Function and Pathway Enrichment Analysis of Corrmotifs (Concentration)

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Last updated: 2025-06-06

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Knit directory: CX5461_Project/

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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 / plot_MF / plot_CC
combined_plot

Past versions of GO1-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
87137ac	sayanpaul01	2025-02-27

📌 GO Enrichment g:Profiler

# Load the gprofiler2 package
library(gprofiler2)

Warning: package 'gprofiler2' was built under R version 4.3.3

library(ggplot2)
library(dplyr)
library(patchwork)

# Perform GO enrichment analysis with gprofiler2
gost_results <- gost(
  query = prob_1_0.1,           # List of input genes (prob_all_1)
  organism = "hsapiens",   # Human organism
  user_threshold = 0.05,   # Adjusted p-value cutoff
  correction_method = "fdr", # Multiple testing correction
  domain_scope = "custom", # Use custom background
  custom_bg = background,    # Background set of genes
  sources = c("GO:BP", "GO:MF", "GO:CC") # Analyze GO categories
)

# Check if enrichment results exist
if (is.null(gost_results$result) || nrow(gost_results$result) == 0) {
  # If no enriched terms, create a placeholder dataframe
  combined_results <- tibble(
    term_name = "No enriched terms",
    p.adjust = NA,
    source = "N/A",
    Category = "N/A"
  )
} else {
  # Convert results to a data frame
  gost_results_df <- gost_results$result

  # Add a column for adjusted p-value
  gost_results_df <- gost_results_df %>%
    rename(p.adjust = p_value)

  # Separate results for BP, MF, and CC
  BP_results <- gost_results_df %>%
    filter(source == "GO:BP") %>%
    mutate(Category = "Biological Process")

  MF_results <- gost_results_df %>%
    filter(source == "GO:MF") %>%
    mutate(Category = "Molecular Function")

  CC_results <- gost_results_df %>%
    filter(source == "GO:CC") %>%
    mutate(Category = "Cellular Component")

  # Select the top 20 terms by adjusted p-value for each category
  top_BP <- BP_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_MF <- MF_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_CC <- CC_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  # Combine all categories
  combined_results <- bind_rows(top_BP, top_MF, top_CC)
}

# Ensure all columns are atomic types for CSV export
combined_results_clean <- combined_results %>%
  mutate(across(everything(), ~ if (is.list(.)) sapply(., toString) else .))

# Function for plotting top terms
plot_gprofiler_results <- function(data, title, color) {
  ggplot(data, aes(x = -log10(p.adjust), y = reorder(term_name, -log10(p.adjust)))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log10(Adjusted p-value)",
      y = title,
      title = paste("Top 20", title, "GO Terms")
    ) +
    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, size = 0.3)
    )
}

# Check if there are enrichment terms to plot
if (nrow(combined_results) == 1 && combined_results$term_name == "No enriched terms") {
  message("No enriched GO terms found for the input gene set.")
} else {
  # Plot the top 20 terms for each category
  plot_BP <- plot_gprofiler_results(top_BP, "Biological Process", "#2E86C1")
  plot_MF <- plot_gprofiler_results(top_MF, "Molecular Function", "#28B463")
  plot_CC <- plot_gprofiler_results(top_CC, "Cellular Component", "#D35400")

  # Combine the plots using patchwork
  combined_plot <- plot_BP / plot_MF / plot_CC

  # Display the combined plot
  combined_plot
}

Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

Past versions of gprofiler_GO-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
87137ac	sayanpaul01	2025-02-27

📌 Pathway Enrichment

# Load required libraries
library(clusterProfiler)
library(org.Hs.eg.db) # Required for enrichPathway
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)
library(ReactomePA)

Warning: package 'ReactomePA' was built under R version 4.3.1

# Function for ClusterProfiler Reactome & KEGG Analysis
process_clusterProfiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment based on the selected category
  if (category == "Reactome") {
    enrichment <- enrichPathway(
      gene = gene_set,
      organism = "human",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  } else if (category == "KEGG") {
    enrichment <- enrichKEGG(
      gene = gene_set,
      organism = "hsa",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  }
  
  # Check if enrichment results exist
  if (is.null(enrichment) || nrow(as.data.frame(enrichment)) == 0) {
    message(paste("No significant enrichment found for", category, "in ClusterProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- as_tibble(as.data.frame(enrichment)) %>%
    mutate(Category = category,
           neglog = -log10(p.adjust)) %>%  # Compute -log10(p.adjust)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(Adjusted p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Function for gProfiler Reactome & KEGG Analysis
process_gprofiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment using gprofiler2
  enrichment <- gost(
    query = gene_set,
    organism = "hsapiens",
    user_threshold = 0.05,
    correction_method = "fdr",
    domain_scope = "custom",
    custom_bg = background,
    sources = category # Either "REAC" or "KEGG"
  )
  
  # Check if enrichment results exist
  if (is.null(enrichment$result) || nrow(enrichment$result) == 0) {
    message(paste("No significant enrichment found for", category, "in gProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- enrichment$result %>%
    as_tibble() %>%
    mutate(Category = category,
           neglog = -log10(p_value)) %>%  # Compute -log10(p-value)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(term_name, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Perform analysis for Reactome and KEGG using ClusterProfiler
cluster_reactome <- process_clusterProfiler(
  gene_set = prob_1_0.1,
  background = background,
  category = "Reactome",
  color = "#2E86C1",
  y_title = "Reactome Pathways"
)

cluster_kegg <- process_clusterProfiler(
  gene_set = prob_1_0.1,
  background = background,
  category = "KEGG",
  color = "#28B463",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for ClusterProfiler
if (!is.null(cluster_reactome) && !is.null(cluster_kegg)) {
  cluster_combined <- cluster_reactome / cluster_kegg
} else if (!is.null(cluster_reactome)) {
  cluster_combined <- cluster_reactome
} else if (!is.null(cluster_kegg)) {
  cluster_combined <- cluster_kegg
} else {
  cluster_combined <- NULL
}

# Perform analysis for Reactome and KEGG using GProfiler
gprofiler_reactome <- process_gprofiler(
  gene_set = prob_1_0.1,
  background = background,
  category = "REAC",  # Corrected category for Reactome in gProfiler
  color = "#D35400",
  y_title = "Reactome Pathways"
)

gprofiler_kegg <- process_gprofiler(
  gene_set = prob_1_0.1,
  background = background,
  category = "KEGG",
  color = "#F39C12",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for GProfiler
if (!is.null(gprofiler_reactome) && !is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_reactome / gprofiler_kegg
} else if (!is.null(gprofiler_reactome)) {
  gprofiler_combined <- gprofiler_reactome
} else if (!is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_kegg
} else {
  gprofiler_combined <- NULL
}

# Display plots (if they are not NULL)
if (!is.null(cluster_combined)) print(cluster_combined)

Past versions of pathway_enrichment-1.png

Version	Author	Date
df65b82	sayanpaul01	2025-06-04
84d7de5	sayanpaul01	2025-06-03
87137ac	sayanpaul01	2025-02-27

if (!is.null(gprofiler_combined)) print(gprofiler_combined)

Past versions of pathway_enrichment-2.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
87137ac	sayanpaul01	2025-02-27

📌 CX-DOX mid-late response (0.1 µM)

📌 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 / plot_MF / plot_CC
combined_plot

Past versions of GO2-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
87137ac	sayanpaul01	2025-02-27

📌 GO Enrichment g:Profiler

# Load the gprofiler2 package
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)

# Perform GO enrichment analysis with gprofiler2
gost_results <- gost(
  query = prob_2_0.1,       
  organism = "hsapiens",   # Human organism
  user_threshold = 0.05,   # Adjusted p-value cutoff
  correction_method = "fdr", # Multiple testing correction
  domain_scope = "custom", # Use custom background
  custom_bg = background,    # Background set of genes
  sources = c("GO:BP", "GO:MF", "GO:CC") # Analyze GO categories
)

# Check if enrichment results exist
if (is.null(gost_results$result) || nrow(gost_results$result) == 0) {
  # If no enriched terms, create a placeholder dataframe
  combined_results <- tibble(
    term_name = "No enriched terms",
    p.adjust = NA,
    source = "N/A",
    Category = "N/A"
  )
} else {
  # Convert results to a data frame
  gost_results_df <- gost_results$result

  # Add a column for adjusted p-value
  gost_results_df <- gost_results_df %>%
    rename(p.adjust = p_value)

  # Separate results for BP, MF, and CC
  BP_results <- gost_results_df %>%
    filter(source == "GO:BP") %>%
    mutate(Category = "Biological Process")

  MF_results <- gost_results_df %>%
    filter(source == "GO:MF") %>%
    mutate(Category = "Molecular Function")

  CC_results <- gost_results_df %>%
    filter(source == "GO:CC") %>%
    mutate(Category = "Cellular Component")

  # Select the top 20 terms by adjusted p-value for each category
  top_BP <- BP_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_MF <- MF_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_CC <- CC_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  # Combine all categories
  combined_results <- bind_rows(top_BP, top_MF, top_CC)
}

# Ensure all columns are atomic types for CSV export
combined_results_clean <- combined_results %>%
  mutate(across(everything(), ~ if (is.list(.)) sapply(., toString) else .))

# Function for plotting top terms
plot_gprofiler_results <- function(data, title, color) {
  ggplot(data, aes(x = -log10(p.adjust), y = reorder(term_name, -log10(p.adjust)))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log10(Adjusted p-value)",
      y = title,
      title = paste("Top 20", title, "GO Terms")
    ) +
    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, size = 0.3)
    )
}

# Check if there are enrichment terms to plot
if (nrow(combined_results) == 1 && combined_results$term_name == "No enriched terms") {
  message("No enriched GO terms found for the input gene set.")
} else {
  # Plot the top 20 terms for each category
  plot_BP <- plot_gprofiler_results(top_BP, "Biological Process", "#2E86C1")
  plot_MF <- plot_gprofiler_results(top_MF, "Molecular Function", "#28B463")
  plot_CC <- plot_gprofiler_results(top_CC, "Cellular Component", "#D35400")

  # Combine the plots using patchwork
  combined_plot <- plot_BP / plot_MF / plot_CC

  # Display the combined plot
  combined_plot
}

Past versions of gprofiler_GO2-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
87137ac	sayanpaul01	2025-02-27

📌 Pathway Enrichment

# Load required libraries
library(clusterProfiler)
library(org.Hs.eg.db) # Required for enrichPathway
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)
library(ReactomePA)

# Function for ClusterProfiler Reactome & KEGG Analysis
process_clusterProfiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment based on the selected category
  if (category == "Reactome") {
    enrichment <- enrichPathway(
      gene = gene_set,
      organism = "human",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  } else if (category == "KEGG") {
    enrichment <- enrichKEGG(
      gene = gene_set,
      organism = "hsa",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  }
  
  # Check if enrichment results exist
  if (is.null(enrichment) || nrow(as.data.frame(enrichment)) == 0) {
    message(paste("No significant enrichment found for", category, "in ClusterProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- as_tibble(as.data.frame(enrichment)) %>%
    mutate(Category = category,
           neglog = -log10(p.adjust)) %>%  # Compute -log10(p.adjust)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(Adjusted p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Function for gProfiler Reactome & KEGG Analysis
process_gprofiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment using gprofiler2
  enrichment <- gost(
    query = gene_set,
    organism = "hsapiens",
    user_threshold = 0.05,
    correction_method = "fdr",
    domain_scope = "custom",
    custom_bg = background,
    sources = category # Either "REAC" or "KEGG"
  )
  
  # Check if enrichment results exist
  if (is.null(enrichment$result) || nrow(enrichment$result) == 0) {
    message(paste("No significant enrichment found for", category, "in gProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- enrichment$result %>%
    as_tibble() %>%
    mutate(Category = category,
           neglog = -log10(p_value)) %>%  # Compute -log10(p-value)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(term_name, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Perform analysis for Reactome and KEGG using ClusterProfiler
cluster_reactome <- process_clusterProfiler(
  gene_set = prob_2_0.1,
  background = background,
  category = "Reactome",
  color = "#2E86C1",
  y_title = "Reactome Pathways"
)

cluster_kegg <- process_clusterProfiler(
  gene_set = prob_2_0.1,
  background = background,
  category = "KEGG",
  color = "#28B463",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for ClusterProfiler
if (!is.null(cluster_reactome) && !is.null(cluster_kegg)) {
  cluster_combined <- cluster_reactome / cluster_kegg
} else if (!is.null(cluster_reactome)) {
  cluster_combined <- cluster_reactome
} else if (!is.null(cluster_kegg)) {
  cluster_combined <- cluster_kegg
} else {
  cluster_combined <- NULL
}

# Perform analysis for Reactome and KEGG using GProfiler
gprofiler_reactome <- process_gprofiler(
  gene_set = prob_2_0.1,
  background = background,
  category = "REAC",  # Corrected category for Reactome in gProfiler
  color = "#D35400",
  y_title = "Reactome Pathways"
)

gprofiler_kegg <- process_gprofiler(
  gene_set = prob_2_0.1,
  background = background,
  category = "KEGG",
  color = "#F39C12",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for GProfiler
if (!is.null(gprofiler_reactome) && !is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_reactome / gprofiler_kegg
} else if (!is.null(gprofiler_reactome)) {
  gprofiler_combined <- gprofiler_reactome
} else if (!is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_kegg
} else {
  gprofiler_combined <- NULL
}

# Display plots (if they are not NULL)
if (!is.null(cluster_combined)) print(cluster_combined)

Past versions of pathway_enrichment2-1.png

Version	Author	Date
df65b82	sayanpaul01	2025-06-04
84d7de5	sayanpaul01	2025-06-03
cf95313	sayanpaul01	2025-03-10
87137ac	sayanpaul01	2025-02-27

if (!is.null(gprofiler_combined)) print(gprofiler_combined)

Past versions of pathway_enrichment2-2.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
87137ac	sayanpaul01	2025-02-27

📌 DOX only mid-late (0.1 µM)

📌 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 / plot_MF / plot_CC
combined_plot

Past versions of GO3-1.png

Version	Author	Date
6484005	sayanpaul01	2025-02-27

📌 GO Enrichment g:Profiler

# Load the gprofiler2 package
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)

# Perform GO enrichment analysis with gprofiler2
gost_results <- gost(
  query = prob_3_0.1,           # List of input genes (prob_all_1)
  organism = "hsapiens",   # Human organism
  user_threshold = 0.05,   # Adjusted p-value cutoff
  correction_method = "fdr", # Multiple testing correction
  domain_scope = "custom", # Use custom background
  custom_bg = background,    # Background set of genes
  sources = c("GO:BP", "GO:MF", "GO:CC") # Analyze GO categories
)

# Check if enrichment results exist
if (is.null(gost_results$result) || nrow(gost_results$result) == 0) {
  # If no enriched terms, create a placeholder dataframe
  combined_results <- tibble(
    term_name = "No enriched terms",
    p.adjust = NA,
    source = "N/A",
    Category = "N/A"
  )
} else {
  # Convert results to a data frame
  gost_results_df <- gost_results$result

  # Add a column for adjusted p-value
  gost_results_df <- gost_results_df %>%
    rename(p.adjust = p_value)

  # Separate results for BP, MF, and CC
  BP_results <- gost_results_df %>%
    filter(source == "GO:BP") %>%
    mutate(Category = "Biological Process")

  MF_results <- gost_results_df %>%
    filter(source == "GO:MF") %>%
    mutate(Category = "Molecular Function")

  CC_results <- gost_results_df %>%
    filter(source == "GO:CC") %>%
    mutate(Category = "Cellular Component")

  # Select the top 20 terms by adjusted p-value for each category
  top_BP <- BP_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_MF <- MF_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_CC <- CC_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  # Combine all categories
  combined_results <- bind_rows(top_BP, top_MF, top_CC)
}

# Ensure all columns are atomic types for CSV export
combined_results_clean <- combined_results %>%
  mutate(across(everything(), ~ if (is.list(.)) sapply(., toString) else .))

# Function for plotting top terms
plot_gprofiler_results <- function(data, title, color) {
  ggplot(data, aes(x = -log10(p.adjust), y = reorder(term_name, -log10(p.adjust)))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log10(Adjusted p-value)",
      y = title,
      title = paste("Top 20", title, "GO Terms")
    ) +
    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, size = 0.3)
    )
}

# Check if there are enrichment terms to plot
if (nrow(combined_results) == 1 && combined_results$term_name == "No enriched terms") {
  message("No enriched GO terms found for the input gene set.")
} else {
  # Plot the top 20 terms for each category
  plot_BP <- plot_gprofiler_results(top_BP, "Biological Process", "#2E86C1")
  plot_MF <- plot_gprofiler_results(top_MF, "Molecular Function", "#28B463")
  plot_CC <- plot_gprofiler_results(top_CC, "Cellular Component", "#D35400")

  # Combine the plots using patchwork
  combined_plot <- plot_BP / plot_MF / plot_CC

  # Display the combined plot
  combined_plot
}

Past versions of gprofiler_GO3-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
6484005	sayanpaul01	2025-02-27

📌 Pathway Enrichment

# Load required libraries
library(clusterProfiler)
library(org.Hs.eg.db) # Required for enrichPathway
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)
library(ReactomePA)

# Function for ClusterProfiler Reactome & KEGG Analysis
process_clusterProfiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment based on the selected category
  if (category == "Reactome") {
    enrichment <- enrichPathway(
      gene = gene_set,
      organism = "human",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  } else if (category == "KEGG") {
    enrichment <- enrichKEGG(
      gene = gene_set,
      organism = "hsa",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  }
  
  # Check if enrichment results exist
  if (is.null(enrichment) || nrow(as.data.frame(enrichment)) == 0) {
    message(paste("No significant enrichment found for", category, "in ClusterProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- as_tibble(as.data.frame(enrichment)) %>%
    mutate(Category = category,
           neglog = -log10(p.adjust)) %>%  # Compute -log10(p.adjust)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(Adjusted p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Function for gProfiler Reactome & KEGG Analysis
process_gprofiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment using gprofiler2
  enrichment <- gost(
    query = gene_set,
    organism = "hsapiens",
    user_threshold = 0.05,
    correction_method = "fdr",
    domain_scope = "custom",
    custom_bg = background,
    sources = category # Either "REAC" or "KEGG"
  )
  
  # Check if enrichment results exist
  if (is.null(enrichment$result) || nrow(enrichment$result) == 0) {
    message(paste("No significant enrichment found for", category, "in gProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- enrichment$result %>%
    as_tibble() %>%
    mutate(Category = category,
           neglog = -log10(p_value)) %>%  # Compute -log10(p-value)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(term_name, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Perform analysis for Reactome and KEGG using ClusterProfiler
cluster_reactome <- process_clusterProfiler(
  gene_set = prob_3_0.1,
  background = background,
  category = "Reactome",
  color = "#2E86C1",
  y_title = "Reactome Pathways"
)

cluster_kegg <- process_clusterProfiler(
  gene_set = prob_3_0.1,
  background = background,
  category = "KEGG",
  color = "#28B463",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for ClusterProfiler
if (!is.null(cluster_reactome) && !is.null(cluster_kegg)) {
  cluster_combined <- cluster_reactome / cluster_kegg
} else if (!is.null(cluster_reactome)) {
  cluster_combined <- cluster_reactome
} else if (!is.null(cluster_kegg)) {
  cluster_combined <- cluster_kegg
} else {
  cluster_combined <- NULL
}

# Perform analysis for Reactome and KEGG using GProfiler
gprofiler_reactome <- process_gprofiler(
  gene_set = prob_3_0.1,
  background = background,
  category = "REAC",  # Corrected category for Reactome in gProfiler
  color = "#D35400",
  y_title = "Reactome Pathways"
)

gprofiler_kegg <- process_gprofiler(
  gene_set = prob_3_0.1,
  background = background,
  category = "KEGG",
  color = "#F39C12",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for GProfiler
if (!is.null(gprofiler_reactome) && !is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_reactome / gprofiler_kegg
} else if (!is.null(gprofiler_reactome)) {
  gprofiler_combined <- gprofiler_reactome
} else if (!is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_kegg
} else {
  gprofiler_combined <- NULL
}

# Display plots (if they are not NULL)
if (!is.null(cluster_combined)) print(cluster_combined)

Past versions of pathway_enrichment3-1.png

Version	Author	Date
df65b82	sayanpaul01	2025-06-04
84d7de5	sayanpaul01	2025-06-03
cf95313	sayanpaul01	2025-03-10
6484005	sayanpaul01	2025-02-27

if (!is.null(gprofiler_combined)) print(gprofiler_combined)

Past versions of pathway_enrichment3-2.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
6484005	sayanpaul01	2025-02-27

📌 0.5 Micromolar

📌 Read and Process Data

# Load the saved datasets
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 (0.5 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 / plot_MF / plot_CC
combined_plot

Past versions of GO4-1.png

Version	Author	Date
11895c8	sayanpaul01	2025-02-27

📌 GO Enrichment g:Profiler

# Load the gprofiler2 package
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)

# Perform GO enrichment analysis with gprofiler2
gost_results <- gost(
  query = prob_1_0.5,           # List of input genes (prob_all_1)
  organism = "hsapiens",   # Human organism
  user_threshold = 0.05,   # Adjusted p-value cutoff
  correction_method = "fdr", # Multiple testing correction
  domain_scope = "custom", # Use custom background
  custom_bg = background,    # Background set of genes
  sources = c("GO:BP", "GO:MF", "GO:CC") # Analyze GO categories
)

# Check if enrichment results exist
if (is.null(gost_results$result) || nrow(gost_results$result) == 0) {
  # If no enriched terms, create a placeholder dataframe
  combined_results <- tibble(
    term_name = "No enriched terms",
    p.adjust = NA,
    source = "N/A",
    Category = "N/A"
  )
} else {
  # Convert results to a data frame
  gost_results_df <- gost_results$result

  # Add a column for adjusted p-value
  gost_results_df <- gost_results_df %>%
    rename(p.adjust = p_value)

  # Separate results for BP, MF, and CC
  BP_results <- gost_results_df %>%
    filter(source == "GO:BP") %>%
    mutate(Category = "Biological Process")

  MF_results <- gost_results_df %>%
    filter(source == "GO:MF") %>%
    mutate(Category = "Molecular Function")

  CC_results <- gost_results_df %>%
    filter(source == "GO:CC") %>%
    mutate(Category = "Cellular Component")

  # Select the top 20 terms by adjusted p-value for each category
  top_BP <- BP_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_MF <- MF_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_CC <- CC_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  # Combine all categories
  combined_results <- bind_rows(top_BP, top_MF, top_CC)
}

# Ensure all columns are atomic types for CSV export
combined_results_clean <- combined_results %>%
  mutate(across(everything(), ~ if (is.list(.)) sapply(., toString) else .))

# Function for plotting top terms
plot_gprofiler_results <- function(data, title, color) {
  ggplot(data, aes(x = -log10(p.adjust), y = reorder(term_name, -log10(p.adjust)))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log10(Adjusted p-value)",
      y = title,
      title = paste("Top 20", title, "GO Terms")
    ) +
    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, size = 0.3)
    )
}

# Check if there are enrichment terms to plot
if (nrow(combined_results) == 1 && combined_results$term_name == "No enriched terms") {
  message("No enriched GO terms found for the input gene set.")
} else {
  # Plot the top 20 terms for each category
  plot_BP <- plot_gprofiler_results(top_BP, "Biological Process", "#2E86C1")
  plot_MF <- plot_gprofiler_results(top_MF, "Molecular Function", "#28B463")
  plot_CC <- plot_gprofiler_results(top_CC, "Cellular Component", "#D35400")

  # Combine the plots using patchwork
  combined_plot <- plot_BP / plot_MF / plot_CC

  # Display the combined plot
  combined_plot
}

Past versions of gprofiler_GO4-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 Pathway Enrichment

# Load required libraries
library(clusterProfiler)
library(org.Hs.eg.db) # Required for enrichPathway
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)
library(ReactomePA)

# Function for ClusterProfiler Reactome & KEGG Analysis
process_clusterProfiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment based on the selected category
  if (category == "Reactome") {
    enrichment <- enrichPathway(
      gene = gene_set,
      organism = "human",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  } else if (category == "KEGG") {
    enrichment <- enrichKEGG(
      gene = gene_set,
      organism = "hsa",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  }
  
  # Check if enrichment results exist
  if (is.null(enrichment) || nrow(as.data.frame(enrichment)) == 0) {
    message(paste("No significant enrichment found for", category, "in ClusterProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- as_tibble(as.data.frame(enrichment)) %>%
    mutate(Category = category,
           neglog = -log10(p.adjust)) %>%  # Compute -log10(p.adjust)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(Adjusted p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Function for gProfiler Reactome & KEGG Analysis
process_gprofiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment using gprofiler2
  enrichment <- gost(
    query = gene_set,
    organism = "hsapiens",
    user_threshold = 0.05,
    correction_method = "fdr",
    domain_scope = "custom",
    custom_bg = background,
    sources = category # Either "REAC" or "KEGG"
  )
  
  # Check if enrichment results exist
  if (is.null(enrichment$result) || nrow(enrichment$result) == 0) {
    message(paste("No significant enrichment found for", category, "in gProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- enrichment$result %>%
    as_tibble() %>%
    mutate(Category = category,
           neglog = -log10(p_value)) %>%  # Compute -log10(p-value)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(term_name, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Perform analysis for Reactome and KEGG using ClusterProfiler
cluster_reactome <- process_clusterProfiler(
  gene_set = prob_1_0.5,
  background = background,
  category = "Reactome",
  color = "#2E86C1",
  y_title = "Reactome Pathways"
)

cluster_kegg <- process_clusterProfiler(
  gene_set = prob_1_0.5,
  background = background,
  category = "KEGG",
  color = "#28B463",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for ClusterProfiler
if (!is.null(cluster_reactome) && !is.null(cluster_kegg)) {
  cluster_combined <- cluster_reactome / cluster_kegg
} else if (!is.null(cluster_reactome)) {
  cluster_combined <- cluster_reactome
} else if (!is.null(cluster_kegg)) {
  cluster_combined <- cluster_kegg
} else {
  cluster_combined <- NULL
}

# Perform analysis for Reactome and KEGG using GProfiler
gprofiler_reactome <- process_gprofiler(
  gene_set = prob_1_0.5,
  background = background,
  category = "REAC",  # Corrected category for Reactome in gProfiler
  color = "#D35400",
  y_title = "Reactome Pathways"
)

gprofiler_kegg <- process_gprofiler(
  gene_set = prob_1_0.5,
  background = background,
  category = "KEGG",
  color = "#F39C12",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for GProfiler
if (!is.null(gprofiler_reactome) && !is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_reactome / gprofiler_kegg
} else if (!is.null(gprofiler_reactome)) {
  gprofiler_combined <- gprofiler_reactome
} else if (!is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_kegg
} else {
  gprofiler_combined <- NULL
}

# Display plots (if they are not NULL)
if (!is.null(cluster_combined)) print(cluster_combined)

Past versions of pathway_enrichment4-1.png

Version	Author	Date
df65b82	sayanpaul01	2025-06-04
84d7de5	sayanpaul01	2025-06-03
cf95313	sayanpaul01	2025-03-10
11895c8	sayanpaul01	2025-02-27

if (!is.null(gprofiler_combined)) print(gprofiler_combined)

Past versions of pathway_enrichment4-2.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 DOX-specific 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_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 / plot_MF / plot_CC
combined_plot

Past versions of GO5-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 GO Enrichment g:Profiler

# Load the gprofiler2 package
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)

# Perform GO enrichment analysis with gprofiler2
gost_results <- gost(
  query = prob_2_0.5,           # List of input genes (prob_all_1)
  organism = "hsapiens",   # Human organism
  user_threshold = 0.05,   # Adjusted p-value cutoff
  correction_method = "fdr", # Multiple testing correction
  domain_scope = "custom", # Use custom background
  custom_bg = background,    # Background set of genes
  sources = c("GO:BP", "GO:MF", "GO:CC") # Analyze GO categories
)

# Check if enrichment results exist
if (is.null(gost_results$result) || nrow(gost_results$result) == 0) {
  # If no enriched terms, create a placeholder dataframe
  combined_results <- tibble(
    term_name = "No enriched terms",
    p.adjust = NA,
    source = "N/A",
    Category = "N/A"
  )
} else {
  # Convert results to a data frame
  gost_results_df <- gost_results$result

  # Add a column for adjusted p-value
  gost_results_df <- gost_results_df %>%
    rename(p.adjust = p_value)

  # Separate results for BP, MF, and CC
  BP_results <- gost_results_df %>%
    filter(source == "GO:BP") %>%
    mutate(Category = "Biological Process")

  MF_results <- gost_results_df %>%
    filter(source == "GO:MF") %>%
    mutate(Category = "Molecular Function")

  CC_results <- gost_results_df %>%
    filter(source == "GO:CC") %>%
    mutate(Category = "Cellular Component")

  # Select the top 20 terms by adjusted p-value for each category
  top_BP <- BP_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_MF <- MF_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_CC <- CC_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  # Combine all categories
  combined_results <- bind_rows(top_BP, top_MF, top_CC)
}

# Ensure all columns are atomic types for CSV export
combined_results_clean <- combined_results %>%
  mutate(across(everything(), ~ if (is.list(.)) sapply(., toString) else .))

# Function for plotting top terms
plot_gprofiler_results <- function(data, title, color) {
  ggplot(data, aes(x = -log10(p.adjust), y = reorder(term_name, -log10(p.adjust)))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log10(Adjusted p-value)",
      y = title,
      title = paste("Top 20", title, "GO Terms")
    ) +
    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, size = 0.3)
    )
}

# Check if there are enrichment terms to plot
if (nrow(combined_results) == 1 && combined_results$term_name == "No enriched terms") {
  message("No enriched GO terms found for the input gene set.")
} else {
  # Plot the top 20 terms for each category
  plot_BP <- plot_gprofiler_results(top_BP, "Biological Process", "#2E86C1")
  plot_MF <- plot_gprofiler_results(top_MF, "Molecular Function", "#28B463")
  plot_CC <- plot_gprofiler_results(top_CC, "Cellular Component", "#D35400")

  # Combine the plots using patchwork
  combined_plot <- plot_BP / plot_MF / plot_CC

  # Display the combined plot
  combined_plot
}

Past versions of gprofiler_GO5-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 Pathway Enrichment

# Load required libraries
library(clusterProfiler)
library(org.Hs.eg.db) # Required for enrichPathway
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)
library(ReactomePA)

# Function for ClusterProfiler Reactome & KEGG Analysis
process_clusterProfiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment based on the selected category
  if (category == "Reactome") {
    enrichment <- enrichPathway(
      gene = gene_set,
      organism = "human",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  } else if (category == "KEGG") {
    enrichment <- enrichKEGG(
      gene = gene_set,
      organism = "hsa",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  }
  
  # Check if enrichment results exist
  if (is.null(enrichment) || nrow(as.data.frame(enrichment)) == 0) {
    message(paste("No significant enrichment found for", category, "in ClusterProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- as_tibble(as.data.frame(enrichment)) %>%
    mutate(Category = category,
           neglog = -log10(p.adjust)) %>%  # Compute -log10(p.adjust)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(Adjusted p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Function for gProfiler Reactome & KEGG Analysis
process_gprofiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment using gprofiler2
  enrichment <- gost(
    query = gene_set,
    organism = "hsapiens",
    user_threshold = 0.05,
    correction_method = "fdr",
    domain_scope = "custom",
    custom_bg = background,
    sources = category # Either "REAC" or "KEGG"
  )
  
  # Check if enrichment results exist
  if (is.null(enrichment$result) || nrow(enrichment$result) == 0) {
    message(paste("No significant enrichment found for", category, "in gProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- enrichment$result %>%
    as_tibble() %>%
    mutate(Category = category,
           neglog = -log10(p_value)) %>%  # Compute -log10(p-value)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(term_name, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Perform analysis for Reactome and KEGG using ClusterProfiler
cluster_reactome <- process_clusterProfiler(
  gene_set = prob_2_0.5,
  background = background,
  category = "Reactome",
  color = "#2E86C1",
  y_title = "Reactome Pathways"
)

cluster_kegg <- process_clusterProfiler(
  gene_set = prob_2_0.5,
  background = background,
  category = "KEGG",
  color = "#28B463",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for ClusterProfiler
if (!is.null(cluster_reactome) && !is.null(cluster_kegg)) {
  cluster_combined <- cluster_reactome / cluster_kegg
} else if (!is.null(cluster_reactome)) {
  cluster_combined <- cluster_reactome
} else if (!is.null(cluster_kegg)) {
  cluster_combined <- cluster_kegg
} else {
  cluster_combined <- NULL
}

# Perform analysis for Reactome and KEGG using GProfiler
gprofiler_reactome <- process_gprofiler(
  gene_set = prob_2_0.5,
  background = background,
  category = "REAC",  # Corrected category for Reactome in gProfiler
  color = "#D35400",
  y_title = "Reactome Pathways"
)

gprofiler_kegg <- process_gprofiler(
  gene_set = prob_2_0.5,
  background = background,
  category = "KEGG",
  color = "#F39C12",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for GProfiler
if (!is.null(gprofiler_reactome) && !is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_reactome / gprofiler_kegg
} else if (!is.null(gprofiler_reactome)) {
  gprofiler_combined <- gprofiler_reactome
} else if (!is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_kegg
} else {
  gprofiler_combined <- NULL
}

# Display plots (if they are not NULL)
if (!is.null(cluster_combined)) print(cluster_combined)
if (!is.null(gprofiler_combined)) print(gprofiler_combined)

Past versions of pathway_enrichment5-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 DOX only mid-late 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_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 / plot_MF / plot_CC
combined_plot

Past versions of GO6-1.png

Version	Author	Date
11895c8	sayanpaul01	2025-02-27

📌 GO Enrichment g:Profiler

# Load the gprofiler2 package
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)

# Perform GO enrichment analysis with gprofiler2
gost_results <- gost(
  query = prob_3_0.5,           # List of input genes (prob_all_1)
  organism = "hsapiens",   # Human organism
  user_threshold = 0.05,   # Adjusted p-value cutoff
  correction_method = "fdr", # Multiple testing correction
  domain_scope = "custom", # Use custom background
  custom_bg = background,    # Background set of genes
  sources = c("GO:BP", "GO:MF", "GO:CC") # Analyze GO categories
)

# Check if enrichment results exist
if (is.null(gost_results$result) || nrow(gost_results$result) == 0) {
  # If no enriched terms, create a placeholder dataframe
  combined_results <- tibble(
    term_name = "No enriched terms",
    p.adjust = NA,
    source = "N/A",
    Category = "N/A"
  )
} else {
  # Convert results to a data frame
  gost_results_df <- gost_results$result

  # Add a column for adjusted p-value
  gost_results_df <- gost_results_df %>%
    rename(p.adjust = p_value)

  # Separate results for BP, MF, and CC
  BP_results <- gost_results_df %>%
    filter(source == "GO:BP") %>%
    mutate(Category = "Biological Process")

  MF_results <- gost_results_df %>%
    filter(source == "GO:MF") %>%
    mutate(Category = "Molecular Function")

  CC_results <- gost_results_df %>%
    filter(source == "GO:CC") %>%
    mutate(Category = "Cellular Component")

  # Select the top 20 terms by adjusted p-value for each category
  top_BP <- BP_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_MF <- MF_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_CC <- CC_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  # Combine all categories
  combined_results <- bind_rows(top_BP, top_MF, top_CC)
}

# Ensure all columns are atomic types for CSV export
combined_results_clean <- combined_results %>%
  mutate(across(everything(), ~ if (is.list(.)) sapply(., toString) else .))

# Function for plotting top terms
plot_gprofiler_results <- function(data, title, color) {
  ggplot(data, aes(x = -log10(p.adjust), y = reorder(term_name, -log10(p.adjust)))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log10(Adjusted p-value)",
      y = title,
      title = paste("Top 20", title, "GO Terms")
    ) +
    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, size = 0.3)
    )
}

# Check if there are enrichment terms to plot
if (nrow(combined_results) == 1 && combined_results$term_name == "No enriched terms") {
  message("No enriched GO terms found for the input gene set.")
} else {
  # Plot the top 20 terms for each category
  plot_BP <- plot_gprofiler_results(top_BP, "Biological Process", "#2E86C1")
  plot_MF <- plot_gprofiler_results(top_MF, "Molecular Function", "#28B463")
  plot_CC <- plot_gprofiler_results(top_CC, "Cellular Component", "#D35400")

  # Combine the plots using patchwork
  combined_plot <- plot_BP / plot_MF / plot_CC

  # Display the combined plot
  combined_plot
}

Past versions of gprofiler_GO6-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 Pathway Enrichment

# Load required libraries
library(clusterProfiler)
library(org.Hs.eg.db) # Required for enrichPathway
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)
library(ReactomePA)

# Function for ClusterProfiler Reactome & KEGG Analysis
process_clusterProfiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment based on the selected category
  if (category == "Reactome") {
    enrichment <- enrichPathway(
      gene = gene_set,
      organism = "human",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  } else if (category == "KEGG") {
    enrichment <- enrichKEGG(
      gene = gene_set,
      organism = "hsa",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  }
  
  # Check if enrichment results exist
  if (is.null(enrichment) || nrow(as.data.frame(enrichment)) == 0) {
    message(paste("No significant enrichment found for", category, "in ClusterProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- as_tibble(as.data.frame(enrichment)) %>%
    mutate(Category = category,
           neglog = -log10(p.adjust)) %>%  # Compute -log10(p.adjust)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(Adjusted p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Function for gProfiler Reactome & KEGG Analysis
process_gprofiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment using gprofiler2
  enrichment <- gost(
    query = gene_set,
    organism = "hsapiens",
    user_threshold = 0.05,
    correction_method = "fdr",
    domain_scope = "custom",
    custom_bg = background,
    sources = category # Either "REAC" or "KEGG"
  )
  
  # Check if enrichment results exist
  if (is.null(enrichment$result) || nrow(enrichment$result) == 0) {
    message(paste("No significant enrichment found for", category, "in gProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- enrichment$result %>%
    as_tibble() %>%
    mutate(Category = category,
           neglog = -log10(p_value)) %>%  # Compute -log10(p-value)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(term_name, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Perform analysis for Reactome and KEGG using ClusterProfiler
cluster_reactome <- process_clusterProfiler(
  gene_set = prob_3_0.5,
  background = background,
  category = "Reactome",
  color = "#2E86C1",
  y_title = "Reactome Pathways"
)

cluster_kegg <- process_clusterProfiler(
  gene_set = prob_3_0.5,
  background = background,
  category = "KEGG",
  color = "#28B463",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for ClusterProfiler
if (!is.null(cluster_reactome) && !is.null(cluster_kegg)) {
  cluster_combined <- cluster_reactome / cluster_kegg
} else if (!is.null(cluster_reactome)) {
  cluster_combined <- cluster_reactome
} else if (!is.null(cluster_kegg)) {
  cluster_combined <- cluster_kegg
} else {
  cluster_combined <- NULL
}

# Perform analysis for Reactome and KEGG using GProfiler
gprofiler_reactome <- process_gprofiler(
  gene_set = prob_3_0.5,
  background = background,
  category = "REAC",  # Corrected category for Reactome in gProfiler
  color = "#D35400",
  y_title = "Reactome Pathways"
)

gprofiler_kegg <- process_gprofiler(
  gene_set = prob_3_0.5,
  background = background,
  category = "KEGG",
  color = "#F39C12",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for GProfiler
if (!is.null(gprofiler_reactome) && !is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_reactome / gprofiler_kegg
} else if (!is.null(gprofiler_reactome)) {
  gprofiler_combined <- gprofiler_reactome
} else if (!is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_kegg
} else {
  gprofiler_combined <- NULL
}

# Display plots (if they are not NULL)
if (!is.null(cluster_combined)) print(cluster_combined)
if (!is.null(gprofiler_combined)) print(gprofiler_combined)

📌 CX total + DOX early 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_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 / plot_MF / plot_CC
combined_plot

Past versions of GO7-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 GO Enrichment g:Profiler

# Load the gprofiler2 package
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)

# Perform GO enrichment analysis with gprofiler2
gost_results <- gost(
  query = prob_4_0.5,           # List of input genes (prob_all_1)
  organism = "hsapiens",   # Human organism
  user_threshold = 0.05,   # Adjusted p-value cutoff
  correction_method = "fdr", # Multiple testing correction
  domain_scope = "custom", # Use custom background
  custom_bg = background,    # Background set of genes
  sources = c("GO:BP", "GO:MF", "GO:CC") # Analyze GO categories
)

# Check if enrichment results exist
if (is.null(gost_results$result) || nrow(gost_results$result) == 0) {
  # If no enriched terms, create a placeholder dataframe
  combined_results <- tibble(
    term_name = "No enriched terms",
    p.adjust = NA,
    source = "N/A",
    Category = "N/A"
  )
} else {
  # Convert results to a data frame
  gost_results_df <- gost_results$result

  # Add a column for adjusted p-value
  gost_results_df <- gost_results_df %>%
    rename(p.adjust = p_value)

  # Separate results for BP, MF, and CC
  BP_results <- gost_results_df %>%
    filter(source == "GO:BP") %>%
    mutate(Category = "Biological Process")

  MF_results <- gost_results_df %>%
    filter(source == "GO:MF") %>%
    mutate(Category = "Molecular Function")

  CC_results <- gost_results_df %>%
    filter(source == "GO:CC") %>%
    mutate(Category = "Cellular Component")

  # Select the top 20 terms by adjusted p-value for each category
  top_BP <- BP_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_MF <- MF_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_CC <- CC_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  # Combine all categories
  combined_results <- bind_rows(top_BP, top_MF, top_CC)
}

# Ensure all columns are atomic types for CSV export
combined_results_clean <- combined_results %>%
  mutate(across(everything(), ~ if (is.list(.)) sapply(., toString) else .))

# Function for plotting top terms
plot_gprofiler_results <- function(data, title, color) {
  ggplot(data, aes(x = -log10(p.adjust), y = reorder(term_name, -log10(p.adjust)))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log10(Adjusted p-value)",
      y = title,
      title = paste("Top 20", title, "GO Terms")
    ) +
    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, size = 0.3)
    )
}

# Check if there are enrichment terms to plot
if (nrow(combined_results) == 1 && combined_results$term_name == "No enriched terms") {
  message("No enriched GO terms found for the input gene set.")
} else {
  # Plot the top 20 terms for each category
  plot_BP <- plot_gprofiler_results(top_BP, "Biological Process", "#2E86C1")
  plot_MF <- plot_gprofiler_results(top_MF, "Molecular Function", "#28B463")
  plot_CC <- plot_gprofiler_results(top_CC, "Cellular Component", "#D35400")

  # Combine the plots using patchwork
  combined_plot <- plot_BP / plot_MF / plot_CC

  # Display the combined plot
  combined_plot
}

Past versions of gprofiler_GO7-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 Pathway Enrichment

# Load required libraries
library(clusterProfiler)
library(org.Hs.eg.db) # Required for enrichPathway
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)
library(ReactomePA)

# Function for ClusterProfiler Reactome & KEGG Analysis
process_clusterProfiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment based on the selected category
  if (category == "Reactome") {
    enrichment <- enrichPathway(
      gene = gene_set,
      organism = "human",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  } else if (category == "KEGG") {
    enrichment <- enrichKEGG(
      gene = gene_set,
      organism = "hsa",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  }
  
  # Check if enrichment results exist
  if (is.null(enrichment) || nrow(as.data.frame(enrichment)) == 0) {
    message(paste("No significant enrichment found for", category, "in ClusterProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- as_tibble(as.data.frame(enrichment)) %>%
    mutate(Category = category,
           neglog = -log10(p.adjust)) %>%  # Compute -log10(p.adjust)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(Adjusted p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Function for gProfiler Reactome & KEGG Analysis
process_gprofiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment using gprofiler2
  enrichment <- gost(
    query = gene_set,
    organism = "hsapiens",
    user_threshold = 0.05,
    correction_method = "fdr",
    domain_scope = "custom",
    custom_bg = background,
    sources = category # Either "REAC" or "KEGG"
  )
  
  # Check if enrichment results exist
  if (is.null(enrichment$result) || nrow(enrichment$result) == 0) {
    message(paste("No significant enrichment found for", category, "in gProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- enrichment$result %>%
    as_tibble() %>%
    mutate(Category = category,
           neglog = -log10(p_value)) %>%  # Compute -log10(p-value)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(term_name, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Perform analysis for Reactome and KEGG using ClusterProfiler
cluster_reactome <- process_clusterProfiler(
  gene_set = prob_4_0.5,
  background = background,
  category = "Reactome",
  color = "#2E86C1",
  y_title = "Reactome Pathways"
)

cluster_kegg <- process_clusterProfiler(
  gene_set = prob_4_0.5,
  background = background,
  category = "KEGG",
  color = "#28B463",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for ClusterProfiler
if (!is.null(cluster_reactome) && !is.null(cluster_kegg)) {
  cluster_combined <- cluster_reactome / cluster_kegg
} else if (!is.null(cluster_reactome)) {
  cluster_combined <- cluster_reactome
} else if (!is.null(cluster_kegg)) {
  cluster_combined <- cluster_kegg
} else {
  cluster_combined <- NULL
}

# Perform analysis for Reactome and KEGG using GProfiler
gprofiler_reactome <- process_gprofiler(
  gene_set = prob_4_0.5,
  background = background,
  category = "REAC",  # Corrected category for Reactome in gProfiler
  color = "#D35400",
  y_title = "Reactome Pathways"
)

gprofiler_kegg <- process_gprofiler(
  gene_set = prob_4_0.5,
  background = background,
  category = "KEGG",
  color = "#F39C12",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for GProfiler
if (!is.null(gprofiler_reactome) && !is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_reactome / gprofiler_kegg
} else if (!is.null(gprofiler_reactome)) {
  gprofiler_combined <- gprofiler_reactome
} else if (!is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_kegg
} else {
  gprofiler_combined <- NULL
}

# Display plots (if they are not NULL)
if (!is.null(cluster_combined)) print(cluster_combined)

Past versions of pathway_enrichment7-1.png

Version	Author	Date
df65b82	sayanpaul01	2025-06-04
84d7de5	sayanpaul01	2025-06-03
cf95313	sayanpaul01	2025-03-10
11895c8	sayanpaul01	2025-02-27

if (!is.null(gprofiler_combined)) print(gprofiler_combined)

Past versions of pathway_enrichment7-2.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 DOX early + CX-DOX mid-late 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_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 / plot_MF / plot_CC
combined_plot

Past versions of GO8-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 GO Enrichment g:Profiler

# Load the gprofiler2 package
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)

# Perform GO enrichment analysis with gprofiler2
gost_results <- gost(
  query = prob_5_0.5,           # List of input genes (prob_all_1)
  organism = "hsapiens",   # Human organism
  user_threshold = 0.05,   # Adjusted p-value cutoff
  correction_method = "fdr", # Multiple testing correction
  domain_scope = "custom", # Use custom background
  custom_bg = background,    # Background set of genes
  sources = c("GO:BP", "GO:MF", "GO:CC") # Analyze GO categories
)

# Check if enrichment results exist
if (is.null(gost_results$result) || nrow(gost_results$result) == 0) {
  # If no enriched terms, create a placeholder dataframe
  combined_results <- tibble(
    term_name = "No enriched terms",
    p.adjust = NA,
    source = "N/A",
    Category = "N/A"
  )
} else {
  # Convert results to a data frame
  gost_results_df <- gost_results$result

  # Add a column for adjusted p-value
  gost_results_df <- gost_results_df %>%
    rename(p.adjust = p_value)

  # Separate results for BP, MF, and CC
  BP_results <- gost_results_df %>%
    filter(source == "GO:BP") %>%
    mutate(Category = "Biological Process")

  MF_results <- gost_results_df %>%
    filter(source == "GO:MF") %>%
    mutate(Category = "Molecular Function")

  CC_results <- gost_results_df %>%
    filter(source == "GO:CC") %>%
    mutate(Category = "Cellular Component")

  # Select the top 20 terms by adjusted p-value for each category
  top_BP <- BP_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_MF <- MF_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  top_CC <- CC_results %>%
    arrange(p.adjust) %>%
    slice_head(n = 20)

  # Combine all categories
  combined_results <- bind_rows(top_BP, top_MF, top_CC)
}

# Ensure all columns are atomic types for CSV export
combined_results_clean <- combined_results %>%
  mutate(across(everything(), ~ if (is.list(.)) sapply(., toString) else .))

# Function for plotting top terms
plot_gprofiler_results <- function(data, title, color) {
  ggplot(data, aes(x = -log10(p.adjust), y = reorder(term_name, -log10(p.adjust)))) +
    geom_bar(stat = "identity", fill = color) +
    labs(
      x = "-log10(Adjusted p-value)",
      y = title,
      title = paste("Top 20", title, "GO Terms")
    ) +
    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, size = 0.3)
    )
}

# Check if there are enrichment terms to plot
if (nrow(combined_results) == 1 && combined_results$term_name == "No enriched terms") {
  message("No enriched GO terms found for the input gene set.")
} else {
  # Plot the top 20 terms for each category
  plot_BP <- plot_gprofiler_results(top_BP, "Biological Process", "#2E86C1")
  plot_MF <- plot_gprofiler_results(top_MF, "Molecular Function", "#28B463")
  plot_CC <- plot_gprofiler_results(top_CC, "Cellular Component", "#D35400")

  # Combine the plots using patchwork
  combined_plot <- plot_BP / plot_MF / plot_CC

  # Display the combined plot
  combined_plot
}

Past versions of gprofiler_GO8-1.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 Pathway Enrichment

# Load required libraries
library(clusterProfiler)
library(org.Hs.eg.db) # Required for enrichPathway
library(gprofiler2)
library(ggplot2)
library(dplyr)
library(patchwork)
library(ReactomePA)

# Function for ClusterProfiler Reactome & KEGG Analysis
process_clusterProfiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment based on the selected category
  if (category == "Reactome") {
    enrichment <- enrichPathway(
      gene = gene_set,
      organism = "human",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  } else if (category == "KEGG") {
    enrichment <- enrichKEGG(
      gene = gene_set,
      organism = "hsa",
      pvalueCutoff = 0.05,
      pAdjustMethod = "BH",
      universe = background
    )
  }
  
  # Check if enrichment results exist
  if (is.null(enrichment) || nrow(as.data.frame(enrichment)) == 0) {
    message(paste("No significant enrichment found for", category, "in ClusterProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- as_tibble(as.data.frame(enrichment)) %>%
    mutate(Category = category,
           neglog = -log10(p.adjust)) %>%  # Compute -log10(p.adjust)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(Description, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(Adjusted p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Function for gProfiler Reactome & KEGG Analysis
process_gprofiler <- function(gene_set, background, category, color, y_title) {
  # Perform enrichment using gprofiler2
  enrichment <- gost(
    query = gene_set,
    organism = "hsapiens",
    user_threshold = 0.05,
    correction_method = "fdr",
    domain_scope = "custom",
    custom_bg = background,
    sources = category # Either "REAC" or "KEGG"
  )
  
  # Check if enrichment results exist
  if (is.null(enrichment$result) || nrow(enrichment$result) == 0) {
    message(paste("No significant enrichment found for", category, "in gProfiler"))
    return(NULL)
  }
  
  # Convert results to tibble and process top 20 terms
  enrichment_tibble <- enrichment$result %>%
    as_tibble() %>%
    mutate(Category = category,
           neglog = -log10(p_value)) %>%  # Compute -log10(p-value)
    arrange(desc(neglog)) %>%
    slice_head(n = min(20, nrow(.)))  # Ensure safe slicing
  
  # Generate plot
  plot <- ggplot(enrichment_tibble, aes(x = neglog, y = reorder(term_name, neglog))) +
    geom_bar(stat = "identity", fill = color) +
    labs(x = "-log10(p-value)",
         y = y_title,
         title = paste("Enriched", category, "Pathways")) +
    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")
    )
  
  return(plot)
}

# Perform analysis for Reactome and KEGG using ClusterProfiler
cluster_reactome <- process_clusterProfiler(
  gene_set = prob_5_0.5,
  background = background,
  category = "Reactome",
  color = "#2E86C1",
  y_title = "Reactome Pathways"
)

cluster_kegg <- process_clusterProfiler(
  gene_set = prob_5_0.5,
  background = background,
  category = "KEGG",
  color = "#28B463",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for ClusterProfiler
if (!is.null(cluster_reactome) && !is.null(cluster_kegg)) {
  cluster_combined <- cluster_reactome / cluster_kegg
} else if (!is.null(cluster_reactome)) {
  cluster_combined <- cluster_reactome
} else if (!is.null(cluster_kegg)) {
  cluster_combined <- cluster_kegg
} else {
  cluster_combined <- NULL
}

# Perform analysis for Reactome and KEGG using GProfiler
gprofiler_reactome <- process_gprofiler(
  gene_set = prob_5_0.5,
  background = background,
  category = "REAC",  # Corrected category for Reactome in gProfiler
  color = "#D35400",
  y_title = "Reactome Pathways"
)

gprofiler_kegg <- process_gprofiler(
  gene_set = prob_5_0.5,
  background = background,
  category = "KEGG",
  color = "#F39C12",
  y_title = "KEGG Pathways"
)

# Combine Reactome and KEGG for GProfiler
if (!is.null(gprofiler_reactome) && !is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_reactome / gprofiler_kegg
} else if (!is.null(gprofiler_reactome)) {
  gprofiler_combined <- gprofiler_reactome
} else if (!is.null(gprofiler_kegg)) {
  gprofiler_combined <- gprofiler_kegg
} else {
  gprofiler_combined <- NULL
}

# Display plots (if they are not NULL)
if (!is.null(cluster_combined)) print(cluster_combined)

Past versions of pathway_enrichment8-1.png

Version	Author	Date
df65b82	sayanpaul01	2025-06-04
84d7de5	sayanpaul01	2025-06-03
cf95313	sayanpaul01	2025-03-10
11895c8	sayanpaul01	2025-02-27

if (!is.null(gprofiler_combined)) print(gprofiler_combined)

Past versions of pathway_enrichment8-2.png

Version	Author	Date
84d7de5	sayanpaul01	2025-06-03
11895c8	sayanpaul01	2025-02-27

📌 Top BP (Cluster Profiler)

### 📦 Load Required Libraries
library(tidyverse)
library(ComplexHeatmap)

Warning: package 'ComplexHeatmap' was built under R version 4.3.1

library(circlize)

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

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"
)

### 🔍 Step 1: Extract Top 5 GO Terms Per Group Based on Adjusted P-value
top_go_terms <- map(go_files, function(file) {
  df <- tryCatch(read.csv(file), error = function(e) return(NULL))
  if (!is.null(df) && nrow(df) > 0 && all(c("Description", "p.adjust") %in% colnames(df))) {
    df %>%
      as_tibble() %>%
      filter(p.adjust < 0.05) %>%
      arrange(p.adjust) %>%
      dplyr::select(Description) %>%
      slice_head(n = 5) %>%
      pull(Description) %>%
      unique()
  } else {
    character(0)
  }
}) %>% unlist() %>% unique()

### 🔁 Step 2: Collect All Matrix Values for Top GO Terms
go_matrix_df <- map_dfr(names(go_files), function(group) {
  file <- go_files[[group]]
  df <- tryCatch(read.csv(file), error = function(e) return(tibble()))

  if (nrow(df) == 0 || !all(c("Description", "pvalue", "p.adjust") %in% colnames(df))) {
    tibble(Description = top_go_terms, pvalue = NA, p.adjust = NA, log10p = NA, Group = group)
  } else {
    df_filtered <- df %>%
      as_tibble() %>%
      dplyr::select(Description, pvalue, p.adjust) %>%
      filter(Description %in% top_go_terms)

    tibble(Description = top_go_terms) %>%
      left_join(df_filtered, by = "Description") %>%
      mutate(
        log10p = ifelse(!is.na(pvalue), -log10(pvalue), NA),
        Group = group
      )
  }
})

### 🧱 Step 3: Pivot to Heatmap Matrices
heatmap_data <- go_matrix_df %>%
  dplyr::select(Description, Group, log10p) %>%
  pivot_wider(names_from = Group, values_from = log10p) %>%
  column_to_rownames("Description") %>%
  as.matrix()

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

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

### 🎨 Step 4: Define Heatmap Color Gradient
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: Draw 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,
  cell_fun = function(j, i, x, y, width, height, fill) {
    adj_p <- p_adj_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 Output
draw(ht, heatmap_legend_side = "top")

Past versions of "GO comp-1.png"

Version	Author	Date
df65b82	sayanpaul01	2025-06-04
cf7130e	sayanpaul01	2025-06-03
c5f10c4	sayanpaul01	2025-06-02
c034ee0	sayanpaul01	2025-06-02
cf95313	sayanpaul01	2025-03-10

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

other attached packages:
 [1] circlize_0.4.16                         
 [2] ComplexHeatmap_2.18.0                   
 [3] ReactomePA_1.46.0                       
 [4] gprofiler2_0.2.3                        
 [5] data.table_1.17.0                       
 [6] patchwork_1.3.0                         
 [7] DOSE_3.28.2                             
 [8] TxDb.Hsapiens.UCSC.hg38.knownGene_3.18.0
 [9] RColorBrewer_1.1-3                      
[10] clusterProfiler_4.10.1                  
[11] pheatmap_1.0.12                         
[12] qvalue_2.34.0                           
[13] BiocParallel_1.36.0                     
[14] Homo.sapiens_1.3.1                      
[15] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 
[16] org.Hs.eg.db_3.18.0                     
[17] GO.db_3.18.0                            
[18] OrganismDbi_1.44.0                      
[19] GenomicFeatures_1.54.4                  
[20] GenomicRanges_1.54.1                    
[21] GenomeInfoDb_1.38.8                     
[22] AnnotationDbi_1.64.1                    
[23] IRanges_2.36.0                          
[24] S4Vectors_0.40.2                        
[25] Biobase_2.62.0                          
[26] BiocGenerics_0.48.1                     
[27] edgeR_4.0.16                            
[28] limma_3.58.1                            
[29] cluster_2.1.8.1                         
[30] ggfortify_0.4.17                        
[31] lubridate_1.9.4                         
[32] forcats_1.0.0                           
[33] stringr_1.5.1                           
[34] dplyr_1.1.4                             
[35] purrr_1.0.4                             
[36] readr_2.1.5                             
[37] tidyr_1.3.1                             
[38] tibble_3.2.1                            
[39] ggplot2_3.5.2                           
[40] 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] doParallel_1.0.17           rprojroot_2.0.4            
 [13] lattice_0.22-7              MASS_7.3-60                
 [15] magrittr_2.0.3              plotly_4.10.4              
 [17] sass_0.4.10                 rmarkdown_2.29             
 [19] jquerylib_0.1.4             yaml_2.3.10                
 [21] httpuv_1.6.15               cowplot_1.1.3              
 [23] DBI_1.2.3                   abind_1.4-8                
 [25] zlibbioc_1.48.2             ggraph_2.2.1               
 [27] RCurl_1.98-1.17             yulab.utils_0.2.0          
 [29] tweenr_2.0.3                rappdirs_0.3.3             
 [31] git2r_0.36.2                GenomeInfoDbData_1.2.11    
 [33] enrichplot_1.22.0           ggrepel_0.9.6              
 [35] tidytree_0.4.6              reactome.db_1.86.2         
 [37] codetools_0.2-20            DelayedArray_0.28.0        
 [39] xml2_1.3.8                  ggforce_0.4.2              
 [41] shape_1.4.6.1               tidyselect_1.2.1           
 [43] aplot_0.2.5                 farver_2.1.2               
 [45] viridis_0.6.5               matrixStats_1.5.0          
 [47] BiocFileCache_2.10.2        GenomicAlignments_1.38.2   
 [49] jsonlite_2.0.0              GetoptLong_1.0.5           
 [51] tidygraph_1.3.1             iterators_1.0.14           
 [53] foreach_1.5.2               tools_4.3.0                
 [55] progress_1.2.3              treeio_1.26.0              
 [57] Rcpp_1.0.12                 glue_1.7.0                 
 [59] gridExtra_2.3               SparseArray_1.2.4          
 [61] xfun_0.52                   MatrixGenerics_1.14.0      
 [63] withr_3.0.2                 BiocManager_1.30.25        
 [65] fastmap_1.2.0               digest_0.6.34              
 [67] timechange_0.3.0            R6_2.6.1                   
 [69] gridGraphics_0.5-1          colorspace_2.1-0           
 [71] Cairo_1.6-2                 biomaRt_2.58.2             
 [73] RSQLite_2.3.9               generics_0.1.3             
 [75] rtracklayer_1.62.0          htmlwidgets_1.6.4          
 [77] prettyunits_1.2.0           graphlayouts_1.2.2         
 [79] httr_1.4.7                  S4Arrays_1.2.1             
 [81] scatterpie_0.2.4            graphite_1.48.0            
 [83] whisker_0.4.1               pkgconfig_2.0.3            
 [85] gtable_0.3.6                blob_1.2.4                 
 [87] workflowr_1.7.1             XVector_0.42.0             
 [89] shadowtext_0.1.4            htmltools_0.5.8.1          
 [91] fgsea_1.28.0                RBGL_1.78.0                
 [93] clue_0.3-66                 scales_1.3.0               
 [95] png_0.1-8                   ggfun_0.1.8                
 [97] knitr_1.50                  rstudioapi_0.17.1          
 [99] tzdb_0.5.0                  reshape2_1.4.4             
[101] rjson_0.2.23                nlme_3.1-168               
[103] curl_6.2.2                  GlobalOptions_0.1.2        
[105] cachem_1.1.0                parallel_4.3.0             
[107] HDO.db_0.99.1               restfulr_0.0.15            
[109] pillar_1.10.2               vctrs_0.6.5                
[111] promises_1.3.2              dbplyr_2.5.0               
[113] evaluate_1.0.3              magick_2.8.6               
[115] cli_3.6.1                   locfit_1.5-9.12            
[117] compiler_4.3.0              Rsamtools_2.18.0           
[119] rlang_1.1.3                 crayon_1.5.3               
[121] labeling_0.4.3              plyr_1.8.9                 
[123] fs_1.6.3                    stringi_1.8.3              
[125] viridisLite_0.4.2           munsell_0.5.1              
[127] Biostrings_2.70.3           lazyeval_0.2.2             
[129] GOSemSim_2.28.1             Matrix_1.6-1.1             
[131] hms_1.1.3                   bit64_4.6.0-1              
[133] KEGGREST_1.42.0             statmod_1.5.0              
[135] SummarizedExperiment_1.32.0 igraph_2.1.4               
[137] memoise_2.0.1               bslib_0.9.0                
[139] ggtree_3.10.1               fastmatch_1.1-6            
[141] bit_4.6.0                   gson_0.1.0                 
[143] ape_5.8-1