Differential Expression Analysis of Malignant CD4Tcells vs Control(Normal CD4 Tcells)-GSEA-after_filtering

1. load libraries

2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes

Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("3-MAST_with_SCT_batch_patient_cellline_as_Covariate_with_Filtered_on_meanExpression.csv", header = T)

3. Create the EnhancedVolcano plot

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 = 0.0000905,
                FCcutoff = 1.0)

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

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', 
                              '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 = 0.0000905,
                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)

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

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_log2FC)))

# Create the EnhancedVolcano plot with the filtered data
EnhancedVolcano(
  filtered_genes, 
  lab = ifelse(filtered_genes$p_val_adj <= 0.0000905 & abs(filtered_genes$avg_log2FC) >= 1.0, filtered_genes$gene, NA),
  x = "avg_log2FC", 
  y = "p_val_adj",
  title = "Malignant CD4 T cells(cell lines) vs normal CD4 T cells",
  pCutoff = 0.0000905,
  FCcutoff = 1.0,
  legendPosition = 'right', 
  labCol = 'black',
  labFace = 'bold',
  boxedLabels = FALSE,  # Set to FALSE to remove boxed labels
  pointSize = 3.0,
  labSize = 5.0,
  col = c('grey70', 'black', 'blue', 'red'),  # Customize point colors
  selectLab = filtered_genes$gene[filtered_genes$p_val_adj <= 0.0000905 & abs(filtered_genes$avg_log2FC) >= 1.0]  # Only label significant genes
)

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

EnhancedVolcano(
  filtered_genes, 
  lab = ifelse(filtered_genes$p_val_adj <= 0.0000905 & abs(filtered_genes$avg_log2FC) >= 1.0, filtered_genes$gene, NA),
  x = "avg_log2FC", 
  y = "p_val_adj",
  title = "Malignant CD4 T cells (cell lines) vs Normal CD4 T cells",
  subtitle = "Highlighting differentially expressed genes",
  pCutoff = 0.0000905,
  FCcutoff = 1.0,
  legendPosition = 'right',
  colAlpha = 0.8,  # Slight transparency for non-significant points
  col = c('grey70', 'black', 'blue', 'red'),  # Custom color scheme
  gridlines.major = TRUE,
  gridlines.minor = FALSE,
  selectLab = filtered_genes$gene[filtered_genes$p_val_adj <= 0.0000905 & abs(filtered_genes$avg_log2FC) >= 1.0]
) 

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

4. Enrichment Analysis-1

# Step-by-Step Guide for Gene Set Enrichment Analysis (GSEA) or Over-Representation Analysis (ORA)

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

# Get upregulated genes based on log2FC and p-value thresholds
upregulated_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells[Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC > 2 & Malignant_CD4Tcells_vs_Normal_CD4Tcells$p_val_adj < 0.0000905, ]

# Get downregulated genes based on log2FC and p-value thresholds
downregulated_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells[Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC < -1 & Malignant_CD4Tcells_vs_Normal_CD4Tcells$p_val_adj < 0.0000905, ]

# # Save upregulated genes to CSV
# write.csv(upregulated_genes, file = "Upregulated_Genes.csv", row.names = FALSE)
# 
# # Save downregulated genes to CSV
# write.csv(downregulated_genes, file = "Downregulated_Genes.csv", row.names = FALSE)



# Gene Ontology (GO) Enrichment Analysis
# GO enrichment for upregulated genes
go_up <- enrichGO(gene = upregulated_genes$gene, 
                  OrgDb = org.Hs.eg.db, 
                  keyType = "SYMBOL", 
                  ont = "BP",   # Biological Process (BP), Molecular Function (MF), Cellular Component (CC)
                  pAdjustMethod = "BH", 
                  pvalueCutoff = 0.05)

# GO enrichment for downregulated genes
go_down <- enrichGO(gene = downregulated_genes$gene, 
                    OrgDb = org.Hs.eg.db, 
                    keyType = "SYMBOL", 
                    ont = "BP", 
                    pAdjustMethod = "BH", 
                    pvalueCutoff = 0.05)

# Visualize the top enriched GO terms
dotplot(go_up, showCategory = 10, title = "GO Enrichment for Upregulated Genes")

dotplot(go_down, showCategory = 10, title = "GO Enrichment for Downregulated Genes")

# KEGG Pathway Enrichment
# Convert gene symbols to Entrez IDs for KEGG analysis
upregulated_entrez <- bitr(upregulated_genes$gene, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)$ENTREZID

'select()' returned 1:1 mapping between keys and columns
Warning: 2.84% of input gene IDs are fail to map...

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

'select()' returned 1:1 mapping between keys and columns
Warning: 4.31% of input gene IDs are fail to map...

# KEGG pathway enrichment for upregulated genes
kegg_up <- enrichKEGG(gene = upregulated_entrez, 
                      organism = "hsa", 
                      pvalueCutoff = 0.05)

# KEGG pathway enrichment for downregulated genes
kegg_down <- enrichKEGG(gene = downregulated_entrez, 
                        organism = "hsa", 
                        pvalueCutoff = 0.05)

# Visualize KEGG pathway results
dotplot(kegg_up, showCategory = 10, title = "KEGG Pathway Enrichment for Upregulated Genes")

dotplot(kegg_down, showCategory = 10, title = "KEGG Pathway Enrichment for Downregulated Genes")

# Reactome Pathway Enrichment
# Reactome pathway enrichment for upregulated genes
reactome_up <- enrichPathway(gene = upregulated_entrez, 
                             organism = "human", 
                             pvalueCutoff = 0.05)

# Reactome pathway enrichment for downregulated genes
reactome_down <- enrichPathway(gene = downregulated_entrez, 
                               organism = "human", 
                               pvalueCutoff = 0.05)

# Visualize Reactome pathways
dotplot(reactome_up, showCategory = 10, title = "Reactome Pathway Enrichment for Upregulated Genes")

dotplot(reactome_down, showCategory = 10, title = "Reactome Pathway Enrichment for Downregulated Genes")

# Gene Set Enrichment Analysis (GSEA)
# Create a ranked list of genes (log2FC as ranking metric)
gene_list <- Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC
names(gene_list) <- Malignant_CD4Tcells_vs_Normal_CD4Tcells$gene  # Use the $gene column for gene symbols
gene_list <- sort(gene_list, decreasing = TRUE)

# Convert gene symbols to Entrez IDs for GSEA
gene_df <- bitr(names(gene_list), fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)

'select()' returned 1:1 mapping between keys and columns
Warning: 2.42% of input gene IDs are fail to map...

# Ensure the gene list matches the Entrez IDs
gene_list <- gene_list[names(gene_list) %in% gene_df$SYMBOL]

# Replace gene symbols with Entrez IDs
names(gene_list) <- gene_df$ENTREZID[match(names(gene_list), gene_df$SYMBOL)]

# Run GSEA using KEGG pathways
gsea_kegg <- gseKEGG(geneList = gene_list, 
                     organism = "hsa", 
                     pvalueCutoff = 0.05)

using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).

preparing geneSet collections...
GSEA analysis...
Warning: There are ties in the preranked stats (0.02% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.Warning: There were 3 pathways for which P-values were not calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)Warning: For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.Warning: For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.leading edge analysis...
done...

# Plot the GSEA results
gseaplot(gsea_kegg, geneSetID = 1, title = "Top KEGG Pathway")

# Extract the name of the top KEGG pathway
top_pathway <- gsea_kegg@result[1, "Description"]

# Plot GSEA with the top pathway's name as the title
gseaplot(gsea_kegg, geneSetID = 1, title = top_pathway)

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4.2. Enrichment Analysis-2

# 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

# Get upregulated and downregulated genes based on log2 fold change and adjusted p-value
upregulated_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells[Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC > 2 & Malignant_CD4Tcells_vs_Normal_CD4Tcells$p_val_adj < 0.0000905, ]
downregulated_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells[Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC < -1 & Malignant_CD4Tcells_vs_Normal_CD4Tcells$p_val_adj < 0.0000905, ]

# 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: 211 

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

Number of downregulated genes in Hallmark gene sets: 39 

# 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
    dotplot(hallmark_up, showCategory = 20, title = "Hallmark Pathway Enrichment for Upregulated Genes")
  } 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
    dotplot(hallmark_down, showCategory = 20, title = "Hallmark Pathway Enrichment for Downregulated Genes")
  } 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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4.3. Hallmark-GSEA

# Gene Set Enrichment Analysis (GSEA) for Hallmark Pathways
# Create a ranked list of genes (log2FC as ranking metric)
gene_list <- Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC
names(gene_list) <- Malignant_CD4Tcells_vs_Normal_CD4Tcells$gene
gene_list <- sort(gene_list, decreasing = TRUE)

# Convert gene symbols to Entrez IDs for GSEA
gene_df <- bitr(names(gene_list), 
                fromType = "SYMBOL", 
                toType = "ENTREZID", 
                OrgDb = org.Hs.eg.db)

'select()' returned 1:1 mapping between keys and columns
Warning: 2.42% of input gene IDs are fail to map...

# Filter out genes without Entrez ID mappings
gene_list <- gene_list[names(gene_list) %in% gene_df$SYMBOL]

# Replace gene symbols with Entrez IDs in the gene list
names(gene_list) <- gene_df$ENTREZID[match(names(gene_list), gene_df$SYMBOL)]

# Run GSEA using Hallmark pathways
gsea_hallmark <- GSEA(geneList = gene_list, 
                      TERM2GENE = hallmark_sets[, c("gs_name", "entrez_gene")], 
                      pvalueCutoff = 0.05)

using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).

preparing geneSet collections...
GSEA analysis...
Warning: There are ties in the preranked stats (0.02% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.Warning: For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.Warning: For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.leading edge analysis...
done...

# Check and visualize GSEA results
if (!is.null(gsea_hallmark) && nrow(gsea_hallmark) > 0) {
  # Visualize top GSEA results for Hallmark pathways
  dotplot(gsea_hallmark, showCategory = 20, title = "GSEA for Hallmark Pathways")
  
  # Plot enrichment score for the top pathway
  gseaplot(gsea_hallmark, geneSetID = 1, title = "Top Hallmark Pathway")
  
  # Extract the name of the top Hallmark pathway
  top_hallmark <- gsea_hallmark@result[1, "Description"]
  
  # Plot GSEA with the top pathway's name as the title
  gseaplot(gsea_hallmark, geneSetID = 1, title = top_hallmark)
} else {
  cat("No significant GSEA results for Hallmark pathways.\n")
}

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5. 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_log2FC > 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_log2FC < -0.5, ]

# Create a new column for color based on significance
Malignant_CD4Tcells_vs_Normal_CD4Tcells$color <- ifelse(Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC > 0.5, "Upregulated genes",
                                                   ifelse(Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC < -0.5, "Downregulated genes", "Nonsignificant"))

# Create a volcano plot
ggplot(Malignant_CD4Tcells_vs_Normal_CD4Tcells, aes(x = avg_log2FC, 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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5. ggplot3 for Volcano

# Load necessary libraries
library(ggplot2)
library(ggrepel)

# Identify top and bottom genes
top_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells[Malignant_CD4Tcells_vs_Normal_CD4Tcells$p_val_adj < 0.00001 & Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC > 4, ]
bottom_genes <- Malignant_CD4Tcells_vs_Normal_CD4Tcells[Malignant_CD4Tcells_vs_Normal_CD4Tcells$p_val_adj < 0.00001 & Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC < -4, ]

# Create a new column for color based on significance
Malignant_CD4Tcells_vs_Normal_CD4Tcells$color <- ifelse(Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC > 0.5, 
                                                        "Upregulated genes",
                                                        ifelse(Malignant_CD4Tcells_vs_Normal_CD4Tcells$avg_log2FC < -0.5, 
                                                               "Downregulated genes", 
                                                               "Nonsignificant"))

# Create the volcano plot
ggplot(Malignant_CD4Tcells_vs_Normal_CD4Tcells, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  geom_point(aes(color = color), alpha = 0.7, size = 2) +
  
  # Add labels next to the dots without repel lines
  geom_text(data = top_genes, aes(label = gene), hjust = -0.2, vjust = 0, size = 3, color = "black", fontface = "bold") +
  geom_text(data = bottom_genes, aes(label = gene), hjust = 1.2, vjust = 0, size = 3, color = "black", 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
  theme_minimal()

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