PseudoBulk Analysis using Libra RNA assay-Deseq2-LRT

1. load libraries

2. load seurat object

#Load Seurat Object L7
load("/home/nabbasi/isilon/To_Transfer_between_computers/23-Harmony_Integration/0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_Selected_0.8_ADT_Normalized_cleaned_mt.robj")

pseudobulk_seurat <- All_samples_Merged

pseudobulk_seurat$label <- factor(
  ifelse(pseudobulk_seurat$orig.ident %in% c("L1", "L2", "L3", "L4", "L5", "L6", "L7"), "Malignant", "Control"),
  levels = c("Malignant", "Control")  # Ensuring "Malignant" is the reference
)

# Ensure 'condition' is a factor with the correct levels
pseudobulk_seurat$label <- factor(pseudobulk_seurat$label, levels = c("Control", "Malignant"))

# Ensure 'replicate' is a factor
pseudobulk_seurat$replicate <- as.factor(pseudobulk_seurat$cell_line)

# Rename the cell type column
pseudobulk_seurat$cell_type <- "CD4T"

3. DE using LIBRA

libra_test <-pseudobulk_seurat



DE_results <- run_de(
  libra_test,
  cell_type_col = "cell_type", 
  replicate_col = "replicate",
  label_col = "label",
  de_family = "pseudobulk",   # or "singlecell"
  de_method = "DESeq2",        # or "wilcox"
)

[1] "CD4T"

converting counts to integer mode
  it appears that the last variable in the design formula, 'group',
  has a factor level, 'Control', which is not the reference level. we recommend
  to use factor(...,levels=...) or relevel() to set this as the reference level
  before proceeding. for more information, please see the 'Note on factor levels'
  in vignette('DESeq2').
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 1197 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing

4. Volcano Plot

library(ggplot2)
library(dplyr)

# Extract results from Libra
DE_results_df <- as.data.frame(DE_results)

# Save results to CSV
write.csv(DE_results_df, file = "1-Pseudobulk_DEseq2_LRT_DE_results_with_RNA_batch_with_meanExpression.csv", row.names = FALSE)

# Ensure logFC and p-value columns are named correctly
colnames(DE_results_df)

 [1] "cell_type"     "gene"          "avg_logFC"     "Malignant.pct" "Control.pct"   "Malignant.exp" "Control.exp"  
 [8] "p_val"         "p_val_adj"     "de_family"     "de_method"     "de_type"      

# Filtering for significant genes:
volcano_data <- DE_results_df %>%
  mutate(
    significance = case_when(
      p_val_adj < 0.05 & avg_logFC > 2 ~ "Upregulated",
      p_val_adj < 0.05 & avg_logFC < -2 ~ "Downregulated",
      TRUE ~ "Not Significant"
    )
  )
ggplot(volcano_data, aes(x = avg_logFC, y = -log10(p_val_adj), color = significance)) +
  geom_point(alpha = 0.6, size = 2) +
  scale_color_manual(values = c("Upregulated" = "red", "Downregulated" = "blue", "Not Significant" = "grey")) +
  theme_minimal() +
  labs(title = "Volcano Plot: Pseudobulk DESeq2 Analysis",
       x = "Log2 Fold Change",
       y = "-Log10 Adjusted P-Value",
       color = "Significance") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black") +
  geom_vline(xintercept = c(-2, 2), linetype = "dashed", color = "black")

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Volcano Plot

library(EnhancedVolcano)

Le chargement a nécessité le package : ggrepel

EnhancedVolcano(
  DE_results, 
  lab = DE_results$gene,  # Labels for genes
  x = 'avg_logFC',             # Log2 Fold Change (corrected name)
  y = 'p_val_adj',             # Adjusted P-value
  title = 'Volcano Plot: Pseudobulk DESeq2 Analysis',
  subtitle = 'Malignant vs Normal CD4T Cells',
  pCutoff = 0.05,              # Adjusted p-value threshold
  FCcutoff = 2,                # Fold Change threshold
  pointSize = 2.0,             # Adjust point size
  labSize = 4.0,               # Adjust label size
  col = c("black", "grey", "blue", "red"), # Color coding
  colAlpha = 0.6,              # Transparency for points
  legendLabels = c("NS", "Log2FC", "P-value", "Both"),
  legendPosition = "right",
  legendLabSize = 10,
  legendIconSize = 4.0,
  drawConnectors = TRUE,       # Connect gene labels
  widthConnectors = 0.5,
  vline = c(-2, 2),            # Vertical lines at logFC ±2
  hline = -log10(0.05),        # Horizontal line at adjusted p-value 0.05
  border = "full"
)

Volcano Plot

library(ggplot2)
library(dplyr)
library(ggrepel)

# Extract results from Libra
DE_results_df <- as.data.frame(DE_results)

# Ensure correct column names
colnames(DE_results_df)

 [1] "cell_type"     "gene"          "avg_logFC"     "Malignant.pct" "Control.pct"   "Malignant.exp" "Control.exp"  
 [8] "p_val"         "p_val_adj"     "de_family"     "de_method"     "de_type"      

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

# Select genes to label: p_val_adj < 1e-50 OR logFC > 2 OR logFC < -2
top_genes <- volcano_data %>%
  filter(p_val_adj < 0.05 | avg_logFC > 2 | avg_logFC < -2)

ggplot(volcano_data, aes(x = avg_logFC, y = -log10(p_val_adj), color = significance)) +
  geom_point(alpha = 0.6, size = 2) +  # Main points
  scale_color_manual(values = c("Upregulated" = "red", "Downregulated" = "blue", "Not Significant" = "grey")) +
  theme_minimal() +
  labs(title = "Volcano Plot: Pseudobulk DESeq2 Analysis",
       x = "Log2 Fold Change",
       y = "-Log10 Adjusted P-Value",
       color = "Significance") +

  # Add gene labels WITHOUT any lines connecting them
  geom_text_repel(data = top_genes, 
                  aes(label = gene),  
                  size = 3, box.padding = 0.3, max.overlaps = 15, segment.color = NA) +  

  # Add threshold lines
  geom_vline(xintercept = c(-2, 2), linetype = "dashed", color = "black") +  # logFC thresholds
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black") +  # p-value threshold

  ylim(0, 70)  # Set max y-axis limit to avoid extreme values

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Volcano Plot

library(ggplot2)
library(dplyr)
library(ggrepel)

# Extract results from Libra
DE_results_df <- as.data.frame(DE_results)

# Ensure correct column names
colnames(DE_results_df)

 [1] "cell_type"     "gene"          "avg_logFC"     "Malignant.pct" "Control.pct"   "Malignant.exp" "Control.exp"  
 [8] "p_val"         "p_val_adj"     "de_family"     "de_method"     "de_type"      

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

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

ggplot(volcano_data, aes(x = avg_logFC, 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_logFC, y = -log10(p_val_adj)), 
             color = "black", size = 3, shape = 21, fill = "black") +

  # Custom color scheme
  scale_color_manual(values = c(
    "Highly Upregulated" = "darkred",
    "Highly 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

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5. 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_significant <- sum(markers$p_val_adj < 0.05)
  num_upregulated <- sum(markers$avg_logFC > 1)
  num_downregulated <- sum(markers$avg_logFC < -1)
  
  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 significant genes (p_val_adj < 0.05):", num_significant, "\n")
  cat("Number of upregulated genes (avg_logFC > 1):", num_upregulated, "\n")
  cat("Number of downregulated genes (avg_logFC < 1):", num_downregulated, "\n")
}

cat("Markers1 Summary (MAST with Batch Correction):\n")

Markers1 Summary (MAST with Batch Correction):

summarize_markers(markers)

Number of genes with p_val_adj = 0: 0 
Number of genes with p_val_adj = 1: 164 
Number of significant genes (p_val_adj < 0.05): 5582 
Number of upregulated genes (avg_logFC > 1): 5591 
Number of downregulated genes (avg_logFC < 1): 4822 

9. 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_logFC)))

# Create the EnhancedVolcano plot with the filtered data
EnhancedVolcano(
  filtered_genes, 
  lab = ifelse(filtered_genes$p_val_adj <= 0.0000905 & abs(filtered_genes$avg_logFC) >= 1.0, filtered_genes$gene, NA),
  x = "avg_logFC", 
  y = "p_val_adj",
  title = "Malignant CD4 T cells(cell lines) vs normal CD4 T cells",
  pCutoff = 0.0000905,
  FCcutoff = 1.0,
  legendPosition = 'right', 
  labCol = 'black',
  labFace = 'bold',
  boxedLabels = FALSE,  # Set to FALSE to remove boxed labels
  pointSize = 3.0,
  labSize = 5.0,
  col = c('grey70', 'lightblue', 'blue', 'red'),  # Customize point colors
  selectLab = filtered_genes$gene[filtered_genes$p_val_adj <= 0.05 & abs(filtered_genes$avg_logFC) >= 1.0]  # Only label significant genes
)

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_logFC)))

# Create the EnhancedVolcano plot with the filtered data
EnhancedVolcano(
  filtered_genes, 
  lab = ifelse(filtered_genes$p_val_adj <= 0.0000905 & abs(filtered_genes$avg_logFC) >= 1.0, filtered_genes$gene, NA),
  x = "avg_logFC", 
  y = "p_val_adj",
  title = "Malignant CD4 T cells(cell lines) vs normal CD4 T cells",
  pCutoff = 1e-4,
  FCcutoff = 1.0,
  legendPosition = 'right', 
  labCol = 'black',
  labFace = 'bold',
  boxedLabels = FALSE,  # Set to FALSE to remove boxed labels
  pointSize = 3.0,
  labSize = 5.0,
  col = c('grey70', 'black', 'blue', 'red'),  # Customize point colors
  selectLab = filtered_genes$gene[filtered_genes$p_val_adj <= 0.05 & abs(filtered_genes$avg_logFC) >= 1.0]  # Only label significant genes
)

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Create the EnhancedVolcano plot

 Malignant_CD4Tcells_vs_Normal_CD4Tcells <- markers

EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells,
                lab = Malignant_CD4Tcells_vs_Normal_CD4Tcells$gene,
                x = "avg_logFC",
                y = "p_val_adj",
                title = "MAST with Batch Correction (All Genes)",
                pCutoff = 0.05,
                FCcutoff = 1.0)

EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, 
                lab = Malignant_CD4Tcells_vs_Normal_CD4Tcells$gene,
                x = "avg_logFC", 
                y = "p_val_adj",
                selectLab = c('EPCAM', 'BCAT1', 'KIR3DL2', 'FOXM1', 'TWIST1', 'TNFSF9', 
                              'CD80',  'IL1B', 'RPS4Y1', 
                              'IL7R', 'TCF7',  'MKI67', 'CD70', 
                              '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)

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 <- Malignant_CD4Tcells_vs_Normal_CD4Tcells %>%
  arrange(p_val_adj, desc(abs(avg_logFC)))

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

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

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ggplot2 for Volcano

library(ggplot2)
library(ggrepel)

# Identify top and bottom genes
top_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells[Malignant_CD4Tcells_vs_Normal_CD4Tcells$p_val_adj < 0.05 & Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_logFC > 0.5, ]
bottom_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells[Malignant_CD4Tcells_vs_Normal_CD4Tcells$p_val_adj < 0.05 & Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_logFC < -0.5, ]

# Create a new column for color based on significance
Malignant_CD4Tcells_vs_Normal_CD4Tcells$color <- ifelse(Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_logFC > 0.5, "Upregulated genes",
                                                   ifelse(Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_logFC < -0.5, "Downregulated genes", "Nonsignificant"))

# Create a volcano plot
ggplot(Malignant_CD4Tcells_vs_Normal_CD4Tcells, aes(x = avg_logFC, y = -log10(p_val_adj))) +
  geom_point(aes(color = color), alpha = 0.7, size = 2) +
  
  # Add labels for top and bottom genes
  geom_text_repel(data = top_genes, aes(label = gene), color = "black", vjust = 1, fontface = "bold") +
  geom_text_repel(data = bottom_genes, aes(label = gene), color = "black", vjust = -1, fontface = "bold") +
  
  # Customize labels and title
  labs(title = "Volcano Plot",
       x = "log2 Fold Change",
       y = "-log10(p-value)") +
  
  # # Add significance threshold lines
   geom_hline(yintercept = -log10(0.00001), linetype = "dashed", color = "black") +
   geom_vline(xintercept = c(-0.5, 0.5), linetype = "dashed", color = "black") +
  
  # Set colors for top and bottom genes
  scale_color_manual(values = c("Upregulated genes" = "red", "Downregulated genes" = "blue", "Nonsignificant" = "darkgrey")) +
  
  # Customize theme if needed
  theme_minimal()

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Create the EnhancedVolcano plot

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

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

# First Volcano Plot
p1 <- EnhancedVolcano(
  Malignant_CD4Tcells_vs_Normal_CD4Tcells,
  lab = Malignant_CD4Tcells_vs_Normal_CD4Tcells$gene,
  x = "avg_logFC",
  y = "p_val_adj",
  title = "Malignant_CD4Tcells_vs_Normal_CD4Tcells",
  pCutoff = 1e-4,
  FCcutoff = 1.0
)
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_logFC", 
  y = "p_val_adj",
  selectLab = c('EPCAM', 'BCAT1', 'KIR3DL2', 'FOXM1', 'TWIST1', 'TNFSF9', 
                'CD80',  'IL1B', 'RPS4Y1', 
                'IL7R', 'TCF7',  'MKI67', 'CD70', 
                '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 = 1e-4,
  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
)
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_logFC)))

# 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_logFC) >= 1.0, filtered_genes$gene, NA),
  x = "avg_logFC", 
  y = "p_val_adj",
  title = "Malignant CD4 T cells(cell lines) vs normal CD4 T cells",
  pCutoff = 1e-4,
  FCcutoff = 1.0,
  legendPosition = 'right', 
  labCol = 'black',
  labFace = 'bold',
  boxedLabels = FALSE,  # Remove boxed labels
  pointSize = 3.0,
  labSize = 5.0,
  col = c('grey70', 'black', 'blue', 'red'),  # Customize point colors
  selectLab = filtered_genes$gene[filtered_genes$p_val_adj <= 0.05 & abs(filtered_genes$avg_logFC) >= 1.0]
)
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_logFC) >= 1.0, filtered_genes$gene, NA),
  x = "avg_logFC", 
  y = "p_val_adj",
  title = "Malignant CD4 T cells (cell lines) vs Normal CD4 T cells",
  subtitle = "Highlighting differentially expressed genes",
  pCutoff = 1e-4,
  FCcutoff = 1.0,
  legendPosition = 'right',
  colAlpha = 0.8,  # Slight transparency for non-significant points
  col = c('grey70', 'black', 'blue', 'red'),  # Custom color scheme
  gridlines.major = TRUE,
  gridlines.minor = FALSE,
  selectLab = filtered_genes$gene[filtered_genes$p_val_adj <= 0.05 & abs(filtered_genes$avg_logFC) >= 1.0]
)
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 'L1_vs_Control' folder.")

All volcano plots have been displayed and saved successfully in the 'L1_vs_Control' folder.

10. Enrichment Analysis-1

# Load necessary libraries
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
library(ReactomePA)
library(DOSE) # For GSEA analysis
library(ggplot2) # Ensure ggplot2 is available for plotting

# Define threshold for differential expression selection (modified thresholds)
logFC_up_threshold <- 1          # Upregulated logFC threshold
logFC_down_threshold <- -1       # Downregulated logFC threshold
pval_threshold <- 0.05  # p-value threshold as specified

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

# Select upregulated and downregulated genes
upregulated_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells[
  Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_logFC > logFC_up_threshold & 
  Malignant_CD4Tcells_vs_Normal_CD4Tcells$p_val_adj < pval_threshold, ]

downregulated_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells[
  Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_logFC < logFC_down_threshold & 
  Malignant_CD4Tcells_vs_Normal_CD4Tcells$p_val_adj < pval_threshold, ]

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

# Save upregulated and downregulated gene results to CSV
write.csv(upregulated_genes, "Malignant_vs_Control/upregulated_genes.csv", row.names = FALSE)
write.csv(downregulated_genes, "Malignant_vs_Control/downregulated_genes.csv", row.names = FALSE)

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

'select()' returned 1:1 mapping between keys and columns
Avis : 14.39% of input gene IDs are fail to map...

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

'select()' returned 1:1 mapping between keys and columns
Avis : 26.94% of input gene IDs are fail to map...

# Check for missing Entrez IDs
missing_upregulated <- upregulated_genes$gene[is.na(upregulated_entrez$ENTREZID)]
missing_downregulated <- downregulated_genes$gene[is.na(downregulated_entrez$ENTREZID)]

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

Missing upregulated genes:
  

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

Missing downregulated genes:
  

# Remove genes that couldn't be mapped to Entrez IDs
upregulated_entrez <- upregulated_entrez$ENTREZID[!is.na(upregulated_entrez$ENTREZID)]
downregulated_entrez <- downregulated_entrez$ENTREZID[!is.na(downregulated_entrez$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)
    if (!is.null(result) && nrow(as.data.frame(result)) > 0) {
      p <- dotplot(result, showCategory = 10, title = title)
      print(p)  
      ggsave(paste0("Malignant_vs_Control/", gsub(".csv", "_dotplot.png", filename)), plot = p, width = 8, height = 6)
      write.csv(as.data.frame(result), file = paste0("Malignant_vs_Control/", 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) {
      p <- dotplot(result, showCategory = 10, title = title)
      print(p)
      ggsave(paste0("Malignant_vs_Control/", gsub(".csv", "_dotplot.png", filename)), plot = p, width = 8, height = 6)
      write.csv(as.data.frame(result), file = paste0("Malignant_vs_Control/", 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) {
      p <- dotplot(result, showCategory = 10, title = title)
      print(p)
      ggsave(paste0("Malignant_vs_Control/", gsub(".csv", "_dotplot.png", filename)), plot = p, width = 8, height = 6)
      write.csv(as.data.frame(result), file = paste0("Malignant_vs_Control/", 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(upregulated_genes$gene, "GO Enrichment for Upregulated Genes", "upregulated_GO_results.csv")

safe_enrichGO(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")
View(downregulated_genes)

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-2-Hallmark

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

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

# Convert gene symbols to uppercase for consistency
upregulated_genes$gene <- toupper(upregulated_genes$gene)
downregulated_genes$gene <- toupper(downregulated_genes$gene)

# Check for overlap between your upregulated/downregulated genes and Hallmark gene sets
upregulated_in_hallmark <- intersect(upregulated_genes$gene, hallmark_sets$gene_symbol)
downregulated_in_hallmark <- intersect(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: 588 

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

Number of downregulated genes in Hallmark gene sets: 264 

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

# 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")
}

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