built under R version 4.3.3

# 📁 Load File
go_df <- fread("data/BP/CorMotif_Terms_Combined/GO_All_Non_response_(0.1).csv")

# 🧠 Convert and filter (only adj p < 0.05)
process_enrichment_tibble <- function(df, ontology, category_label) {
  df %>%
    filter(Ontology == ontology & p.adjust < 0.05) %>%
    mutate(neglog = -log(p.adjust)) %>%
    arrange(desc(neglog)) %>%
    slice(1:20) %>%
    mutate(Category = category_label)
}

# Create tibbles for each GO class
BP_Tibble <- process_enrichment_tibble(go_df, "BP", "Biological Process")
MF_Tibble <- process_enrichment_tibble(go_df, "MF", "Molecular Function")
CC_Tibble <- process_enrichment_tibble(go_df, "CC", "Cellular Component")

# 📊 Plot function
process_enrichment_plot <- function(tibble, title, color) {
  ggplot(tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log(p-adjust)", y = title,
      title = paste("Top 20", title, "GO Terms (adj p < 0.05)")
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(size = 12, face = "bold", colour = "black", angle = 45, hjust = 1),
      axis.text.y = element_text(size = 12, face = "bold", colour = "black"),
      axis.title.x = element_text(size = 14, face = "bold", colour = "black"),
      axis.title.y = element_text(size = 14, face = "bold", colour = "black"),
      plot.title = element_text(size = 14, face = "bold", colour = "black"),
      panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.3)
    ) +
    xlim(0, max(tibble$neglog, na.rm = TRUE) + 1)
}

# 🔧 Plot for each GO category
plot_BP <- process_enrichment_plot(BP_Tibble, "Biological Process", "#2E86C1")
#plot_MF <- process_enrichment_plot(MF_Tibble, "Molecular Function", "#28B463")
#plot_CC <- process_enrichment_plot(CC_Tibble, "Cellular Component", "#D35400")

# 🧩 Combine plots
combined_plot <- plot_BP
combined_plot

📌 CX_DOX_1

📌 GO Enrichment Clusterprofiler

# 📦 Load Libraries
library(tidyverse)
library(data.table)
library(patchwork)

# 📁 Load GO CSV for CX-DOX mid-late response (0.1)
go_df <- fread("data/BP/CorMotif_Terms_Combined/GO_All_CX-DOX_mid-late_response_(0.1).csv")

# 🧠 Convert and filter (only adj p < 0.05)
process_enrichment_tibble <- function(df, ontology, category_label) {
  df %>%
    filter(Ontology == ontology & p.adjust < 0.05) %>%
    mutate(neglog = -log(p.adjust)) %>%
    arrange(desc(neglog)) %>%
    slice(1:20) %>%
    mutate(Category = category_label)
}

# Create tibbles for each GO class
BP_Tibble <- process_enrichment_tibble(go_df, "BP", "Biological Process")
MF_Tibble <- process_enrichment_tibble(go_df, "MF", "Molecular Function")
CC_Tibble <- process_enrichment_tibble(go_df, "CC", "Cellular Component")

# 📊 Plot function
process_enrichment_plot <- function(tibble, title, color) {
  ggplot(tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log(p-adjust)", y = title,
      title = paste("Top 20", title, "GO Terms (adj p < 0.05)")
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(size = 12, face = "bold", colour = "black", angle = 45, hjust = 1),
      axis.text.y = element_text(size = 12, face = "bold", colour = "black"),
      axis.title.x = element_text(size = 14, face = "bold", colour = "black"),
      axis.title.y = element_text(size = 14, face = "bold", colour = "black"),
      plot.title = element_text(size = 14, face = "bold", colour = "black"),
      panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.3)
    ) +
    xlim(0, max(tibble$neglog, na.rm = TRUE) + 1)
}

# 🔧 Plot for each GO category
plot_BP <- process_enrichment_plot(BP_Tibble, "Biological Process", "#2E86C1")
plot_MF <- process_enrichment_plot(MF_Tibble, "Molecular Function", "#28B463")
plot_CC <- process_enrichment_plot(CC_Tibble, "Cellular Component", "#D35400")

# 🧩 Combine plots
combined_plot <- plot_BP
combined_plot

📌 DOX_sp_1

📌 GO Enrichment Clusterprofiler

# 📦 Load Required Libraries
library(tidyverse)
library(data.table)
library(patchwork)

# 📁 Load GO Enrichment CSV
go_df <- fread("data/BP/CorMotif_Terms_Combined/GO_All_DOX_only_mid-late_(0.1).csv")

# 🧠 Convert and filter (only adj p < 0.05)
process_enrichment_tibble <- function(df, ontology, category_label) {
  df %>%
    filter(Ontology == ontology & p.adjust < 0.05) %>%
    mutate(neglog = -log(p.adjust)) %>%
    arrange(desc(neglog)) %>%
    slice(1:20) %>%
    mutate(Category = category_label)
}

# Create tibbles for each GO class
BP_Tibble <- process_enrichment_tibble(go_df, "BP", "Biological Process")
MF_Tibble <- process_enrichment_tibble(go_df, "MF", "Molecular Function")
CC_Tibble <- process_enrichment_tibble(go_df, "CC", "Cellular Component")

# 📊 Plot function
process_enrichment_plot <- function(tibble, title, color) {
  ggplot(tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log(p-adjust)", y = title,
      title = paste("Top 20", title, "GO Terms (adj p < 0.05)")
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(size = 12, face = "bold", colour = "black", angle = 45, hjust = 1),
      axis.text.y = element_text(size = 12, face = "bold", colour = "black"),
      axis.title.x = element_text(size = 14, face = "bold", colour = "black"),
      axis.title.y = element_text(size = 14, face = "bold", colour = "black"),
      plot.title = element_text(size = 14, face = "bold", colour = "black"),
      panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.3)
    ) +
    xlim(0, max(tibble$neglog, na.rm = TRUE) + 1)
}

# 🔧 Plot for each GO category
plot_BP <- process_enrichment_plot(BP_Tibble, "Biological Process", "#2E86C1")

Warning in max(tibble$neglog, na.rm = TRUE): no non-missing arguments to max;
returning -Inf

plot_MF <- process_enrichment_plot(MF_Tibble, "Molecular Function", "#28B463")
plot_CC <- process_enrichment_plot(CC_Tibble, "Cellular Component", "#D35400")

# 🧩 Combine plots
combined_plot <- plot_BP
combined_plot

📌 Non response (0.5 µM)

📌 GO Enrichment Clusterprofiler

# 📦 Load Required Libraries
library(tidyverse)
library(data.table)
library(patchwork)

# 📁 Load Enrichment File
go_df <- fread("data/BP/CorMotif_Terms_Combined/GO_All_Non_response_(0.5).csv")

# 🧠 Process Enrichment (only adj p < 0.05)
process_enrichment_tibble <- function(df, ontology, category_label) {
  df %>%
    filter(Ontology == ontology & p.adjust < 0.05) %>%
    mutate(neglog = -log(p.adjust)) %>%
    arrange(desc(neglog)) %>%
    slice(1:20) %>%
    mutate(Category = category_label)
}

# ⛏️ Extract each GO class
BP_Tibble <- process_enrichment_tibble(go_df, "BP", "Biological Process")
MF_Tibble <- process_enrichment_tibble(go_df, "MF", "Molecular Function")
CC_Tibble <- process_enrichment_tibble(go_df, "CC", "Cellular Component")

# 📊 Plotting Function
process_enrichment_plot <- function(tibble, title, color) {
  ggplot(tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log(p-adjust)", y = title,
      title = paste("Top 20", title, "GO Terms (adj p < 0.05)")
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(size = 12, face = "bold", colour = "black", angle = 45, hjust = 1),
      axis.text.y = element_text(size = 12, face = "bold", colour = "black"),
      axis.title.x = element_text(size = 14, face = "bold", colour = "black"),
      axis.title.y = element_text(size = 14, face = "bold", colour = "black"),
      plot.title = element_text(size = 14, face = "bold", colour = "black"),
      panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.3)
    ) +
    xlim(0, max(tibble$neglog, na.rm = TRUE) + 1)
}

# 🎨 Generate GO Plots
plot_BP <- process_enrichment_plot(BP_Tibble, "Biological Process", "#2E86C1")
plot_MF <- process_enrichment_plot(MF_Tibble, "Molecular Function", "#28B463")
plot_CC <- process_enrichment_plot(CC_Tibble, "Cellular Component", "#D35400")

# 🧩 Combine & Show
combined_plot <- plot_BP
combined_plot

📌 DOX_sp_2

📌 GO Enrichment Clusterprofiler

# 📦 Load Required Libraries
library(tidyverse)
library(data.table)
library(patchwork)

# 📁 Load Enrichment File
go_df <- fread("data/BP/CorMotif_Terms_Combined/GO_All_DOX_specific_response_(0.5).csv")

# 🧠 Process Enrichment (only adj p < 0.05)
process_enrichment_tibble <- function(df, ontology, category_label) {
  df %>%
    filter(Ontology == ontology & p.adjust < 0.05) %>%
    mutate(neglog = -log(p.adjust)) %>%
    arrange(desc(neglog)) %>%
    slice(1:20) %>%
    mutate(Category = category_label)
}

# ⛏️ Extract each GO class
BP_Tibble <- process_enrichment_tibble(go_df, "BP", "Biological Process")
MF_Tibble <- process_enrichment_tibble(go_df, "MF", "Molecular Function")
CC_Tibble <- process_enrichment_tibble(go_df, "CC", "Cellular Component")

# 📊 Plotting Function
process_enrichment_plot <- function(tibble, title, color) {
  ggplot(tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log(p-adjust)", y = title,
      title = paste("Top 20", title, "GO Terms (adj p < 0.05)")
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(size = 12, face = "bold", colour = "black", angle = 45, hjust = 1),
      axis.text.y = element_text(size = 12, face = "bold", colour = "black"),
      axis.title.x = element_text(size = 14, face = "bold", colour = "black"),
      axis.title.y = element_text(size = 14, face = "bold", colour = "black"),
      plot.title = element_text(size = 14, face = "bold", colour = "black"),
      panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.3)
    ) +
    xlim(0, max(tibble$neglog, na.rm = TRUE) + 1)
}

# 🎨 Generate GO Plots
plot_BP <- process_enrichment_plot(BP_Tibble, "Biological Process", "#2E86C1")

Warning in max(tibble$neglog, na.rm = TRUE): no non-missing arguments to max;
returning -Inf

plot_MF <- process_enrichment_plot(MF_Tibble, "Molecular Function", "#28B463")
plot_CC <- process_enrichment_plot(CC_Tibble, "Cellular Component", "#D35400")

# 🧩 Combine & Show
combined_plot <- plot_BP
combined_plot

📌 DOX_sp_3

📌 GO Enrichment Clusterprofiler

# 📦 Load Required Libraries
library(tidyverse)
library(data.table)
library(patchwork)

# 📁 Load Enrichment File
go_df <- fread("data/BP/CorMotif_Terms_Combined/GO_All_DOX_only_mid-late_response_(0.5).csv")

# 🧠 Process Enrichment (only adj p < 0.05)
process_enrichment_tibble <- function(df, ontology, category_label) {
  df %>%
    filter(Ontology == ontology & p.adjust < 0.05) %>%
    mutate(neglog = -log(p.adjust)) %>%
    arrange(desc(neglog)) %>%
    slice(1:20) %>%
    mutate(Category = category_label)
}

# ⛏️ Extract each GO class
BP_Tibble <- process_enrichment_tibble(go_df, "BP", "Biological Process")
MF_Tibble <- process_enrichment_tibble(go_df, "MF", "Molecular Function")
CC_Tibble <- process_enrichment_tibble(go_df, "CC", "Cellular Component")

# 📊 Plotting Function
process_enrichment_plot <- function(tibble, title, color) {
  ggplot(tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log(p-adjust)", y = title,
      title = paste("Top 20", title, "GO Terms (adj p < 0.05)")
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(size = 12, face = "bold", colour = "black", angle = 45, hjust = 1),
      axis.text.y = element_text(size = 12, face = "bold", colour = "black"),
      axis.title.x = element_text(size = 14, face = "bold", colour = "black"),
      axis.title.y = element_text(size = 14, face = "bold", colour = "black"),
      plot.title = element_text(size = 14, face = "bold", colour = "black"),
      panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.3)
    ) +
    xlim(0, max(tibble$neglog, na.rm = TRUE) + 1)
}

# 🎨 Generate GO Plots
plot_BP <- process_enrichment_plot(BP_Tibble, "Biological Process", "#2E86C1")
plot_MF <- process_enrichment_plot(MF_Tibble, "Molecular Function", "#28B463")

Warning in max(tibble$neglog, na.rm = TRUE): no non-missing arguments to max;
returning -Inf

plot_CC <- process_enrichment_plot(CC_Tibble, "Cellular Component", "#D35400")

Warning in max(tibble$neglog, na.rm = TRUE): no non-missing arguments to max;
returning -Inf

# 🧩 Combine & Show
combined_plot <- plot_BP
combined_plot

📌 CX_DOX_2

📌 GO Enrichment Clusterprofiler

# 📦 Load Required Libraries
library(tidyverse)
library(data.table)
library(patchwork)

# 📁 Load Enrichment File
go_df <- fread("data/BP/CorMotif_Terms_Combined/GO_All_CX_total_+_DOX_early_response_(0.5).csv")

# 🧠 Process Enrichment (only adj p < 0.05)
process_enrichment_tibble <- function(df, ontology, category_label) {
  df %>%
    filter(Ontology == ontology & p.adjust < 0.05) %>%
    mutate(neglog = -log(p.adjust)) %>%
    arrange(desc(neglog)) %>%
    slice(1:20) %>%
    mutate(Category = category_label)
}

# ⛏️ Extract each GO class
BP_Tibble <- process_enrichment_tibble(go_df, "BP", "Biological Process")
MF_Tibble <- process_enrichment_tibble(go_df, "MF", "Molecular Function")
CC_Tibble <- process_enrichment_tibble(go_df, "CC", "Cellular Component")

# 📊 Plotting Function
process_enrichment_plot <- function(tibble, title, color) {
  ggplot(tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log(p-adjust)", y = title,
      title = paste("Top 20", title, "GO Terms (adj p < 0.05)")
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(size = 12, face = "bold", colour = "black", angle = 45, hjust = 1),
      axis.text.y = element_text(size = 12, face = "bold", colour = "black"),
      axis.title.x = element_text(size = 14, face = "bold", colour = "black"),
      axis.title.y = element_text(size = 14, face = "bold", colour = "black"),
      plot.title = element_text(size = 14, face = "bold", colour = "black"),
      panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.3)
    ) +
    xlim(0, max(tibble$neglog, na.rm = TRUE) + 1)
}

# 🎨 Generate GO Plots
plot_BP <- process_enrichment_plot(BP_Tibble, "Biological Process", "#2E86C1")
plot_MF <- process_enrichment_plot(MF_Tibble, "Molecular Function", "#28B463")
plot_CC <- process_enrichment_plot(CC_Tibble, "Cellular Component", "#D35400")

# 🧩 Combine & Show
combined_plot <- plot_BP
combined_plot

📌 CX_DOX_3

📌 GO Enrichment Clusterprofiler

# 📦 Load Required Libraries
library(tidyverse)
library(data.table)
library(patchwork)

# 📁 Load Enrichment File
go_df <- fread("data/BP/CorMotif_Terms_Combined/GO_All_DOX_early_+_CX-DOX_mid-late_response_(0.5).csv")

# 🧠 Process Enrichment (only adj p < 0.05)
process_enrichment_tibble <- function(df, ontology, category_label) {
  df %>%
    filter(Ontology == ontology & p.adjust < 0.05) %>%
    mutate(neglog = -log(p.adjust)) %>%
    arrange(desc(neglog)) %>%
    slice(1:20) %>%
    mutate(Category = category_label)
}

# ⛏️ Extract each GO class
BP_Tibble <- process_enrichment_tibble(go_df, "BP", "Biological Process")
MF_Tibble <- process_enrichment_tibble(go_df, "MF", "Molecular Function")
CC_Tibble <- process_enrichment_tibble(go_df, "CC", "Cellular Component")

# 📊 Plotting Function
process_enrichment_plot <- function(tibble, title, color) {
  ggplot(tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log(p-adjust)", y = title,
      title = paste("Top 20", title, "GO Terms (adj p < 0.05)")
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(size = 12, face = "bold", colour = "black", angle = 45, hjust = 1),
      axis.text.y = element_text(size = 12, face = "bold", colour = "black"),
      axis.title.x = element_text(size = 14, face = "bold", colour = "black"),
      axis.title.y = element_text(size = 14, face = "bold", colour = "black"),
      plot.title = element_text(size = 14, face = "bold", colour = "black"),
      panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.3)
    ) +
    xlim(0, max(tibble$neglog, na.rm = TRUE) + 1)
}

# 🎨 Generate GO Plots
plot_BP <- process_enrichment_plot(BP_Tibble, "Biological Process", "#2E86C1")
plot_MF <- process_enrichment_plot(MF_Tibble, "Molecular Function", "#28B463")
plot_CC <- process_enrichment_plot(CC_Tibble, "Cellular Component", "#D35400")

# 🧩 Combine & Show
combined_plot <- plot_BP
combined_plot

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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] data.table_1.17.0                       
 [2] patchwork_1.3.0                         
 [3] DOSE_3.28.2                             
 [4] TxDb.Hsapiens.UCSC.hg38.knownGene_3.18.0
 [5] RColorBrewer_1.1-3                      
 [6] clusterProfiler_4.10.1                  
 [7] pheatmap_1.0.12                         
 [8] qvalue_2.34.0                           
 [9] BiocParallel_1.36.0                     
[10] Homo.sapiens_1.3.1                      
[11] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 
[12] org.Hs.eg.db_3.18.0                     
[13] GO.db_3.18.0                            
[14] OrganismDbi_1.44.0                      
[15] GenomicFeatures_1.54.4                  
[16] GenomicRanges_1.54.1                    
[17] GenomeInfoDb_1.38.8                     
[18] AnnotationDbi_1.64.1                    
[19] IRanges_2.36.0                          
[20] S4Vectors_0.40.2                        
[21] Biobase_2.62.0                          
[22] BiocGenerics_0.48.1                     
[23] edgeR_4.0.16                            
[24] limma_3.58.1                            
[25] cluster_2.1.8.1                         
[26] ggfortify_0.4.17                        
[27] lubridate_1.9.4                         
[28] forcats_1.0.0                           
[29] stringr_1.5.1                           
[30] dplyr_1.1.4                             
[31] purrr_1.0.4                             
[32] readr_2.1.5                             
[33] tidyr_1.3.1                             
[34] tibble_3.2.1                            
[35] ggplot2_3.5.2                           
[36] tidyverse_2.0.0                         

loaded via a namespace (and not attached):
  [1] splines_4.3.0               later_1.3.2                
  [3] BiocIO_1.12.0               bitops_1.0-9               
  [5] ggplotify_0.1.2             filelock_1.0.3             
  [7] polyclip_1.10-7             graph_1.80.0               
  [9] XML_3.99-0.18               lifecycle_1.0.4            
 [11] rprojroot_2.0.4             lattice_0.22-7             
 [13] MASS_7.3-60                 magrittr_2.0.3             
 [15] sass_0.4.10                 rmarkdown_2.29             
 [17] jquerylib_0.1.4             yaml_2.3.10                
 [19] httpuv_1.6.15               cowplot_1.1.3              
 [21] DBI_1.2.3                   abind_1.4-8                
 [23] zlibbioc_1.48.2             ggraph_2.2.1               
 [25] RCurl_1.98-1.17             yulab.utils_0.2.0          
 [27] tweenr_2.0.3                rappdirs_0.3.3             
 [29] git2r_0.36.2                GenomeInfoDbData_1.2.11    
 [31] enrichplot_1.22.0           ggrepel_0.9.6              
 [33] tidytree_0.4.6              codetools_0.2-20           
 [35] DelayedArray_0.28.0         xml2_1.3.8                 
 [37] ggforce_0.4.2               tidyselect_1.2.1           
 [39] aplot_0.2.5                 farver_2.1.2               
 [41] viridis_0.6.5               matrixStats_1.5.0          
 [43] BiocFileCache_2.10.2        GenomicAlignments_1.38.2   
 [45] jsonlite_2.0.0              tidygraph_1.3.1            
 [47] tools_4.3.0                 progress_1.2.3             
 [49] treeio_1.26.0               Rcpp_1.0.12                
 [51] glue_1.7.0                  gridExtra_2.3              
 [53] SparseArray_1.2.4           xfun_0.52                  
 [55] MatrixGenerics_1.14.0       withr_3.0.2                
 [57] BiocManager_1.30.25         fastmap_1.2.0              
 [59] digest_0.6.34               timechange_0.3.0           
 [61] R6_2.6.1                    gridGraphics_0.5-1         
 [63] colorspace_2.1-0            biomaRt_2.58.2             
 [65] RSQLite_2.3.9               generics_0.1.3             
 [67] rtracklayer_1.62.0          prettyunits_1.2.0          
 [69] graphlayouts_1.2.2          httr_1.4.7                 
 [71] S4Arrays_1.2.1              scatterpie_0.2.4           
 [73] pkgconfig_2.0.3             gtable_0.3.6               
 [75] blob_1.2.4                  workflowr_1.7.1            
 [77] XVector_0.42.0              shadowtext_0.1.4           
 [79] htmltools_0.5.8.1           fgsea_1.28.0               
 [81] RBGL_1.78.0                 scales_1.3.0               
 [83] png_0.1-8                   ggfun_0.1.8                
 [85] knitr_1.50                  rstudioapi_0.17.1          
 [87] tzdb_0.5.0                  reshape2_1.4.4             
 [89] rjson_0.2.23                nlme_3.1-168               
 [91] curl_6.2.2                  cachem_1.1.0               
 [93] parallel_4.3.0              HDO.db_0.99.1              
 [95] restfulr_0.0.15             pillar_1.10.2              
 [97] grid_4.3.0                  vctrs_0.6.5                
 [99] promises_1.3.2              dbplyr_2.5.0               
[101] evaluate_1.0.3              cli_3.6.1                  
[103] locfit_1.5-9.12             compiler_4.3.0             
[105] Rsamtools_2.18.0            rlang_1.1.3                
[107] crayon_1.5.3                labeling_0.4.3             
[109] plyr_1.8.9                  fs_1.6.3                   
[111] stringi_1.8.3               viridisLite_0.4.2          
[113] munsell_0.5.1               Biostrings_2.70.3          
[115] lazyeval_0.2.2              GOSemSim_2.28.1            
[117] Matrix_1.6-1.1              hms_1.1.3                  
[119] bit64_4.6.0-1               KEGGREST_1.42.0            
[121] statmod_1.5.0               SummarizedExperiment_1.32.0
[123] igraph_2.1.4                memoise_2.0.1              
[125] bslib_0.9.0                 ggtree_3.10.1              
[127] fastmatch_1.1-6             bit_4.6.0                  
[129] gson_0.1.0                  ape_5.8-1