Enrichment Analysis of L6L7 derived from P3_filtred_on_mean

1. load libraries

2. Load the filtered list on mean expression

# Load the DE results from CSV
df <- read.csv("../comparison_L6_vs_L7_with_mean_expression_filtered.csv", stringsAsFactors = FALSE)


DE_results_df <- df

3. Summarize Markers

markers <- DE_results_df

summarize_markers <- function(markers) {
  num_pval0 <- sum(markers$p_val_adj == 0)
  num_pval1 <- sum(markers$p_val_adj == 1)
  num_upregulated <- sum(markers$avg_log2FC > 1.5)
  num_downregulated <- sum(markers$avg_log2FC < -1)
  num_significant <- sum(markers$p_val_adj < 0.05)
  
  
  cat("Number of genes with p_val_adj = 0:", num_pval0, "\n")
  cat("Number of genes with p_val_adj = 1:", num_pval1, "\n")
  cat("Number of upregulated genes (avg_log2FC > 1.5):", num_upregulated, "\n")
  cat("Number of downregulated genes (avg_log2FC < -1):", num_downregulated, "\n")
  cat("Number of significant genes (p_val_adj < 0.05):", num_significant, "\n")
}

cat("Markers Summary at 0.05:\n")

Markers Summary at 0.05:

summarize_markers(markers)

Number of genes with p_val_adj = 0: 165 
Number of genes with p_val_adj = 1: 520 
Number of upregulated genes (avg_log2FC > 1.5): 93 
Number of downregulated genes (avg_log2FC < -1): 136 
Number of significant genes (p_val_adj < 0.05): 3220 

markers2 <- DE_results_df
summarize_markers <- function(markers) {
  num_pval0 <- sum(markers$p_val_adj == 0)
  num_pval1 <- sum(markers$p_val_adj == 1)
  num_upregulated <- sum(markers$avg_log2FC > 1.5)
  num_downregulated <- sum(markers$avg_log2FC < -1)
  num_significant <- sum(markers$p_val_adj < 1e-4)
  
  cat("Number of genes with p_val_adj = 0:", num_pval0, "\n")
  cat("Number of genes with p_val_adj = 1:", num_pval1, "\n")
  cat("Number of upregulated genes (avg_log2FC > 1.5):", num_upregulated, "\n")
  cat("Number of downregulated genes (avg_log2FC < -1):", num_downregulated, "\n")
  cat("Number of significant genes (p_val_adj < 1e-4):", num_significant, "\n")
}

cat("Markers Summary at 1e-4:\n")

Markers Summary at 1e-4:

summarize_markers(markers2)

Number of genes with p_val_adj = 0: 165 
Number of genes with p_val_adj = 1: 520 
Number of upregulated genes (avg_log2FC > 1.5): 93 
Number of downregulated genes (avg_log2FC < -1): 136 
Number of significant genes (p_val_adj < 1e-4): 2899 

markers3 <- DE_results_df
summarize_markers <- function(markers) {
  num_pval0 <- sum(markers$p_val_adj == 0)
  num_pval1 <- sum(markers$p_val_adj == 1)
  num_upregulated <- sum(markers$avg_log2FC > 1.5)
  num_downregulated <- sum(markers$avg_log2FC < -1)
  num_significant <- sum(markers$p_val_adj < 1e-6)
  
  cat("Number of genes with p_val_adj = 0:", num_pval0, "\n")
  cat("Number of genes with p_val_adj = 1:", num_pval1, "\n")
  cat("Number of upregulated genes (avg_log2FC > 1.5):", num_upregulated, "\n")
  cat("Number of downregulated genes (avg_log2FC < -1):", num_downregulated, "\n")
  cat("Number of significant genes (p_val_adj < 1e-6):", num_significant, "\n")
}

cat("Markers Summary at 1e-6:\n")

Markers Summary at 1e-6:

summarize_markers(markers3)

Number of genes with p_val_adj = 0: 165 
Number of genes with p_val_adj = 1: 520 
Number of upregulated genes (avg_log2FC > 1.5): 93 
Number of downregulated genes (avg_log2FC < -1): 136 
Number of significant genes (p_val_adj < 1e-6): 2720 

markers4 <- DE_results_df
summarize_markers <- function(markers) {
  num_pval0 <- sum(markers$p_val_adj == 0)
  num_pval1 <- sum(markers$p_val_adj == 1)
  num_upregulated <- sum(markers$avg_log2FC > 1.5)
  num_downregulated <- sum(markers$avg_log2FC < -1)
  num_significant <- sum(markers$p_val_adj < 1e-10)
  
  cat("Number of genes with p_val_adj = 0:", num_pval0, "\n")
  cat("Number of genes with p_val_adj = 1:", num_pval1, "\n")
  cat("Number of upregulated genes (avg_log2FC > 1.5):", num_upregulated, "\n")
  cat("Number of downregulated genes (avg_log2FC < -1):", num_downregulated, "\n")
  cat("Number of significant genes (p_val_adj < 1e-10):", num_significant, "\n")
  }

cat("Markers Summary at 1e-10:\n")

Markers Summary at 1e-10:

summarize_markers(markers4)

Number of genes with p_val_adj = 0: 165 
Number of genes with p_val_adj = 1: 520 
Number of upregulated genes (avg_log2FC > 1.5): 93 
Number of downregulated genes (avg_log2FC < -1): 136 
Number of significant genes (p_val_adj < 1e-10): 2414 

markers5 <- DE_results_df
summarize_markers <- function(markers) {
  num_pval0 <- sum(markers$p_val_adj == 0)
  num_pval1 <- sum(markers$p_val_adj == 1)
  num_upregulated <- sum(markers$avg_log2FC > 1.5)
  num_downregulated <- sum(markers$avg_log2FC < -1)
  num_significant <- sum(markers$p_val_adj < 1e-15)
  
  cat("Number of genes with p_val_adj = 0:", num_pval0, "\n")
  cat("Number of genes with p_val_adj = 1:", num_pval1, "\n")
  cat("Number of upregulated genes (avg_log2FC > 1.5):", num_upregulated, "\n")
  cat("Number of downregulated genes (avg_log2FC < -1):", num_downregulated, "\n")
  cat("Number of significant genes (p_val_adj < 1e-15):", num_significant, "\n")
}

cat("Markers Summary at 1e-15:\n")

Markers Summary at 1e-15:

summarize_markers(markers5)

Number of genes with p_val_adj = 0: 165 
Number of genes with p_val_adj = 1: 520 
Number of upregulated genes (avg_log2FC > 1.5): 93 
Number of downregulated genes (avg_log2FC < -1): 136 
Number of significant genes (p_val_adj < 1e-15): 2118 

4. Volcano Plots

library(ggplot2)
library(dplyr)

Attachement du package : ‘dplyr’

L'objet suivant est masqué depuis ‘package:Biobase’:

    combine

Les objets suivants sont masqués depuis ‘package:GenomicRanges’:

    intersect, setdiff, union

L'objet suivant est masqué depuis ‘package:GenomeInfoDb’:

    intersect

Les objets suivants sont masqués depuis ‘package:IRanges’:

    collapse, desc, intersect, setdiff, slice, union

Les objets suivants sont masqués depuis ‘package:S4Vectors’:

    first, intersect, rename, setdiff, setequal, union

Les objets suivants sont masqués depuis ‘package:BiocGenerics’:

    combine, intersect, setdiff, union

L'objet suivant est masqué depuis ‘package:matrixStats’:

    count

Les objets suivants sont masqués depuis ‘package:stats’:

    filter, lag

Les objets suivants sont masqués depuis ‘package:base’:

    intersect, setdiff, setequal, union

library(ggrepel)


# Ensure correct column names
colnames(DE_results_df)

[1] "gene"             "p_val"            "avg_log2FC"       "pct.1"            "pct.2"            "p_val_adj"       
[7] "mean_expr_ident1" "mean_expr_ident2"

# Define significance categories
volcano_data <- DE_results_df %>%
  mutate(
    significance = case_when(
      p_val_adj < 1e-20 & avg_log2FC > 2 ~ "Most Upregulated",
      p_val_adj < 1e-20 & avg_log2FC < -2 ~ "Most Downregulated",
      p_val_adj < 0.05 & avg_log2FC > 2 ~ "Upregulated",
      p_val_adj < 0.05 & avg_log2FC < -2 ~ "Downregulated",
      TRUE ~ "Not Significant"
    )
  )

# Select only very significant genes for labeling
top_genes <- volcano_data %>%
  filter(p_val_adj < 0.05 & (avg_log2FC > 2 | avg_log2FC < -2))

ggplot(volcano_data, aes(x = avg_log2FC, y = -log10(p_val_adj), color = significance)) +
  
  # Main points
  geom_point(alpha = 0.7, size = 2.5) +
  
  # Highlight highly significant genes with larger points
  geom_point(data = top_genes, aes(x = avg_log2FC, y = -log10(p_val_adj)), 
             color = "black", size = 3, shape = 21, fill = "black") +

  # Custom color scheme
  scale_color_manual(values = c(
    "Most Upregulated" = "darkred",
    "Most Downregulated" = "darkblue",
    "Upregulated" = "red",
    "Downregulated" = "blue",
    "Not Significant" = "grey"
  )) +

  # Add gene labels (only for highly significant genes)
  geom_text_repel(data = top_genes, aes(label = gene),  
                  size = 4, box.padding = 0.5, max.overlaps = 10, segment.color = NA) +
  
  # Add threshold lines
  geom_vline(xintercept = c(-2, 2), linetype = "dashed", color = "black") +  
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black") +  

  # Improve theme
  theme_minimal(base_size = 14) +
  labs(title = "Volcano Plot: Pseudobulk DESeq2 Analysis",
       x = "Log2 Fold Change",
       y = "-Log10 Adjusted P-Value",
       color = "Significance") +

  ylim(0, 50)  # Avoid extreme scaling issues

NA
NA

EnhancedVolcano plot

library(dplyr)
library(EnhancedVolcano)

# Assuming you have a data frame named Malignant_CD4Tcells_vs_Normal_CD4Tcells
# Filter genes based on lowest p-values but include all genes
filtered_genes <- markers %>%
  arrange(p_val_adj, desc(abs(avg_log2FC)))

# Create the EnhancedVolcano plot with the filtered data
EnhancedVolcano(
  filtered_genes, 
  lab = ifelse(filtered_genes$p_val_adj <= 1e-6 & abs(filtered_genes$avg_log2FC) >= 1.5, filtered_genes$gene, NA),
  x = "avg_log2FC", 
  y = "p_val_adj",
  title = "Malignant CD4 T cells(cell lines) vs normal CD4 T cells",
  pCutoff = 1e-6,
  FCcutoff = 1.0,
  legendPosition = 'right', 
  labCol = 'black',
  labFace = 'bold',
  boxedLabels = FALSE,  # Set to FALSE to remove boxed labels
  pointSize = 3.0,
  labSize = 5.0,
  col = c('grey70', 'black', 'blue', 'red'),  # Customize point colors
  selectLab = filtered_genes$gene[filtered_genes$p_val_adj <= 0.05 & abs(filtered_genes$avg_log2FC) >= 1.0]  # Only label significant genes
)

Avis : One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...

EnhancedVolcano plot

library(ggplot2)
library(EnhancedVolcano)
library(dplyr)

# Define the output directory
output_dir <- "Volcano_Plot_Malignant_vs_Control"
dir.create(output_dir, showWarnings = FALSE)

 Malignant_CD4Tcells_vs_Normal_CD4Tcells <- filtered_genes

# First Volcano Plot
p1 <- EnhancedVolcano(
  Malignant_CD4Tcells_vs_Normal_CD4Tcells,
  lab = Malignant_CD4Tcells_vs_Normal_CD4Tcells$gene,
  x = "avg_log2FC",
  y = "p_val_adj",
  title = "Malignant_CD4Tcells_vs_Normal_CD4Tcells",
  pCutoff = 1e-4,
  FCcutoff = 1.0
)

Avis : One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...

print(p1)  # Display in notebook

ggsave(filename = file.path(output_dir, "VolcanoPlot1.png"), plot = p1, width = 14, height = 10, dpi = 300)

# Second Volcano Plot with selected genes
p2 <- EnhancedVolcano(
  Malignant_CD4Tcells_vs_Normal_CD4Tcells, 
  lab = Malignant_CD4Tcells_vs_Normal_CD4Tcells$gene,
  x = "avg_log2FC", 
  y = "p_val_adj",
  selectLab = c('EPCAM', 'BCAT1', 'KIR3DL2', 'FOXM1', 'TWIST1', 'TNFSF9', 
                              'CD80',  'IL1B', 'RPS4Y1', "TOX", "CD52", "TWIST1", "CCR4", "CCR7","PDCD1",
                              'IL7R', 'TCF7',  'MKI67', 'CD70', "DPP4",
                              'IL2RA','TRBV6-2', 'TRBV10-3', 'TRBV4-2', 'TRBV9', 'TRBV7-9', 
                              'TRAV12-1', 'CD8B', 'FCGR3A', 'GNLY', 'FOXP3', 'SELL', 
                              'GIMAP1', 'RIPOR2', 'LEF1', 'HOXC9', 'SP5',
                              'CCL17', 'ETV4', 'THY1', 'FOXA2', 'ITGAD', 'S100P', 'TBX4', 
                              'ID1', 'XCL1', 'SOX2', 'CD27', 'CD28','PLS3','CD70','RAB25' , 'TRBV27', 'TRBV2'),
  title = "Malignant CD4 T cells(cell lines) vs normal CD4 T cells",
  xlab = bquote(~Log[2]~ 'fold change'),
  pCutoff = 0.05,
  FCcutoff = 1.5, 
  pointSize = 3.0,
  labSize = 5.0,
  boxedLabels = TRUE,
  colAlpha = 0.5,
  legendPosition = 'right',
  legendLabSize = 10,
  legendIconSize = 4.0,
  drawConnectors = TRUE,
  widthConnectors = 0.5,
  colConnectors = 'grey50',
  arrowheads = FALSE,
  max.overlaps = 30
)

Avis : One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...

print(p2)  # Display in notebook

ggsave(filename = file.path(output_dir, "VolcanoPlot2.png"), plot = p2, width = 14, height = 10, dpi = 300)

# Filtering genes
filtered_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells %>%
  arrange(p_val_adj, desc(abs(avg_log2FC)))

# Third Volcano Plot - Filtering by p-value and logFC
p3 <- EnhancedVolcano(
  filtered_genes, 
  lab = ifelse(filtered_genes$p_val_adj <= 1e-4 & abs(filtered_genes$avg_log2FC) >= 1.0, filtered_genes$gene, NA),
  x = "avg_log2FC", 
  y = "p_val_adj",
  title = "Malignant CD4 T cells(cell lines) vs normal CD4 T cells",
  pCutoff = 1e-4,
  FCcutoff = 1.0,
  legendPosition = 'right', 
  labCol = 'black',
  labFace = 'bold',
  boxedLabels = FALSE,  # Remove boxed labels
  pointSize = 3.0,
  labSize = 5.0,
  col = c('grey70', 'black', 'blue', 'red'),  # Customize point colors
  selectLab = filtered_genes$gene[filtered_genes$p_val_adj <= 0.05 & abs(filtered_genes$avg_log2FC) >= 1.0]
)

Avis : One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...

print(p3)  # Display in notebook

ggsave(filename = file.path(output_dir, "VolcanoPlot3.png"), plot = p3, width = 14, height = 10, dpi = 300)

# Fourth Volcano Plot - More refined filtering
p4 <- EnhancedVolcano(
  filtered_genes, 
  lab = ifelse(filtered_genes$p_val_adj <= 1e-4 & abs(filtered_genes$avg_log2FC) >= 1.0, filtered_genes$gene, NA),
  x = "avg_log2FC", 
  y = "p_val_adj",
  title = "Malignant CD4 T cells (cell lines) vs Normal CD4 T cells",
  subtitle = "Highlighting differentially expressed genes",
  pCutoff = 1e-4,
  FCcutoff = 1.0,
  legendPosition = 'right',
  colAlpha = 0.8,  # Slight transparency for non-significant points
  col = c('grey70', 'black', 'blue', 'red'),  # Custom color scheme
  gridlines.major = TRUE,
  gridlines.minor = FALSE,
  selectLab = filtered_genes$gene[filtered_genes$p_val_adj <= 0.05 & abs(filtered_genes$avg_log2FC) >= 1.0]
)

Avis : One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...

print(p4)  # Display in notebook

ggsave(filename = file.path(output_dir, "VolcanoPlot4.png"), plot = p4, width = 14, height = 10, dpi = 300)

message("All volcano plots have been displayed and saved successfully in the 'Malignant_vs_Control' folder.")

All volcano plots have been displayed and saved successfully in the 'Malignant_vs_Control' folder.

5. Enrichment Analysis-All_Pathways

# Load necessary libraries
library(clusterProfiler)

clusterProfiler v4.6.2  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/

If you use clusterProfiler in published research, please cite:
T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141

Attachement du package : ‘clusterProfiler’

L'objet suivant est masqué depuis ‘package:IRanges’:

    slice

L'objet suivant est masqué depuis ‘package:S4Vectors’:

    rename

L'objet suivant est masqué depuis ‘package:stats’:

    filter

library(org.Hs.eg.db)

Le chargement a nécessité le package : AnnotationDbi

Attachement du package : ‘AnnotationDbi’

L'objet suivant est masqué depuis ‘package:clusterProfiler’:

    select

L'objet suivant est masqué depuis ‘package:dplyr’:

    select

library(enrichplot)
library(ReactomePA)

ReactomePA v1.42.0  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/

If you use ReactomePA in published research, please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479

library(DOSE) # For GSEA analysis

DOSE v3.24.2  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/

If you use DOSE in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609

library(ggplot2) # Ensure ggplot2 is available for plotting
library(dplyr)

# Define the output folder where the results will be saved
output_folder <- "L6_vs_L7/"

# Create the output folder if it doesn't exist
if (!dir.exists(output_folder)) {
  dir.create(output_folder)
}

# Define the number of upregulated and downregulated genes to select
UP_genes <- 250
Down_genes <- 250

# Define threshold for differential expression selection (modified thresholds)
logFC_up_threshold <- 1          # Upregulated logFC threshold
logFC_down_threshold <- -1         # Downregulated logFC threshold

# Load your differential expression results (modify based on actual data structure)
# Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("Your_DE_Results_File.csv")

# Filter the genes based on avg_log2FC and arrange by p_val_adj
filtered_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells %>%
  filter(avg_log2FC > logFC_up_threshold | avg_log2FC < logFC_down_threshold) %>%
  arrange(p_val_adj)

# Separate upregulated and downregulated genes
upregulated_genes <- filtered_genes %>%
  filter(avg_log2FC > logFC_up_threshold)

downregulated_genes <- filtered_genes %>%
  filter(avg_log2FC < logFC_down_threshold)

# Check if there are fewer than the specified number of upregulated genes
if (nrow(upregulated_genes) < UP_genes) {
  top_upregulated_genes <- upregulated_genes
  cat("Number of upregulated genes selected:", nrow(top_upregulated_genes), "\n")
  cat("p_val_adj value for the last selected upregulated gene:", tail(top_upregulated_genes$p_val_adj, 1), "\n")
} else {
  # Select the specified number of upregulated genes
  top_upregulated_genes <- upregulated_genes %>%
    head(UP_genes)
  cat("Number of upregulated genes selected:", nrow(top_upregulated_genes), "\n")
  cat("p_val_adj value for the last selected upregulated gene:", tail(top_upregulated_genes$p_val_adj, 1), "\n")
}

Number of upregulated genes selected: 169 
p_val_adj value for the last selected upregulated gene: 2.251439e-35 

# Check if there are fewer than the specified number of downregulated genes
if (nrow(downregulated_genes) < Down_genes) {
  top_downregulated_genes <- downregulated_genes
  cat("Number of downregulated genes selected:", nrow(top_downregulated_genes), "\n")
  cat("p_val_adj value for the last selected downregulated gene:", tail(top_downregulated_genes$p_val_adj, 1), "\n")
} else {
  # Select the specified number of downregulated genes
  top_downregulated_genes <- downregulated_genes %>%
    head(Down_genes)
  cat("Number of downregulated genes selected:", nrow(top_downregulated_genes), "\n")
  cat("p_val_adj value for the last selected downregulated gene:", tail(top_downregulated_genes$p_val_adj, 1), "\n")
}

Number of downregulated genes selected: 136 
p_val_adj value for the last selected downregulated gene: 1.404732e-09 

# Combine the top upregulated and downregulated genes
top_genes <- bind_rows(top_upregulated_genes, top_downregulated_genes)

# Check for missing genes (NAs) in the gene column and remove them
top_genes <- na.omit(top_genes)

# Save upregulated and downregulated gene results to CSV
write.csv(top_upregulated_genes, paste0(output_folder, "upregulated_genes.csv"), row.names = FALSE)
write.csv(top_downregulated_genes, paste0(output_folder, "downregulated_genes.csv"), row.names = FALSE)

# Convert gene symbols to Entrez IDs for enrichment analysis, with checks for missing values
upregulated_entrez <- bitr(top_upregulated_genes$gene, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)

'select()' returned 1:1 mapping between keys and columns
Avis : 6.51% of input gene IDs are fail to map...

downregulated_entrez <- bitr(top_downregulated_genes$gene, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)

'select()' returned 1:1 mapping between keys and columns
Avis : 2.21% of input gene IDs are fail to map...

# Check for missing Entrez IDs and retain gene names
missing_upregulated <- top_upregulated_genes$gene[!top_upregulated_genes$gene %in% upregulated_entrez$SYMBOL]
missing_downregulated <- top_downregulated_genes$gene[!top_downregulated_genes$gene %in% downregulated_entrez$SYMBOL]

# Print out the missing gene symbols for debugging
cat("Missing upregulated genes:\n", missing_upregulated, "\n")

Missing upregulated genes:
 AL590550.1 AARS GARS MARS AP001783.1 AC099552.1 PALM2-AKAP2 AC010967.1 AC090015.1 AC106729.1 AC114977.1 

cat("Missing downregulated genes:\n", missing_downregulated, "\n")

Missing downregulated genes:
 TMEM173 AC108865.1 AC011246.1 

# Merge the Entrez IDs back with the original data frames to retain gene names
top_upregulated_genes <- merge(top_upregulated_genes, upregulated_entrez, by.x = "gene", by.y = "SYMBOL", all.x = TRUE)
top_downregulated_genes <- merge(top_downregulated_genes, downregulated_entrez, by.x = "gene", by.y = "SYMBOL", all.x = TRUE)

# Remove genes that couldn't be mapped to Entrez IDs
top_upregulated_genes <- top_upregulated_genes[!is.na(top_upregulated_genes$ENTREZID), ]
top_downregulated_genes <- top_downregulated_genes[!is.na(top_downregulated_genes$ENTREZID), ]

# Extract Entrez IDs for enrichment analysis
upregulated_entrez <- top_upregulated_genes$ENTREZID
downregulated_entrez <- top_downregulated_genes$ENTREZID

# Define a function to safely run enrichment, plot results, and save them
safe_enrichGO <- function(gene_list, title, filename) {
  if (length(gene_list) > 0) {
    result <- enrichGO(gene = gene_list, OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
                       ont = "BP", pAdjustMethod = "BH", pvalueCutoff = 0.05, readable = TRUE)
    if (!is.null(result) && nrow(as.data.frame(result)) > 0) {
      p <- dotplot(result, showCategory = 10, title = title)
      print(p)  
      ggsave(paste0(output_folder, gsub(".csv", "_dotplot.png", filename)), plot = p, width = 8, height = 6)
      write.csv(as.data.frame(result), file = paste0(output_folder, filename), row.names = FALSE)
    } else {
      message(paste("No significant enrichment found for:", title))
    }
  } else {
    message(paste("No genes found for:", title))
  }
}

safe_enrichKEGG <- function(entrez_list, title, filename) {
  if (length(entrez_list) > 0) {
    result <- enrichKEGG(gene = entrez_list, organism = "hsa", pvalueCutoff = 0.05)
    if (!is.null(result) && nrow(as.data.frame(result)) > 0) {
      result <- setReadable(result, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
      p <- dotplot(result, showCategory = 10, title = title)
      print(p)
      ggsave(paste0(output_folder, gsub(".csv", "_dotplot.png", filename)), plot = p, width = 8, height = 6)
      write.csv(as.data.frame(result), file = paste0(output_folder, filename), row.names = FALSE)
    } else {
      message(paste("No significant KEGG pathways found for:", title))
    }
  } else {
    message(paste("No genes found for:", title))
  }
}

safe_enrichReactome <- function(entrez_list, title, filename) {
  if (length(entrez_list) > 0) {
    result <- enrichPathway(gene = entrez_list, organism = "human", pvalueCutoff = 0.05)
    if (!is.null(result) && nrow(as.data.frame(result)) > 0) {
      result <- setReadable(result, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
      p <- dotplot(result, showCategory = 10, title = title)
      print(p)
      ggsave(paste0(output_folder, gsub(".csv", "_dotplot.png", filename)), plot = p, width = 8, height = 6)
      write.csv(as.data.frame(result), file = paste0(output_folder, filename), row.names = FALSE)
    } else {
      message(paste("No significant Reactome pathways found for:", title))
    }
  } else {
    message(paste("No genes found for:", title))
  }
}

# Perform enrichment analyses, generate plots, and save results
safe_enrichGO(top_upregulated_genes$gene, "GO Enrichment for Upregulated Genes", "upregulated_GO_results.csv")

safe_enrichGO(top_downregulated_genes$gene, "GO Enrichment for Downregulated Genes", "downregulated_GO_results.csv")

safe_enrichKEGG(upregulated_entrez, "KEGG Pathway Enrichment for Upregulated Genes", "upregulated_KEGG_results.csv")

Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...
Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...

safe_enrichKEGG(downregulated_entrez, "KEGG Pathway Enrichment for Downregulated Genes", "downregulated_KEGG_results.csv")

safe_enrichReactome(upregulated_entrez, "Reactome Pathway Enrichment for Upregulated Genes", "upregulated_Reactome_results.csv")

safe_enrichReactome(downregulated_entrez, "Reactome Pathway Enrichment for Downregulated Genes", "downregulated_Reactome_results.csv")

No significant Reactome pathways found for: Reactome Pathway Enrichment for Downregulated Genes

Enrichment Analysis_Hallmark

# Load necessary libraries
library(clusterProfiler)
library(org.Hs.eg.db)
library(msigdbr)
library(enrichplot)
library(ggplot2)
library(dplyr)

# Define the output folder where the results will be saved
output_folder <- "L6_vs_L7/"

# Create the output folder if it doesn't exist
if (!dir.exists(output_folder)) {
  dir.create(output_folder)
}

# Load Hallmark gene sets from msigdbr
hallmark_sets <- msigdbr(species = "Homo sapiens", collection = "H")  # "H" is for Hallmark gene sets

# Convert gene symbols to uppercase for consistency
top_upregulated_genes$gene <- toupper(top_upregulated_genes$gene)
top_downregulated_genes$gene <- toupper(top_downregulated_genes$gene)

# Check for overlap between your upregulated/downregulated genes and Hallmark gene sets
upregulated_in_hallmark <- intersect(top_upregulated_genes$gene, hallmark_sets$gene_symbol)
downregulated_in_hallmark <- intersect(top_downregulated_genes$gene, hallmark_sets$gene_symbol)

# Print the number of overlapping genes for both upregulated and downregulated genes
cat("Number of upregulated genes in Hallmark gene sets:", length(upregulated_in_hallmark), "\n")

Number of upregulated genes in Hallmark gene sets: 75 

cat("Number of downregulated genes in Hallmark gene sets:", length(downregulated_in_hallmark), "\n")

Number of downregulated genes in Hallmark gene sets: 70 

# If there are genes to analyze, proceed with enrichment analysis
if (length(upregulated_in_hallmark) > 0) {
  # Perform enrichment analysis for upregulated genes using Hallmark gene sets
  hallmark_up <- enricher(gene = upregulated_in_hallmark, 
                          TERM2GENE = hallmark_sets[, c("gs_name", "gene_symbol")],  # Ensure TERM2GENE uses correct columns
                          pvalueCutoff = 0.05)
  # Check if results exist
  if (!is.null(hallmark_up) && nrow(hallmark_up) > 0) {
    # Visualize results if available
    up_dotplot <- dotplot(hallmark_up, showCategory = 20, title = "Hallmark Pathway Enrichment for Upregulated Genes")
    
    # Display the plot in the notebook
    print(up_dotplot)
    
    # Save the dotplot to a PNG file
    ggsave(paste0(output_folder, "hallmark_upregulated_dotplot.png"), plot = up_dotplot, width = 10, height = 8)
    
    # Optionally, save the results as CSV
    write.csv(as.data.frame(hallmark_up), file = paste0(output_folder, "hallmark_upregulated_enrichment.csv"), row.names = FALSE)
  } else {
    cat("No significant enrichment found for upregulated genes.\n")
  }
} else {
  cat("No upregulated genes overlap with Hallmark gene sets.\n")
}

if (length(downregulated_in_hallmark) > 0) {
  # Perform enrichment analysis for downregulated genes using Hallmark gene sets
  hallmark_down <- enricher(gene = downregulated_in_hallmark, 
                            TERM2GENE = hallmark_sets[, c("gs_name", "gene_symbol")],  # Ensure TERM2GENE uses correct columns
                            pvalueCutoff = 0.05)
  # Check if results exist
  if (!is.null(hallmark_down) && nrow(hallmark_down) > 0) {
    # Visualize results if available
    down_dotplot <- dotplot(hallmark_down, showCategory = 20, title = "Hallmark Pathway Enrichment for Downregulated Genes")
    
    # Display the plot in the notebook
    print(down_dotplot)
    
    # Save the dotplot to a PNG file
    ggsave(paste0(output_folder, "hallmark_downregulated_dotplot.png"), plot = down_dotplot, width = 10, height = 8)
    
    # Optionally, save the results as CSV
    write.csv(as.data.frame(hallmark_down), file = paste0(output_folder, "hallmark_downregulated_enrichment.csv"), row.names = FALSE)
  } else {
    cat("No significant enrichment found for downregulated genes.\n")
  }
} else {
  cat("No downregulated genes overlap with Hallmark gene sets.\n")
}

NA
NA