Sézary Syndrome top10 Clusters

1. load libraries

#Differential Expression Analysis

2. load seurat object

All_samples_Merged

An object of class Seurat 
64169 features across 59355 samples within 6 assays 
Active assay: SCT (27417 features, 3000 variable features)
 3 layers present: counts, data, scale.data
 5 other assays present: RNA, ADT, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3
 6 dimensional reductions calculated: integrated_dr, ref.umap, pca, umap, harmony, umap.harmony

DimPlot(All_samples_Merged, reduction = "umap.harmony", group.by = "cell_line",label = T, label.box = T)

DimPlot(All_samples_Merged, reduction = "umap.harmony", group.by = "Harmony_snn_res.0.9",label = T, label.box = T)

# Prepare the object for DE analysis
All_samples_Merged <- PrepSCTFindMarkers(All_samples_Merged)

Only one SCT model is stored - skipping recalculating corrected counts

#Differential Expression Analysis

3. Find Markers for Each Cell Line

DefaultAssay(All_samples_Merged) <- "SCT"
Idents(All_samples_Merged) <- "Harmony_snn_res.0.9"
all_markers <- FindAllMarkers(All_samples_Merged, only.pos = FALSE, logfc.threshold = 0.25)

Calculating cluster 0
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Calculating cluster 4
Calculating cluster 5
Calculating cluster 6
Calculating cluster 7
Calculating cluster 8
Calculating cluster 9
Calculating cluster 10
Calculating cluster 11
Calculating cluster 12
Calculating cluster 13
Calculating cluster 14
Calculating cluster 15
Calculating cluster 16
Calculating cluster 17
Calculating cluster 18
Calculating cluster 19
Calculating cluster 20
Calculating cluster 21
Calculating cluster 22
Calculating cluster 23
Calculating cluster 24

write.csv(all_markers, "all_markers.csv")
head(all_markers)

NA
NA

4. generates an expression heatmap for given cells and features

DefaultAssay(All_samples_Merged) <- "SCT"  # or the assay where the genes are present

all_markers %>%
    group_by(cluster) %>%
    dplyr::filter(avg_log2FC > 1) %>%
    slice_head(n = 5) %>%
    ungroup() -> top5
heatmap_plot5 <-DoHeatmap(All_samples_Merged, features = top5$gene) + NoLegend()

Warning: The following features were omitted as they were not found in the scale.data slot for the SCT assay: MMP2, AC111000.4, PROM1, AC092979.1, CPA3, ITGA2B, AL137230.1, EFNA1

# Save the heatmap as a PNG file
ggsave(filename = "heatmap_result5.png", plot = heatmap_plot5, width = 12, height = 8, dpi = 300)

all_markers %>%
    group_by(cluster) %>%
    dplyr::filter(avg_log2FC > 1) %>%
    slice_head(n = 10) %>%
    ungroup() -> top10
heatmap_plot10 <-DoHeatmap(All_samples_Merged, features = top10$gene) + NoLegend()

# Save the heatmap as a PNG file
ggsave(filename = "heatmap_result10.png", plot = heatmap_plot10, width = 12, height = 8, dpi = 300)

4. Heatmap of Top 10 Upregulated Genes (Sorted by avg_log2FC)

```r

top10_markers <- all_markers %>%
  group_by(cluster) %>%
  top_n(n = 10, wt = avg_log2FC) %>%
  arrange(cluster, desc(avg_log2FC))

top10_genes <- unique(top10_markers$gene)

heatmap_data <- GetAssayData(All_samples_Merged, assay = \SCT\, slot = \data\)[top10_genes, ]
heatmap_data <- heatmap_data - rowMeans(heatmap_data)

cell_line_colors <- rainbow(length(unique(All_samples_Merged$cell_line)))
names(cell_line_colors) <- unique(All_samples_Merged$cell_line)
annotation_colors <- list(cell_line = cell_line_colors)

p <- pheatmap(heatmap_data,
         show_rownames = TRUE,
         show_colnames = FALSE,
         annotation_col = All_samples_Merged@meta.data[, \cell_line\, drop = FALSE],
         annotation_colors = annotation_colors,
         main = \Top 10 Upregulated Genes per Cell Line (Sorted by avg_log2FC)\,
         fontsize_row = 6,
         treeheight_col = 0,
         cluster_rows = FALSE,
         cluster_cols = FALSE)

print(p)
png(\heatmap_top10_log2fc_sorted.png\, width = 12, height = 8, units = \in\, res = 300)
print(p)
dev.off()

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# 5. Heatmap of Top 10 Markers (Seurat Default)

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```r
```r

top10_markers_seurat <- all_markers %>%
  group_by(cluster) %>%
  top_n(n = 10, wt = avg_log2FC)

top10_genes_seurat <- unique(top10_markers_seurat$gene)

heatmap_data_seurat <- GetAssayData(All_samples_Merged, assay = \SCT\, slot = \data\)[top10_genes_seurat, ]
heatmap_data_seurat <- heatmap_data_seurat - rowMeans(heatmap_data_seurat)

p2 <- pheatmap(heatmap_data_seurat,
         show_rownames = TRUE,
         show_colnames = FALSE,
         annotation_col = All_samples_Merged@meta.data[, \cell_line\, drop = FALSE],
         annotation_colors = annotation_colors,
         main = \Top 10 Markers per Cell Line (Seurat Default)\,
         fontsize_row = 6,
         treeheight_col = 0)

print(p2)
png(\heatmap_top10_seurat.png\, width = 12, height = 8, units = \in\, res = 300)
print(p2)
dev.off()

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# 6. Pairwise Comparisons

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```r
```r
library(EnhancedVolcano)

perform_comparison_and_volcano <- function(All_samples_Merged, ident1, ident2) {
  Idents(All_samples_Merged) <- \cell_line\
  markers <- FindMarkers(All_samples_Merged, ident.1 = ident1, ident.2 = ident2, assay = \SCT\)
  write.csv(markers, paste0(\comparison_\, ident1, \_vs_\, ident2, \.csv\))
  
  # Create volcano plot
  volcano_plot <- EnhancedVolcano(markers,
                                  lab = rownames(markers),
                                  x = 'avg_log2FC',
                                  y = 'p_val_adj',
                                  title = paste(ident1, 'vs', ident2),
                                  pCutoff = 0.05,
                                  FCcutoff = 1,
                                  pointSize = 1.5,
                                  labSize = 4.0,
                                  col = c('grey', 'darkgreen', 'blue', 'red'),
                                  colAlpha = 0.5,
                                  legendPosition = 'right',
                                  legendLabSize = 10,
                                  legendIconSize = 4.0,
                                  drawConnectors = TRUE,
                                  widthConnectors = 0.5)
  
  print(volcano_plot)
  png(paste0(\volcano_\, ident1, \_vs_\, ident2, \.png\), width = 12, height = 10, units = \in\, res = 300)
  print(volcano_plot)
  dev.off()
  
  return(markers)
}

# Patient 1
p1_comparison <- perform_comparison_and_volcano(All_samples_Merged, \L1\, \L2\)
head(p1_comparison)

# Patient 2
p2_comparison <- perform_comparison_and_volcano(All_samples_Merged, \L3\, \L4\)
head(p2_comparison)

# Patient 3
p3_comparison_L5L6 <- perform_comparison_and_volcano(All_samples_Merged, \L5\, \L6\)
p3_comparison_L5L7 <- perform_comparison_and_volcano(All_samples_Merged, \L5\, \L7\)
p3_comparison_L6L7 <- perform_comparison_and_volcano(All_samples_Merged, \L6\, \L7\)
head(p3_comparison_L5L6)
head(p3_comparison_L5L7)
head(p3_comparison_L6L7)

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# 7. Enrichment Analysis

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-->\n\n```r\n```r\n\nperform_go_enrichment <- function(gene_list, gene_universe, title) {\n  ego <- enrichGO(gene = gene_list,\n                  universe = gene_universe,\n                  OrgDb = org.Hs.eg.db,\n                  keyType = \\SYMBOL\\,\n                  ont = \\BP\\,\n                  pAdjustMethod = \\BH\\,\n                  qvalueCutoff = 0.05,\n                  readable = TRUE)\n  \n  p <- dotplot(ego, showCategory = 20, title = paste(\\GO -\\, title)) +\n    theme(axis.text.y = element_text(size = 8))\n  \n  print(p)\n  png(paste0(\\GO_enrichment_\\, gsub(\\ \\, \\_\\, title), \\.png\\), width = 12, height = 8, units = \\in\\, res = 300)\n  print(p)\n  dev.off()\n  \n  return(ego)\n}\n\nperform_kegg_enrichment <- function(gene_list, gene_universe, title) {\n  # Convert gene symbols to Entrez IDs\n  entrez_ids <- bitr(gene_list, fromType = \\SYMBOL\\, toType = \\ENTREZID\\, OrgDb = org.Hs.eg.db)$ENTREZID\n  universe_entrez <- bitr(gene_universe, fromType = \\SYMBOL\\, toType = \\ENTREZID\\, OrgDb = org.Hs.eg.db)$ENTREZID\n  \n  ekegg <- enrichKEGG(gene = entrez_ids,\n                      universe = universe_entrez,\n                      organism = 'hsa',\n                      keyType = \\kegg\\,\n                      pvalueCutoff = 0.05,\n                      pAdjustMethod = \\BH\\)\n  \n  p <- dotplot(ekegg, showCategory = 20, title = paste(\\KEGG -\\, title)) +\n    theme(axis.text.y = element_text(size = 8))\n  \n  print(p)\n  png(paste0(\\KEGG_enrichment_\\, gsub(\\ \\, \\_\\, title), \\.png\\), width = 12, height = 8, units = \\in\\, res = 300)\n  print(p)\n  dev.off()\n  \n  return(ekegg)\n}\n\ngene_universe <- rownames(All_samples_Merged)\n\n# Patient 1 (P1) comparison: L1 vs L2\nupregulated_genes_P1 <- rownames(p1_comparison[p1_comparison$avg_log2FC > 1 & p1_comparison$p_val_adj < 0.05, ])\ndownregulated_genes_P1 <- rownames(p1_comparison[p1_comparison$avg_log2FC < -1 & p1_comparison$p_val_adj < 0.05, ])\n\ngo_up_P1 <- perform_go_enrichment(upregulated_genes_P1, gene_universe, \\Upregulated Genes in L1 vs L2\\)\ngo_down_P1 <- perform_go_enrichment(downregulated_genes_P1, gene_universe, \\Downregulated Genes in L1 vs L2\\)\nkegg_up_P1 <- perform_kegg_enrichment(upregulated_genes_P1, gene_universe, \\Upregulated Genes in L1 vs L2\\)\nkegg_down_P1 <- perform_kegg_enrichment(downregulated_genes_P1, gene_universe, \\Downregulated Genes in L1 vs L2\\)\n\n# Patient 2 (P2) comparison: L3 vs L4\nupregulated_genes_P2 <- rownames(p2_comparison[p2_comparison$avg_log2FC > 1 & p2_comparison$p_val_adj < 0.05, ])\ndownregulated_genes_P2 <- rownames(p2_comparison[p2_comparison$avg_log2FC < -1 & p2_comparison$p_val_adj < 0.05, ])\n\ngo_up_P2 <- perform_go_enrichment(upregulated_genes_P2, gene_universe, \\Upregulated Genes in L3 vs L4\\)\ngo_down_P2 <- perform_go_enrichment(downregulated_genes_P2, gene_universe, \\Downregulated Genes in L3 vs L4\\)\nkegg_up_P2 <- perform_kegg_enrichment(upregulated_genes_P2, gene_universe, \\Upregulated Genes in L3 vs L4\\)\nkegg_down_P2 <- perform_kegg_enrichment(downregulated_genes_P2, gene_universe, \\Downregulated Genes in L3 vs L4\\)\n\n# Patient 3 (P3) comparisons\n# L5 vs L6\nupregulated_genes_P3_L5L6 <- rownames(p3_comparison_L5L6[p3_comparison_L5L6$avg_log2FC > 1 & p3_comparison_L5L6$p_val_adj < 0.05, ])\ndownregulated_genes_P3_L5L6 <- rownames(p3_comparison_L5L6[p3_comparison_L5L6$avg_log2FC < -1 & p3_comparison_L5L6$p_val_adj < 0.05, ])\n\ngo_up_P3_L5L6 <- perform_go_enrichment(upregulated_genes_P3_L5L6, gene_universe, \\Upregulated Genes in L5 vs L6\\)\ngo_down_P3_L5L6 <- perform_go_enrichment(downregulated_genes_P3_L5L6, gene_universe, \\Downregulated Genes in L5 vs L6\\)\nkegg_up_P3_L5L6 <- perform_kegg_enrichment(upregulated_genes_P3_L5L6, gene_universe, \\Upregulated Genes in L5 vs L6\\)\nkegg_down_P3_L5L6 <- perform_kegg_enrichment(downregulated_genes_P3_L5L6, gene_universe, \\Downregulated Genes in L5 vs L6\\)\n\n# L5 vs L7\nupregulated_genes_P3_L5L7 <- rownames(p3_comparison_L5L7[p3_comparison_L5L7$avg_log2FC > 1 & p3_comparison_L5L7$p_val_adj < 0.05, ])\ndownregulated_genes_P3_L5L7 <- rownames(p3_comparison_L5L7[p3_comparison_L5L7$avg_log2FC < -1 & p3_comparison_L5L7$p_val_adj < 0.05, ])\n\ngo_up_P3_L5L7 <- perform_go_enrichment(upregulated_genes_P3_L5L7, gene_universe, \\Upregulated Genes in L5 vs L7\\)\ngo_down_P3_L5L7 <- perform_go_enrichment(downregulated_genes_P3_L5L7, gene_universe, \\Downregulated Genes in L5 vs L7\\)\nkegg_up_P3_L5L7 <- perform_kegg_enrichment(upregulated_genes_P3_L5L7, gene_universe, \\Upregulated Genes in L5 vs L7\\)\nkegg_down_P3_L5L7 <- perform_kegg_enrichment(downregulated_genes_P3_L5L7, gene_universe, \\Downregulated Genes in L5 vs L7\\)\n\n# L6 vs L7\nupregulated_genes_P3_L6L7 <- rownames(p3_comparison_L6L7[p3_comparison_L6L7$avg_log2FC > 1 & p3_comparison_L6L7$p_val_adj < 0.05, ])\ndownregulated_genes_P3_L6L7 <- rownames(p3_comparison_L6L7[p3_comparison_L6L7$avg_log2FC < -1 & p3_comparison_L6L7$p_val_adj < 0.05, ])\n\ngo_up_P3_L6L7 <- perform_go_enrichment(upregulated_genes_P3_L6L7, gene_universe, \\Upregulated Genes in L6 vs L7\\)\ngo_down_P3_L6L7 <- perform_go_enrichment(downregulated_genes_P3_L6L7, gene_universe, \\Downregulated Genes in L6 vs L7\\)\nkegg_up_P3_L6L7 <- perform_kegg_enrichment(upregulated_genes_P3_L6L7, gene_universe, \\Upregulated Genes in L6 vs L7\\)\nkegg_down_P3_L6L7 <- perform_kegg_enrichment(downregulated_genes_P3_L6L7, gene_universe, \\Downregulated Genes in L6 vs L7\\)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->


<!-- rnb-source-begin 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 -->

```r
```r

perform_go_enrichment <- function(gene_list, gene_universe, title) {
  ego <- enrichGO(gene = gene_list,
                  universe = gene_universe,
                  OrgDb = org.Hs.eg.db,
                  keyType = \SYMBOL\,
                  ont = \BP\,
                  pAdjustMethod = \BH\,
                  qvalueCutoff = 0.05,
                  readable = TRUE)
  
  p <- dotplot(ego, showCategory = 20, title = paste(\GO -\, title)) +
    theme(axis.text.y = element_text(size = 8))
  
  print(p)
  png(paste0(\GO_enrichment_\, gsub(\ \, \_\, title), \.png\), width = 12, height = 8, units = \in\, res = 300)
  print(p)
  dev.off()
  
  return(ego)
}

perform_kegg_enrichment <- function(gene_list, gene_universe, title) {
  # Convert gene symbols to Entrez IDs
  entrez_ids <- bitr(gene_list, fromType = \SYMBOL\, toType = \ENTREZID\, OrgDb = org.Hs.eg.db)$ENTREZID
  universe_entrez <- bitr(gene_universe, fromType = \SYMBOL\, toType = \ENTREZID\, OrgDb = org.Hs.eg.db)$ENTREZID
  
  ekegg <- enrichKEGG(gene = entrez_ids,
                      universe = universe_entrez,
                      organism = 'hsa',
                      keyType = \kegg\,
                      pvalueCutoff = 0.05,
                      pAdjustMethod = \BH\)
  
  p <- dotplot(ekegg, showCategory = 20, title = paste(\KEGG -\, title)) +
    theme(axis.text.y = element_text(size = 8))
  
  print(p)
  png(paste0(\KEGG_enrichment_\, gsub(\ \, \_\, title), \.png\), width = 12, height = 8, units = \in\, res = 300)
  print(p)
  dev.off()
  
  return(ekegg)
}

gene_universe <- rownames(All_samples_Merged)

# Patient 1 (P1) comparison: L1 vs L2
upregulated_genes_P1 <- rownames(p1_comparison[p1_comparison$avg_log2FC > 1 & p1_comparison$p_val_adj < 0.05, ])
downregulated_genes_P1 <- rownames(p1_comparison[p1_comparison$avg_log2FC < -1 & p1_comparison$p_val_adj < 0.05, ])

go_up_P1 <- perform_go_enrichment(upregulated_genes_P1, gene_universe, \Upregulated Genes in L1 vs L2\)
go_down_P1 <- perform_go_enrichment(downregulated_genes_P1, gene_universe, \Downregulated Genes in L1 vs L2\)
kegg_up_P1 <- perform_kegg_enrichment(upregulated_genes_P1, gene_universe, \Upregulated Genes in L1 vs L2\)
kegg_down_P1 <- perform_kegg_enrichment(downregulated_genes_P1, gene_universe, \Downregulated Genes in L1 vs L2\)

# Patient 2 (P2) comparison: L3 vs L4
upregulated_genes_P2 <- rownames(p2_comparison[p2_comparison$avg_log2FC > 1 & p2_comparison$p_val_adj < 0.05, ])
downregulated_genes_P2 <- rownames(p2_comparison[p2_comparison$avg_log2FC < -1 & p2_comparison$p_val_adj < 0.05, ])

go_up_P2 <- perform_go_enrichment(upregulated_genes_P2, gene_universe, \Upregulated Genes in L3 vs L4\)
go_down_P2 <- perform_go_enrichment(downregulated_genes_P2, gene_universe, \Downregulated Genes in L3 vs L4\)
kegg_up_P2 <- perform_kegg_enrichment(upregulated_genes_P2, gene_universe, \Upregulated Genes in L3 vs L4\)
kegg_down_P2 <- perform_kegg_enrichment(downregulated_genes_P2, gene_universe, \Downregulated Genes in L3 vs L4\)

# Patient 3 (P3) comparisons
# L5 vs L6
upregulated_genes_P3_L5L6 <- rownames(p3_comparison_L5L6[p3_comparison_L5L6$avg_log2FC > 1 & p3_comparison_L5L6$p_val_adj < 0.05, ])
downregulated_genes_P3_L5L6 <- rownames(p3_comparison_L5L6[p3_comparison_L5L6$avg_log2FC < -1 & p3_comparison_L5L6$p_val_adj < 0.05, ])

go_up_P3_L5L6 <- perform_go_enrichment(upregulated_genes_P3_L5L6, gene_universe, \Upregulated Genes in L5 vs L6\)
go_down_P3_L5L6 <- perform_go_enrichment(downregulated_genes_P3_L5L6, gene_universe, \Downregulated Genes in L5 vs L6\)
kegg_up_P3_L5L6 <- perform_kegg_enrichment(upregulated_genes_P3_L5L6, gene_universe, \Upregulated Genes in L5 vs L6\)
kegg_down_P3_L5L6 <- perform_kegg_enrichment(downregulated_genes_P3_L5L6, gene_universe, \Downregulated Genes in L5 vs L6\)

# L5 vs L7
upregulated_genes_P3_L5L7 <- rownames(p3_comparison_L5L7[p3_comparison_L5L7$avg_log2FC > 1 & p3_comparison_L5L7$p_val_adj < 0.05, ])
downregulated_genes_P3_L5L7 <- rownames(p3_comparison_L5L7[p3_comparison_L5L7$avg_log2FC < -1 & p3_comparison_L5L7$p_val_adj < 0.05, ])

go_up_P3_L5L7 <- perform_go_enrichment(upregulated_genes_P3_L5L7, gene_universe, \Upregulated Genes in L5 vs L7\)
go_down_P3_L5L7 <- perform_go_enrichment(downregulated_genes_P3_L5L7, gene_universe, \Downregulated Genes in L5 vs L7\)
kegg_up_P3_L5L7 <- perform_kegg_enrichment(upregulated_genes_P3_L5L7, gene_universe, \Upregulated Genes in L5 vs L7\)
kegg_down_P3_L5L7 <- perform_kegg_enrichment(downregulated_genes_P3_L5L7, gene_universe, \Downregulated Genes in L5 vs L7\)

# L6 vs L7
upregulated_genes_P3_L6L7 <- rownames(p3_comparison_L6L7[p3_comparison_L6L7$avg_log2FC > 1 & p3_comparison_L6L7$p_val_adj < 0.05, ])
downregulated_genes_P3_L6L7 <- rownames(p3_comparison_L6L7[p3_comparison_L6L7$avg_log2FC < -1 & p3_comparison_L6L7$p_val_adj < 0.05, ])

go_up_P3_L6L7 <- perform_go_enrichment(upregulated_genes_P3_L6L7, gene_universe, \Upregulated Genes in L6 vs L7\)
go_down_P3_L6L7 <- perform_go_enrichment(downregulated_genes_P3_L6L7, gene_universe, \Downregulated Genes in L6 vs L7\)
kegg_up_P3_L6L7 <- perform_kegg_enrichment(upregulated_genes_P3_L6L7, gene_universe, \Upregulated Genes in L6 vs L7\)
kegg_down_P3_L6L7 <- perform_kegg_enrichment(downregulated_genes_P3_L6L7, gene_universe, \Downregulated Genes in L6 vs L7\)

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# 8. Network Analysis

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-->\n\n```r\n```r\n# Function to get top genes from a comparison\n\nlibrary(igraph)\nlibrary(STRINGdb)\nlibrary(ggraph)\nlibrary(tidyverse)\nlibrary(tibble)\n\nget_top_genes <- function(comparison_result, n = 50) {\n  top_genes <- comparison_result %>%\n    rownames_to_column(\\gene\\) %>%\n    arrange(desc(abs(avg_log2FC))) %>%\n    head(n) %>%\n    pull(gene)\n  return(top_genes)\n}\n\n# Combine top genes from all comparisons\nall_top_genes <- unique(c(\n  get_top_genes(p1_comparison),\n  get_top_genes(p2_comparison),\n  get_top_genes(p3_comparison_L5L6),\n  get_top_genes(p3_comparison_L5L7),\n  get_top_genes(p3_comparison_L6L7)\n))\n\n# Initialize STRINGdb\nstring_db <- STRINGdb$new(version=\\11\\, species=9606, score_threshold=700)\n\n# Map genes to STRING identifiers\nmapped_genes <- string_db$map(data.frame(gene=all_top_genes), \\gene\\, removeUnmappedRows = TRUE)\n\n# Get interactions\ninteractions <- string_db$get_interactions(mapped_genes$STRING_id)\n\n# Create igraph object\ng <- graph_from_data_frame(interactions, directed = FALSE)\n\n# Calculate node degrees\nV(g)$degree <- degree(g)\n\n# Calculate betweenness centrality\nV(g)$betweenness <- betweenness(g)\n\n# Identify communities\ncommunities <- cluster_louvain(g)\nV(g)$community <- communities$membership\n\n# Plot the network\nset.seed(123)  # for reproducibility\nggraph(g, layout = \\fr\\) +\n  geom_edge_link(aes(edge_alpha = combined_score), show.legend = FALSE) +\n  geom_node_point(aes(color = factor(community), size = degree)) +\n  geom_node_text(aes(label = name), repel = TRUE, size = 3) +\n  scale_color_brewer(palette = \\Set1\\) +\n  theme_void() +\n  labs(title = \\Gene Interaction Network\\,\n       subtitle = \\Based on top differentially expressed genes\\,\n       color = \\Community\\,\n       size = \\Degree\\)\n\n# Save the plot\nggsave(\\gene_interaction_network.png\\, width = 12, height = 10, dpi = 300)\n\n# Identify hub genes\nhub_genes <- V(g)$name[order(V(g)$degree, decreasing = TRUE)][1:10]\ncat(\\Top 10 hub genes:\\n\\)\nprint(hub_genes)\n\n# Identify genes with high betweenness centrality\nhigh_betweenness_genes <- V(g)$name[order(V(g)$betweenness, decreasing = TRUE)][1:10]\ncat(\\\\nTop 10 genes with high betweenness centrality:\\n\\)\nprint(high_betweenness_genes)\n\n# Calculate and print some network statistics\ncat(\\\\nNetwork Statistics:\\n\\)\ncat(\\Number of nodes:\\, vcount(g), \\\\n\\)\ncat(\\Number of edges:\\, ecount(g), \\\\n\\)\ncat(\\Network density:\\, edge_density(g), \\\\n\\)\ncat(\\Average path length:\\, mean_distance(g), \\\\n\\)\ncat(\\Clustering coefficient:\\, transitivity(g), \\\\n\\)\n\n# Extract edge information\nedges_df <- data.frame(\n  from = ends(g, E(g))[,1],\n  to = ends(g, E(g))[,2]\n)\n\n# Add edge attributes if any\nedge_attrs <- edge_attr(g)\nfor (attr in names(edge_attrs)) {\n  edges_df[[attr]] <- edge_attrs[[attr]]\n}\n\n# Save the edges data frame\nwrite.csv(edges_df, \\gene_network_edges.csv\\, row.names = FALSE)\n\n# Extract node information\nnodes_df <- data.frame(\n  id = V(g)$name,\n  degree = degree(g),\n  betweenness = betweenness(g)\n)\n\n# Add other vertex attributes if any\nvertex_attrs <- vertex_attr(g)\nfor (attr in names(vertex_attrs)) {\n  if (attr != \\name\\) {  # Skip 'name' as we already have it as 'id'\n    nodes_df[[attr]] <- vertex_attrs[[attr]]\n  }\n}\n\n# Save the nodes data frame\nwrite.csv(nodes_df, \\gene_network_nodes.csv\\, row.names = FALSE)\n\n# Print a summary of the saved data\ncat(\\Saved network data:\\n\\)\ncat(\\Edges file: gene_network_edges.csv -\\, nrow(edges_df), \\rows\\n\\)\ncat(\\Nodes file: gene_network_nodes.csv -\\, nrow(nodes_df), \\rows\\n\\)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->


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```r
```r
# Function to get top genes from a comparison

library(igraph)
library(STRINGdb)
library(ggraph)
library(tidyverse)
library(tibble)

get_top_genes <- function(comparison_result, n = 50) {
  top_genes <- comparison_result %>%
    rownames_to_column(\gene\) %>%
    arrange(desc(abs(avg_log2FC))) %>%
    head(n) %>%
    pull(gene)
  return(top_genes)
}

# Combine top genes from all comparisons
all_top_genes <- unique(c(
  get_top_genes(p1_comparison),
  get_top_genes(p2_comparison),
  get_top_genes(p3_comparison_L5L6),
  get_top_genes(p3_comparison_L5L7),
  get_top_genes(p3_comparison_L6L7)
))

# Initialize STRINGdb
string_db <- STRINGdb$new(version=\11\, species=9606, score_threshold=700)

# Map genes to STRING identifiers
mapped_genes <- string_db$map(data.frame(gene=all_top_genes), \gene\, removeUnmappedRows = TRUE)

# Get interactions
interactions <- string_db$get_interactions(mapped_genes$STRING_id)

# Create igraph object
g <- graph_from_data_frame(interactions, directed = FALSE)

# Calculate node degrees
V(g)$degree <- degree(g)

# Calculate betweenness centrality
V(g)$betweenness <- betweenness(g)

# Identify communities
communities <- cluster_louvain(g)
V(g)$community <- communities$membership

# Plot the network
set.seed(123)  # for reproducibility
ggraph(g, layout = \fr\) +
  geom_edge_link(aes(edge_alpha = combined_score), show.legend = FALSE) +
  geom_node_point(aes(color = factor(community), size = degree)) +
  geom_node_text(aes(label = name), repel = TRUE, size = 3) +
  scale_color_brewer(palette = \Set1\) +
  theme_void() +
  labs(title = \Gene Interaction Network\,
       subtitle = \Based on top differentially expressed genes\,
       color = \Community\,
       size = \Degree\)

# Save the plot
ggsave(\gene_interaction_network.png\, width = 12, height = 10, dpi = 300)

# Identify hub genes
hub_genes <- V(g)$name[order(V(g)$degree, decreasing = TRUE)][1:10]
cat(\Top 10 hub genes:\n\)
print(hub_genes)

# Identify genes with high betweenness centrality
high_betweenness_genes <- V(g)$name[order(V(g)$betweenness, decreasing = TRUE)][1:10]
cat(\\nTop 10 genes with high betweenness centrality:\n\)
print(high_betweenness_genes)

# Calculate and print some network statistics
cat(\\nNetwork Statistics:\n\)
cat(\Number of nodes:\, vcount(g), \\n\)
cat(\Number of edges:\, ecount(g), \\n\)
cat(\Network density:\, edge_density(g), \\n\)
cat(\Average path length:\, mean_distance(g), \\n\)
cat(\Clustering coefficient:\, transitivity(g), \\n\)

# Extract edge information
edges_df <- data.frame(
  from = ends(g, E(g))[,1],
  to = ends(g, E(g))[,2]
)

# Add edge attributes if any
edge_attrs <- edge_attr(g)
for (attr in names(edge_attrs)) {
  edges_df[[attr]] <- edge_attrs[[attr]]
}

# Save the edges data frame
write.csv(edges_df, \gene_network_edges.csv\, row.names = FALSE)

# Extract node information
nodes_df <- data.frame(
  id = V(g)$name,
  degree = degree(g),
  betweenness = betweenness(g)
)

# Add other vertex attributes if any
vertex_attrs <- vertex_attr(g)
for (attr in names(vertex_attrs)) {
  if (attr != \name\) {  # Skip 'name' as we already have it as 'id'
    nodes_df[[attr]] <- vertex_attrs[[attr]]
  }
}

# Save the nodes data frame
write.csv(nodes_df, \gene_network_nodes.csv\, row.names = FALSE)

# Print a summary of the saved data
cat(\Saved network data:\n\)
cat(\Edges file: gene_network_edges.csv -\, nrow(edges_df), \rows\n\)
cat(\Nodes file: gene_network_nodes.csv -\, nrow(nodes_df), \rows\n\)

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-->\n\n```r\n```r\n# Function to get top genes from a comparison\n\nlibrary(igraph)\nlibrary(STRINGdb)\nlibrary(ggraph)\nlibrary(tidyverse)\nlibrary(tibble)\n\nget_top_genes <- function(comparison_result, n = 50) {\n  top_genes <- comparison_result %>%\n    rownames_to_column(\\gene\\) %>%\n    arrange(desc(abs(avg_log2FC))) %>%\n    head(n) %>%\n    pull(gene)\n  return(top_genes)\n}\n\n# Combine top genes from all comparisons\nall_top_genes <- unique(c(\n  get_top_genes(p1_comparison),\n  get_top_genes(p2_comparison),\n  get_top_genes(p3_comparison_L5L6),\n  get_top_genes(p3_comparison_L5L7),\n  get_top_genes(p3_comparison_L6L7)\n))\n\n# Initialize STRINGdb\nstring_db <- STRINGdb$new(version=\\11\\, species=9606, score_threshold=700)\n\n# Map genes to STRING identifiers\nmapped_genes <- string_db$map(data.frame(gene=all_top_genes), \\gene\\, removeUnmappedRows = TRUE)\n\n# Get interactions\ninteractions <- string_db$get_interactions(mapped_genes$STRING_id)\n\n# Map STRING identifiers back to gene symbols\ninteractions$from <- mapped_genes$gene[match(interactions$from, mapped_genes$STRING_id)]\ninteractions$to <- mapped_genes$gene[match(interactions$to, mapped_genes$STRING_id)]\n\n# Create igraph object\ng <- graph_from_data_frame(interactions, directed = FALSE)\n\n# Calculate node degrees\nV(g)$degree <- degree(g)\n\n# Calculate betweenness centrality\nV(g)$betweenness <- betweenness(g)\n\n# Identify communities\ncommunities <- cluster_louvain(g)\nV(g)$community <- communities$membership\n\n# Plot the network\nset.seed(123)  # for reproducibility\nggraph(g, layout = \\fr\\) +\n  geom_edge_link(aes(edge_alpha = combined_score), show.legend = FALSE) +\n  geom_node_point(aes(color = factor(community), size = degree)) +\n  geom_node_text(aes(label = name), repel = TRUE, size = 3) +\n  scale_color_brewer(palette = \\Set1\\) +\n  theme_void() +\n  labs(title = \\Gene Interaction Network\\,\n       subtitle = \\Based on top differentially expressed genes\\,\n       color = \\Community\\,\n       size = \\Degree\\)\n\n# Save the plot\nggsave(\\gene_interaction_network.png\\, width = 12, height = 10, dpi = 300)\n\n# Identify hub genes\nhub_genes <- V(g)$name[order(V(g)$degree, decreasing = TRUE)][1:10]\ncat(\\Top 10 hub genes:\\n\\)\nprint(hub_genes)\n\n# Identify genes with high betweenness centrality\nhigh_betweenness_genes <- V(g)$name[order(V(g)$betweenness, decreasing = TRUE)][1:10]\ncat(\\\\nTop 10 genes with high betweenness centrality:\\n\\)\nprint(high_betweenness_genes)\n\n# Calculate and print some network statistics\ncat(\\\\nNetwork Statistics:\\n\\)\ncat(\\Number of nodes:\\, vcount(g), \\\\n\\)\ncat(\\Number of edges:\\, ecount(g), \\\\n\\)\ncat(\\Network density:\\, edge_density(g), \\\\n\\)\ncat(\\Average path length:\\, mean_distance(g), \\\\n\\)\ncat(\\Clustering coefficient:\\, transitivity(g), \\\\n\\)\n\n# Extract edge information\nedges_df <- data.frame(\n  from = ends(g, E(g))[,1],\n  to = ends(g, E(g))[,2]\n)\n\n# Add edge attributes if any\nedge_attrs <- edge_attr(g)\nfor (attr in names(edge_attrs)) {\n  edges_df[[attr]] <- edge_attrs[[attr]]\n}\n\n# Save the edges data frame\nwrite.csv(edges_df, \\gene_network_edges.csv\\, row.names = FALSE)\n\n# Extract node information\nnodes_df <- data.frame(\n  id = V(g)$name,\n  degree = degree(g),\n  betweenness = betweenness(g)\n)\n\n# Add other vertex attributes if any\nvertex_attrs <- vertex_attr(g)\nfor (attr in names(vertex_attrs)) {\n  if (attr != \\name\\) {  # Skip 'name' as we already have it as 'id'\n    nodes_df[[attr]] <- vertex_attrs[[attr]]\n  }\n}\n\n# Save the nodes data frame\nwrite.csv(nodes_df, \\gene_network_nodes.csv\\, row.names = FALSE)\n\n# Print a summary of the saved data\ncat(\\Saved network data:\\n\\)\ncat(\\Edges file: gene_network_edges.csv -\\, nrow(edges_df), \\rows\\n\\)\ncat(\\Nodes file: gene_network_nodes.csv -\\, nrow(nodes_df), \\rows\\n\\)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->


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-->

```r
```r
# Function to get top genes from a comparison

library(igraph)
library(STRINGdb)
library(ggraph)
library(tidyverse)
library(tibble)

get_top_genes <- function(comparison_result, n = 50) {
  top_genes <- comparison_result %>%
    rownames_to_column(\gene\) %>%
    arrange(desc(abs(avg_log2FC))) %>%
    head(n) %>%
    pull(gene)
  return(top_genes)
}

# Combine top genes from all comparisons
all_top_genes <- unique(c(
  get_top_genes(p1_comparison),
  get_top_genes(p2_comparison),
  get_top_genes(p3_comparison_L5L6),
  get_top_genes(p3_comparison_L5L7),
  get_top_genes(p3_comparison_L6L7)
))

# Initialize STRINGdb
string_db <- STRINGdb$new(version=\11\, species=9606, score_threshold=700)

# Map genes to STRING identifiers
mapped_genes <- string_db$map(data.frame(gene=all_top_genes), \gene\, removeUnmappedRows = TRUE)

# Get interactions
interactions <- string_db$get_interactions(mapped_genes$STRING_id)

# Map STRING identifiers back to gene symbols
interactions$from <- mapped_genes$gene[match(interactions$from, mapped_genes$STRING_id)]
interactions$to <- mapped_genes$gene[match(interactions$to, mapped_genes$STRING_id)]

# Create igraph object
g <- graph_from_data_frame(interactions, directed = FALSE)

# Calculate node degrees
V(g)$degree <- degree(g)

# Calculate betweenness centrality
V(g)$betweenness <- betweenness(g)

# Identify communities
communities <- cluster_louvain(g)
V(g)$community <- communities$membership

# Plot the network
set.seed(123)  # for reproducibility
ggraph(g, layout = \fr\) +
  geom_edge_link(aes(edge_alpha = combined_score), show.legend = FALSE) +
  geom_node_point(aes(color = factor(community), size = degree)) +
  geom_node_text(aes(label = name), repel = TRUE, size = 3) +
  scale_color_brewer(palette = \Set1\) +
  theme_void() +
  labs(title = \Gene Interaction Network\,
       subtitle = \Based on top differentially expressed genes\,
       color = \Community\,
       size = \Degree\)

# Save the plot
ggsave(\gene_interaction_network.png\, width = 12, height = 10, dpi = 300)

# Identify hub genes
hub_genes <- V(g)$name[order(V(g)$degree, decreasing = TRUE)][1:10]
cat(\Top 10 hub genes:\n\)
print(hub_genes)

# Identify genes with high betweenness centrality
high_betweenness_genes <- V(g)$name[order(V(g)$betweenness, decreasing = TRUE)][1:10]
cat(\\nTop 10 genes with high betweenness centrality:\n\)
print(high_betweenness_genes)

# Calculate and print some network statistics
cat(\\nNetwork Statistics:\n\)
cat(\Number of nodes:\, vcount(g), \\n\)
cat(\Number of edges:\, ecount(g), \\n\)
cat(\Network density:\, edge_density(g), \\n\)
cat(\Average path length:\, mean_distance(g), \\n\)
cat(\Clustering coefficient:\, transitivity(g), \\n\)

# Extract edge information
edges_df <- data.frame(
  from = ends(g, E(g))[,1],
  to = ends(g, E(g))[,2]
)

# Add edge attributes if any
edge_attrs <- edge_attr(g)
for (attr in names(edge_attrs)) {
  edges_df[[attr]] <- edge_attrs[[attr]]
}

# Save the edges data frame
write.csv(edges_df, \gene_network_edges.csv\, row.names = FALSE)

# Extract node information
nodes_df <- data.frame(
  id = V(g)$name,
  degree = degree(g),
  betweenness = betweenness(g)
)

# Add other vertex attributes if any
vertex_attrs <- vertex_attr(g)
for (attr in names(vertex_attrs)) {
  if (attr != \name\) {  # Skip 'name' as we already have it as 'id'
    nodes_df[[attr]] <- vertex_attrs[[attr]]
  }
}

# Save the nodes data frame
write.csv(nodes_df, \gene_network_nodes.csv\, row.names = FALSE)

# Print a summary of the saved data
cat(\Saved network data:\n\)
cat(\Edges file: gene_network_edges.csv -\, nrow(edges_df), \rows\n\)
cat(\Nodes file: gene_network_nodes.csv -\, nrow(nodes_df), \rows\n\)

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# 9. Network Analysis-kegg

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-->\n\n```r\n```r\n\n# Load required libraries\nlibrary(igraph)\nlibrary(ggraph)\nlibrary(tidyverse)\nlibrary(tibble)\nlibrary(org.Hs.eg.db)\nlibrary(GO.db)\nlibrary(AnnotationDbi)\nlibrary(dplyr)\n\n# Function to get top genes from comparison results\nget_top_genes <- function(comparison_result, n = 50) {\n  top_genes <- comparison_result %>%\n    tibble::rownames_to_column(\\gene\\) %>%\n    dplyr::arrange(desc(abs(avg_log2FC))) %>%\n    head(n) %>%\n    dplyr::pull(gene)\n  return(top_genes)\n}\n\n# Combine top genes from all comparisons\nall_top_genes <- unique(c(\n  get_top_genes(p1_comparison),\n  get_top_genes(p2_comparison),\n  get_top_genes(p3_comparison_L5L6),\n  get_top_genes(p3_comparison_L5L7),\n  get_top_genes(p3_comparison_L6L7)\n))\n\n# Get GO terms for all top genes\ngo_terms <- AnnotationDbi::select(org.Hs.eg.db, keys = all_top_genes, \n                                  columns = c(\\SYMBOL\\, \\GO\\, \\ONTOLOGY\\), \n                                  keytype = \\SYMBOL\\)\n\n# Filter for biological process GO terms and remove NA values\ngo_terms_bp <- go_terms %>%\n  dplyr::filter(ONTOLOGY == \\BP\\) %>%\n  dplyr::filter(!is.na(GO))\n\n# Create edges dataframe\nedges <- go_terms_bp %>%\n  dplyr::select(from = SYMBOL, to = GO)\n\n# Print summary of edges\nprint(paste(\\Number of edges:\\, nrow(edges)))\nprint(head(edges))\n\n# If edges dataframe is empty, stop here\nif (nrow(edges) == 0) {\n  stop(\\No GO terms found for any genes. Cannot create network.\\)\n}\n\n# Create graph\ng <- igraph::graph_from_data_frame(edges, directed = FALSE)\n\n# Calculate node degrees\nV(g)$degree <- igraph::degree(g)\n\n# Calculate betweenness centrality\nV(g)$betweenness <- igraph::betweenness(g)\n\n# Identify communities\ncommunities <- igraph::cluster_louvain(g)\nV(g)$community <- communities$membership\n\n# Get GO term descriptions\ngo_terms_desc <- AnnotationDbi::select(GO.db, keys = unique(edges$to), \n                                       columns = \\TERM\\, keytype = \\GOID\\)\n\n# Add GO term descriptions to the graph\nV(g)$description <- go_terms_desc$TERM[match(V(g)$name, go_terms_desc$GOID)]\n\n# Plot the network\nset.seed(123)  # for reproducibility\np <- ggraph(g, layout = \\fr\\) +\n  geom_edge_link(alpha = 0.1) +\n  geom_node_point(aes(color = factor(community), size = degree)) +\n  geom_node_text(aes(label = ifelse(degree > quantile(degree, 0.95), name, \\\\)), \n                 repel = TRUE, size = 3) +\n  scale_color_brewer(palette = \\Set1\\) +\n  theme_void() +\n  labs(title = \\Gene-GO Term Interaction Network\\,\n       subtitle = \\Based on top differentially expressed genes\\,\n       color = \\Community\\,\n       size = \\Degree\\)\n\n# Save the plot\nggsave(\\gene_go_network.png\\, p, width = 12, height = 10, dpi = 300)\n\n# Identify hub genes\nhub_genes <- V(g)$name[V(g)$name %in% all_top_genes][order(V(g)$degree[V(g)$name %in% all_top_genes], decreasing = TRUE)][1:10]\ncat(\\Top 10 hub genes:\\n\\)\nprint(hub_genes)\n\n# Identify genes with high betweenness centrality\nhigh_betweenness_genes <- V(g)$name[V(g)$name %in% all_top_genes][order(V(g)$betweenness[V(g)$name %in% all_top_genes], decreasing = TRUE)][1:10]\ncat(\\\\nTop 10 genes with high betweenness centrality:\\n\\)\nprint(high_betweenness_genes)\n\n# Calculate and print some network statistics\ncat(\\\\nNetwork Statistics:\\n\\)\ncat(\\Number of nodes:\\, igraph::vcount(g), \\\\n\\)\ncat(\\Number of edges:\\, igraph::ecount(g), \\\\n\\)\ncat(\\Network density:\\, igraph::edge_density(g), \\\\n\\)\ncat(\\Average path length:\\, igraph::mean_distance(g), \\\\n\\)\ncat(\\Clustering coefficient:\\, igraph::transitivity(g), \\\\n\\)\n\n# Extract edge information\nedges_df <- igraph::as_data_frame(g, what = \\edges\\)\n\n# Save the edges data frame\nwrite.csv(edges_df, \\gene_go_edges.csv\\, row.names = FALSE)\n\n# Extract node information\nnodes_df <- igraph::as_data_frame(g, what = \\vertices\\)\n\n# Save the nodes data frame\nwrite.csv(nodes_df, \\gene_go_nodes.csv\\, row.names = FALSE)\n\n# Print a summary of the saved data\ncat(\\Saved network data:\\n\\)\ncat(\\Edges file: gene_go_edges.csv -\\, nrow(edges_df), \\rows\\n\\)\ncat(\\Nodes file: gene_go_nodes.csv -\\, nrow(nodes_df), \\rows\\n\\)\n\n# Print top GO terms\ntop_go_terms <- V(g)$name[!(V(g)$name %in% all_top_genes)][order(V(g)$degree[!(V(g)$name %in% all_top_genes)], decreasing = TRUE)][1:20]\ncat(\\\\nTop 20 GO terms:\\n\\)\nfor (term in top_go_terms) {\n  cat(term, \\-\\, V(g)$description[V(g)$name == term], \\\\n\\)\n}\n```\n```\n\n<!-- rnb-source-end -->\n"} -->


<!-- rnb-source-begin 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-->

```r
```r

# Load required libraries
library(igraph)
library(ggraph)
library(tidyverse)
library(tibble)
library(org.Hs.eg.db)
library(GO.db)
library(AnnotationDbi)
library(dplyr)

# Function to get top genes from comparison results
get_top_genes <- function(comparison_result, n = 50) {
  top_genes <- comparison_result %>%
    tibble::rownames_to_column(\gene\) %>%
    dplyr::arrange(desc(abs(avg_log2FC))) %>%
    head(n) %>%
    dplyr::pull(gene)
  return(top_genes)
}

# Combine top genes from all comparisons
all_top_genes <- unique(c(
  get_top_genes(p1_comparison),
  get_top_genes(p2_comparison),
  get_top_genes(p3_comparison_L5L6),
  get_top_genes(p3_comparison_L5L7),
  get_top_genes(p3_comparison_L6L7)
))

# Get GO terms for all top genes
go_terms <- AnnotationDbi::select(org.Hs.eg.db, keys = all_top_genes, 
                                  columns = c(\SYMBOL\, \GO\, \ONTOLOGY\), 
                                  keytype = \SYMBOL\)

# Filter for biological process GO terms and remove NA values
go_terms_bp <- go_terms %>%
  dplyr::filter(ONTOLOGY == \BP\) %>%
  dplyr::filter(!is.na(GO))

# Create edges dataframe
edges <- go_terms_bp %>%
  dplyr::select(from = SYMBOL, to = GO)

# Print summary of edges
print(paste(\Number of edges:\, nrow(edges)))
print(head(edges))

# If edges dataframe is empty, stop here
if (nrow(edges) == 0) {
  stop(\No GO terms found for any genes. Cannot create network.\)
}

# Create graph
g <- igraph::graph_from_data_frame(edges, directed = FALSE)

# Calculate node degrees
V(g)$degree <- igraph::degree(g)

# Calculate betweenness centrality
V(g)$betweenness <- igraph::betweenness(g)

# Identify communities
communities <- igraph::cluster_louvain(g)
V(g)$community <- communities$membership

# Get GO term descriptions
go_terms_desc <- AnnotationDbi::select(GO.db, keys = unique(edges$to), 
                                       columns = \TERM\, keytype = \GOID\)

# Add GO term descriptions to the graph
V(g)$description <- go_terms_desc$TERM[match(V(g)$name, go_terms_desc$GOID)]

# Plot the network
set.seed(123)  # for reproducibility
p <- ggraph(g, layout = \fr\) +
  geom_edge_link(alpha = 0.1) +
  geom_node_point(aes(color = factor(community), size = degree)) +
  geom_node_text(aes(label = ifelse(degree > quantile(degree, 0.95), name, \\)), 
                 repel = TRUE, size = 3) +
  scale_color_brewer(palette = \Set1\) +
  theme_void() +
  labs(title = \Gene-GO Term Interaction Network\,
       subtitle = \Based on top differentially expressed genes\,
       color = \Community\,
       size = \Degree\)

# Save the plot
ggsave(\gene_go_network.png\, p, width = 12, height = 10, dpi = 300)

# Identify hub genes
hub_genes <- V(g)$name[V(g)$name %in% all_top_genes][order(V(g)$degree[V(g)$name %in% all_top_genes], decreasing = TRUE)][1:10]
cat(\Top 10 hub genes:\n\)
print(hub_genes)

# Identify genes with high betweenness centrality
high_betweenness_genes <- V(g)$name[V(g)$name %in% all_top_genes][order(V(g)$betweenness[V(g)$name %in% all_top_genes], decreasing = TRUE)][1:10]
cat(\\nTop 10 genes with high betweenness centrality:\n\)
print(high_betweenness_genes)

# Calculate and print some network statistics
cat(\\nNetwork Statistics:\n\)
cat(\Number of nodes:\, igraph::vcount(g), \\n\)
cat(\Number of edges:\, igraph::ecount(g), \\n\)
cat(\Network density:\, igraph::edge_density(g), \\n\)
cat(\Average path length:\, igraph::mean_distance(g), \\n\)
cat(\Clustering coefficient:\, igraph::transitivity(g), \\n\)

# Extract edge information
edges_df <- igraph::as_data_frame(g, what = \edges\)

# Save the edges data frame
write.csv(edges_df, \gene_go_edges.csv\, row.names = FALSE)

# Extract node information
nodes_df <- igraph::as_data_frame(g, what = \vertices\)

# Save the nodes data frame
write.csv(nodes_df, \gene_go_nodes.csv\, row.names = FALSE)

# Print a summary of the saved data
cat(\Saved network data:\n\)
cat(\Edges file: gene_go_edges.csv -\, nrow(edges_df), \rows\n\)
cat(\Nodes file: gene_go_nodes.csv -\, nrow(nodes_df), \rows\n\)

# Print top GO terms
top_go_terms <- V(g)$name[!(V(g)$name %in% all_top_genes)][order(V(g)$degree[!(V(g)$name %in% all_top_genes)], decreasing = TRUE)][1:20]
cat(\\nTop 20 GO terms:\n\)
for (term in top_go_terms) {
  cat(term, \-\, V(g)$description[V(g)$name == term], \\n\)
}

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


# 10. Save the Seurat object as an Robj file

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNWNiaU56WVhabEtFRnNiRjl6WVcxd2JHVnpYMDFsY21kbFpDd2dabWxzWlNBOUlGeGNRV3hzWDNOaGJYQnNaWE5mVFdWeVoyVmtYMFJGTGxKdlltcGNYQ2xjYmx4dVhHNWdZR0JjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbmBgYHJcblxuXG4jc2F2ZShBbGxfc2FtcGxlc19NZXJnZWQsIGZpbGUgPSBcXEFsbF9zYW1wbGVzX01lcmdlZF9ERS5Sb2JqXFwpXG5cblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5cbiNzYXZlKEFsbF9zYW1wbGVzX01lcmdlZCwgZmlsZSA9IFxcQWxsX3NhbXBsZXNfTWVyZ2VkX0RFLlJvYmpcXClcblxuXG5gYGBcbmBgYCJ9 -->

```r
```r


#save(All_samples_Merged, file = \All_samples_Merged_DE.Robj\)

```