1 Step 1. Load Libraries

library(readxl)
library(clusterProfiler)
library(org.Hs.eg.db)
library(ReactomePA)
library(enrichplot)
library(msigdbr)
library(openxlsx)
library(stringr)
library(purrr)
library(tibble)
library(tidyr)
library(ggplot2)
library(dplyr)   # load LAST so dplyr verbs win the masking conflict

2 Step 2. Load Top100 Marker Table

markers <- read_excel("../Supplementary_Table_S6.xlsx") %>%
  rename_with(tolower) %>%
  mutate(cluster = as.character(cluster))

# Fix known outdated/renamed gene symbols
symbol_updates <- c("QARS" = "QARS1", "CARS" = "CARS1", "WARS" = "WARS1")
markers <- markers %>%
  mutate(gene = ifelse(gene %in% names(symbol_updates), symbol_updates[gene], gene)) %>%
  filter(gene != "46083.0")   # remove spreadsheet artifact

clusters_list <- as.character(sort(as.numeric(unique(markers$cluster))))
cat("Clusters found (numeric order):", paste(clusters_list, collapse = ", "), "\n")
Clusters found (numeric order): 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 
print(colnames(markers))
[1] "p_val"      "avg_log2fc" "pct.1"      "pct.2"      "p_val_adj"  "cluster"    "gene"

3 Step 3. Build Background Universe and Per-Cluster Gene Lists

# Background = all genes that appear anywhere in the marker table (all clusters combined)
background_genes <- unique(markers$gene)
cat("Background universe size:", length(background_genes), "genes\n")
Background universe size: 1095 genes
# Map SYMBOL -> ENTREZID (needed for enrichKEGG and ReactomePA)
gene_map <- bitr(background_genes, fromType = "SYMBOL", toType = "ENTREZID",
                  OrgDb = org.Hs.eg.db, drop = TRUE)

background_entrez <- unique(gene_map$ENTREZID)

get_cluster_genes <- function(cluster_id, marker_df, top_n = 100) {
  marker_df %>%
    filter(cluster == cluster_id) %>%
    distinct(gene, avg_log2fc) %>%
    arrange(desc(avg_log2fc)) %>%
    slice_head(n = top_n) %>%
    pull(gene)
}

cluster_gene_lists <- setNames(
  lapply(clusters_list, get_cluster_genes, marker_df = markers),
  clusters_list
)

sapply(cluster_gene_lists, length)
  0   1   2   3   4   5   6   7   8   9  10  11  12  13 
100 100 100 100 100 100 100 100 100 100 100  99 100 100

4 Step 4. Load Hallmark Gene Sets (for enricher())

hallmark_sets <- msigdbr(species = "Homo sapiens", collection = "H") %>%
  distinct(gs_name, gene_symbol)

5 Step 5. Run ORA for Each Cluster (GO:BP, KEGG, Reactome, Hallmark)

all_results <- list()

for (cl in clusters_list) {
  message("Running ORA for cluster ", cl)

  genes_symbol <- cluster_gene_lists[[cl]]
  genes_entrez <- gene_map$ENTREZID[gene_map$SYMBOL %in% genes_symbol]

  res_list <- list()

  # GO:BP
  res_list$GO_BP <- tryCatch(
    enrichGO(gene = genes_symbol, universe = background_genes,
             OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
             ont = "BP", pAdjustMethod = "BH",
             pvalueCutoff = 1, qvalueCutoff = 1),
    error = function(e) { message("  GO:BP failed: ", e$message); NULL })

  # KEGG
  res_list$KEGG <- tryCatch(
    enrichKEGG(gene = genes_entrez, universe = background_entrez,
               organism = "hsa", pAdjustMethod = "BH",
               pvalueCutoff = 1, qvalueCutoff = 1),
    error = function(e) { message("  KEGG failed: ", e$message); NULL })

  # Reactome
  res_list$Reactome <- tryCatch(
    enrichPathway(gene = genes_entrez, universe = background_entrez,
                   organism = "human", pAdjustMethod = "BH",
                   pvalueCutoff = 1, qvalueCutoff = 1, readable = TRUE),
    error = function(e) { message("  Reactome failed: ", e$message); NULL })

  # Hallmark (via generic enricher)
  res_list$Hallmark <- tryCatch(
    enricher(gene = genes_symbol, universe = background_genes,
             TERM2GENE = hallmark_sets, pAdjustMethod = "BH",
             pvalueCutoff = 1, qvalueCutoff = 1),
    error = function(e) { message("  Hallmark failed: ", e$message); NULL })

  all_results[[cl]] <- res_list
}

6 Step 6. Export All Enrichment Tables to Excel

wb <- createWorkbook()

for (cl in names(all_results)) {
  res <- all_results[[cl]]
  if (is.null(res)) next

  for (ont in names(res)) {
    obj <- res[[ont]]
    if (is.null(obj) || nrow(as.data.frame(obj)) == 0) next

    sheet_name <- substr(paste0("C", cl, "_", ont), 1, 31)
    addWorksheet(wb, sheet_name)
    writeData(wb, sheet_name, as.data.frame(obj))
  }
}

saveWorkbook(wb, "Cluster_ORA_Results_Top100.xlsx", overwrite = TRUE)

7 Step 7. Combine Top Terms per Cluster into One Long Table

build_long_ora <- function(all_results) {
  purrr::map_dfr(names(all_results), function(cl) {
    res <- all_results[[cl]]
    purrr::map_dfr(names(res), function(db) {
      obj <- res[[db]]
      if (is.null(obj)) return(NULL)
      df <- as.data.frame(obj)
      if (nrow(df) == 0) return(NULL)
      df <- as.data.frame(df)   # strip any lingering S4/tibble subclass
      df$cluster <- cl
      df$database <- db
      dplyr::select(df, cluster, database, ID, Description, GeneRatio, BgRatio,
                     pvalue, p.adjust, qvalue, geneID, Count)
    })
  })
}

all_long_ora <- build_long_ora(all_results)
write.csv(all_long_ora, "Cluster_ORA_Results_long.csv", row.names = FALSE)

cat("Total enriched terms across all clusters/databases:", nrow(all_long_ora), "\n")
Total enriched terms across all clusters/databases: 20335

8 Step 8. Check How Many Significant Terms per Cluster

all_long_ora %>%
  group_by(cluster) %>%
  summarise(n_sig_padj05 = sum(p.adjust < 0.05, na.rm = TRUE),
            n_sig_padj25 = sum(p.adjust < 0.25, na.rm = TRUE),
            n_total_terms = n(),
            min_padj = suppressWarnings(min(p.adjust, na.rm = TRUE))) %>%
  arrange(as.numeric(as.character(cluster)))

9 Step 9. Parse GeneRatio to Numeric (needed for dotplot x-axis)

parse_ratio <- function(x) {
  parts <- str_split(x, "/", simplify = TRUE)
  as.numeric(parts[, 1]) / as.numeric(parts[, 2])
}

all_long_ora <- all_long_ora %>%
  mutate(GeneRatioNum = parse_ratio(GeneRatio))

10 Step 10. Dotplot per Cluster (GeneRatio x, Count size, p.adjust color)

plot_cluster_ora_dotplot <- function(cluster_id, long_df, n_top = 10, sig_cutoff = 0.05) {

  df <- long_df %>%
    filter(cluster == cluster_id, p.adjust < sig_cutoff) %>%
    arrange(p.adjust) %>%
    slice_head(n = n_top)

  used_cutoff <- sig_cutoff

  # Fallback if nothing passes 0.05
  if (nrow(df) == 0) {
    df <- long_df %>%
      filter(cluster == cluster_id, p.adjust < 0.25) %>%
      arrange(p.adjust) %>%
      slice_head(n = n_top)
    used_cutoff <- 0.25
  }

  if (nrow(df) == 0) {
    cat("**No enriched terms reached p.adjust < 0.25 for cluster", cluster_id, "**\n\n")
    return(invisible(NULL))
  }

  df <- df %>%
    mutate(Description = str_wrap(Description, width = 40),
           label = paste0(Description, " [", database, "]"))

  p <- ggplot(df, aes(x = GeneRatioNum, y = reorder(label, GeneRatioNum),
                       size = Count, color = p.adjust)) +
    geom_point() +
    scale_color_gradient(low = "#B2182B", high = "#4393C3", name = "p.adjust") +
    scale_size_continuous(name = "Gene count", range = c(3, 9)) +
    labs(title = paste0("Cluster ", cluster_id, " - Top100 ORA (p.adjust<", used_cutoff, ")"),
         x = "Gene Ratio", y = NULL) +
    theme_minimal(base_size = 11) +
    theme(axis.text.y = element_text(size = 9))

  print(p)
  ggsave(filename = paste0("cluster", cluster_id, "_top100_ORA_dotplot.png"),
         plot = p, width = 9, height = 6, dpi = 300)
  cat("\n\n")
}

for (cl in clusters_list) {
  cat("\n## Cluster", cl, "- ORA Dotplot\n\n")
  plot_cluster_ora_dotplot(cl, all_long_ora)
}

10.1 Cluster 0 - ORA Dotplot

10.2 Cluster 1 - ORA Dotplot

10.3 Cluster 2 - ORA Dotplot

No enriched terms reached p.adjust < 0.25 for cluster 2

10.4 Cluster 3 - ORA Dotplot

10.5 Cluster 4 - ORA Dotplot

10.6 Cluster 5 - ORA Dotplot

10.7 Cluster 6 - ORA Dotplot

10.8 Cluster 7 - ORA Dotplot

10.9 Cluster 8 - ORA Dotplot

10.10 Cluster 9 - ORA Dotplot

No enriched terms reached p.adjust < 0.25 for cluster 9

10.11 Cluster 10 - ORA Dotplot

No enriched terms reached p.adjust < 0.25 for cluster 10

10.12 Cluster 11 - ORA Dotplot

10.13 Cluster 12 - ORA Dotplot

10.14 Cluster 13 - ORA Dotplot

11 Step 11. Table of Top Pathways per Cluster (Printed Individually)

for (cl in clusters_list) {
  cat("\n### Cluster", cl, "- Top Pathways (p.adjust < 0.05, fallback 0.25)\n\n")

  df <- all_long_ora %>%
    dplyr::filter(cluster == cl, p.adjust < 0.05) %>%
    dplyr::arrange(p.adjust)

  if (nrow(df) == 0) {
    df <- all_long_ora %>%
      dplyr::filter(cluster == cl, p.adjust < 0.25) %>%
      dplyr::arrange(p.adjust)
  }

  if (nrow(df) == 0) {
    cat("No enriched pathways found for this cluster.\n\n")
    next
  }

  df_show <- df %>%
    dplyr::slice_head(n = 10) %>%
    dplyr::select(database, Description, GeneRatio, p.adjust, Count, geneID) %>%
    dplyr::mutate(p.adjust = formatC(p.adjust, format = "e", digits = 2))

  print(knitr::kable(df_show, caption = paste0("Cluster ", cl, " top pathways")))
  cat("\n\n")
}

11.0.1 Cluster 0 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 0 top pathways
database Description GeneRatio p.adjust Count geneID
hsa05152 KEGG Tuberculosis 6/42 3.91e-02 6 6772/3117/9902/3123/972/3119
hsa05416 KEGG Viral myocarditis 5/42 3.91e-02 5 3117/1756/3123/958/3119

11.0.2 Cluster 1 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 1 top pathways
database Description GeneRatio p.adjust Count geneID
hsa04650 KEGG Natural killer cell mediated cytotoxicity 7/48 5.00e-02 7 3805/3821/3802/22914/3811/3804/5551

11.0.3 Cluster 2 - Top Pathways (p.adjust < 0.05, fallback 0.25)

No enriched pathways found for this cluster.

11.0.4 Cluster 3 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 3 top pathways
database Description GeneRatio p.adjust Count geneID
R-HSA-74160 Reactome Gene expression (Transcription) 12/48 9.02e-02 12 TCF7/TXNIP/LEF1/MAML2/BTG1/DYRK2/PHF1/FOXO1/SOCS3/ATM/ZNF101/SESN3
R-HSA-212436 Reactome Generic Transcription Pathway 11/48 9.02e-02 11 TCF7/TXNIP/LEF1/MAML2/BTG1/DYRK2/FOXO1/SOCS3/ATM/ZNF101/SESN3
R-HSA-73857 Reactome RNA Polymerase II Transcription 11/48 9.02e-02 11 TCF7/TXNIP/LEF1/MAML2/BTG1/DYRK2/FOXO1/SOCS3/ATM/ZNF101/SESN3
GO:0030217 GO_BP T cell differentiation 12/77 2.19e-01 12 TCF7/LEF1/IL6ST/TGFBR2/CD27/LY9/FOXP1/CD28/IL7R/SOCS3/ITPKB/ZFP36L1
GO:0030098 GO_BP lymphocyte differentiation 13/77 2.19e-01 13 TCF7/LEF1/IL6ST/TGFBR2/CD27/LY9/FOXP1/CD28/IL7R/SOCS3/ATM/ITPKB/ZFP36L1
GO:0019222 GO_BP regulation of metabolic process 46/77 2.19e-01 46 TMIGD2/ADTRP/BEX4/TCF7/LDLRAP1/ITGA6/NCF1/TXK/LBH/TXNIP/PLAC8/FHIT/BEX2/LEF1/IL6ST/UTY/TGFBR2/ABLIM1/SCML4/APBB1/APBA2/TRIM22/SATB1/PLCL1/LY9/MAML2/SNX9/BTG1/DYRK2/PNRC1/SFMBT2/BEX3/FOXP1/CD28/PHF1/FOXO1/TSHZ2/IL7R/ATM/SBNO2/ZNF101/IL16/ZFP36L1/CREBRF/SESN3/HLA-E
GO:0035264 GO_BP multicellular organism growth 6/77 2.19e-01 6 PLAC8/APBA2/SELENOM/PLEKHA1/ATM/ZFP36L1

11.0.5 Cluster 4 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 4 top pathways
database Description GeneRatio p.adjust Count geneID
HALLMARK_HEME_METABOLISM Hallmark HALLMARK_HEME_METABOLISM 5/38 9.75e-02 5 CLIC2/BLVRB/OSBP2/MPP1/GDE1

11.0.6 Cluster 5 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 5 top pathways
database Description GeneRatio p.adjust Count geneID
R-HSA-1266738 Reactome Developmental Biology 20/63 4.91e-02 20 NELL2/MAML2/FOXP1/ARHGEF28/ZNF521/SEMA4A/KRT1/DSC1/IL12RB2/CEBPD/BMP4/PKP2/KITLG/EPHA1/PTK2/EPHB1/CSF1/RUNX1/PRKCA/LRIG1

11.0.7 Cluster 6 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 6 top pathways
database Description GeneRatio p.adjust Count geneID
R-HSA-112316 Reactome Neuronal System 8/50 2.45e-01 8 GRIA4/GNB4/HOMER2/TUBB6/EPB41L2/MAOA/NEFL/NLGN1

11.0.8 Cluster 7 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 7 top pathways
database Description GeneRatio p.adjust Count geneID
GO:0007059 GO_BP chromosome segregation 41/92 1.08e-32 41 PSRC1/SGO2/NDC80/ASPM/KIF14/NEK2/DLGAP5/NUSAP1/TOP2A/GPSM2/CENPE/ECT2/KIFC1/CDCA2/HJURP/CENPF/CDK1/KIF23/MKI67/CCNB2/CDCA8/KIF4A/BUB1/RACGAP1/SGO1/AURKA/KNL1/FAM83D/BIRC5/UBE2C/NUF2/KIF15/PLK1/INCENP/TPX2/KIF2C/KIF22/KIF18A/SMC4/SPC24/KIF18B
GO:1903047 GO_BP mitotic cell cycle process 49/92 4.90e-29 49 PSRC1/CDKN2C/NDC80/KIF14/KIF20A/NEK2/DLGAP5/NUSAP1/GPSM2/CENPE/CENPA/ECT2/CDC25C/CCNA2/KIFC1/CDCA2/CENPF/STMN1/CDK1/KIF23/TK1/MKI67/CCNB2/CCNF/CDCA8/KIF4A/RRM2/BUB1/RACGAP1/AURKA/CDKN3/KNL1/BIRC5/UBE2C/NUF2/CIT/KIF15/PLK1/CDKN2A/CEP55/INCENP/TPX2/KIF2C/KIF22/KIF18A/SMC4/SPC24/KIF20B/KIF18B
GO:0098813 GO_BP nuclear chromosome segregation 36/92 2.56e-28 36 PSRC1/NDC80/ASPM/KIF14/NEK2/DLGAP5/NUSAP1/TOP2A/CENPE/ECT2/KIFC1/CENPF/CDK1/KIF23/CCNB2/CDCA8/KIF4A/BUB1/RACGAP1/SGO1/AURKA/KNL1/FAM83D/BIRC5/UBE2C/NUF2/KIF15/PLK1/INCENP/TPX2/KIF2C/KIF22/KIF18A/SMC4/SPC24/KIF18B
GO:0051301 GO_BP cell division 46/92 2.56e-28 46 PSRC1/SGO2/NDC80/ASPM/KIF14/KIF20A/NEK2/NUSAP1/TOP2A/GPSM2/CENPE/CENPA/ECT2/CDC25C/CCNA2/KIFC1/CDCA2/CENPF/STMN1/CDK1/KIF23/CDCA3/CCNB2/CCNF/CDCA8/KIF4A/BUB1/RACGAP1/SGO1/AURKA/KNL1/FAM83D/BIRC5/UBE2C/NUF2/CIT/PLK1/CDKN2A/CEP55/INCENP/TPX2/KIF2C/SMC4/SPC24/KIF20B/KIF18B
GO:0022402 GO_BP cell cycle process 55/92 2.56e-28 55 PSRC1/CDKN2C/SGO2/NDC80/ASPM/KIF14/KIF20A/NEK2/DLGAP5/NUSAP1/TOP2A/GPSM2/CENPE/CENPA/ECT2/CDC25C/CCNA2/KIFC1/CDCA2/HJURP/CENPF/STMN1/CDK1/KIF23/TK1/MKI67/CCNB2/CCNF/CDCA8/KIF4A/RRM2/BUB1/RACGAP1/SGO1/AURKA/CDKN3/KNL1/FAM83D/BIRC5/UBE2C/NUF2/CIT/KIF15/PLK1/CDKN2A/CEP55/INCENP/TPX2/KIF2C/KIF22/KIF18A/SMC4/SPC24/KIF20B/KIF18B
GO:0000278 GO_BP mitotic cell cycle 50/92 4.16e-28 50 PSRC1/CDKN2C/NDC80/KIF14/KIF20A/NEK2/DLGAP5/NUSAP1/GPSM2/CENPE/CENPA/ECT2/CDC25C/CCNA2/KIFC1/CDCA2/CENPF/STMN1/TUBA4A/CDK1/KIF23/TK1/MKI67/CCNB2/CCNF/CDCA8/KIF4A/RRM2/BUB1/RACGAP1/AURKA/CDKN3/KNL1/BIRC5/UBE2C/NUF2/CIT/KIF15/PLK1/CDKN2A/CEP55/INCENP/TPX2/KIF2C/KIF22/KIF18A/SMC4/SPC24/KIF20B/KIF18B
GO:0000280 GO_BP nuclear division 38/92 1.87e-27 38 PSRC1/NDC80/ASPM/KIF14/NEK2/DLGAP5/NUSAP1/TOP2A/CENPE/CDC25C/KIFC1/CDCA2/CENPF/CDK1/KIF23/MKI67/CCNB2/CDCA8/KIF4A/BUB1/RACGAP1/SGO1/AURKA/KNL1/BIRC5/UBE2C/NUF2/KIF15/PLK1/INCENP/TPX2/KIF2C/KIF22/KIF18A/SMC4/SPC24/KIF20B/KIF18B
HALLMARK_G2M_CHECKPOINT Hallmark HALLMARK_G2M_CHECKPOINT 33/58 2.87e-27 33 CDKN2C/NDC80/NEK2/NUSAP1/TOP2A/CENPE/CENPA/CCNA2/CENPF/STMN1/HMMR/CDK1/KIF23/MKI67/CCNB2/CCNF/KIF4A/KPNA2/BUB1/RACGAP1/AURKA/CDKN3/KNL1/BIRC5/UBE2C/KIF15/PLK1/INCENP/TPX2/KIF2C/KIF22/SMC4/KIF20B
GO:0007049 GO_BP cell cycle 58/92 7.80e-27 58 PSRC1/CDKN2C/SGO2/NDC80/ASPM/KIF14/KIF20A/NEK2/HPGD/DLGAP5/NUSAP1/TOP2A/GPSM2/CENPE/CENPA/ECT2/CDC25C/CCNA2/KIFC1/CDCA2/HJURP/CENPF/STMN1/TUBA4A/CDK1/KIF23/TK1/MKI67/CCNB2/CCNF/CDCA8/KIF4A/RRM2/BUB1/RACGAP1/SGO1/AURKA/CDKN3/KNL1/FAM83D/BIRC5/UBE2C/NUF2/PRR11/CIT/KIF15/PLK1/CDKN2A/CEP55/INCENP/TPX2/KIF2C/KIF22/KIF18A/SMC4/SPC24/KIF20B/KIF18B
GO:0048285 GO_BP organelle fission 38/92 3.65e-26 38 PSRC1/NDC80/ASPM/KIF14/NEK2/DLGAP5/NUSAP1/TOP2A/CENPE/CDC25C/KIFC1/CDCA2/CENPF/CDK1/KIF23/MKI67/CCNB2/CDCA8/KIF4A/BUB1/RACGAP1/SGO1/AURKA/KNL1/BIRC5/UBE2C/NUF2/KIF15/PLK1/INCENP/TPX2/KIF2C/KIF22/KIF18A/SMC4/SPC24/KIF20B/KIF18B

11.0.9 Cluster 8 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 8 top pathways
database Description GeneRatio p.adjust Count geneID
GO:0034220 GO_BP monoatomic ion transmembrane transport 13/84 1.25e-01 13 ATP7B/TMEM163/KCNQ2/KCND2/ATP12A/CACNA1D/CHRNA6/ORAI3/GRIA4/NCS1/KCNMA1/SLC26A4/CACNA2D1
R-HSA-9709957 Reactome Sensory Perception 5/50 1.34e-01 5 CACNA1D/LRP2/LRP12/KCNMA1/RDH10
GO:0006811 GO_BP monoatomic ion transport 15/84 1.40e-01 15 LRP2/ATP7B/TMEM163/KCNQ2/KCND2/ATP12A/CACNA1D/CHRNA6/ORAI3/GRIA4/NCS1/STC2/KCNMA1/SLC26A4/CACNA2D1

11.0.10 Cluster 9 - Top Pathways (p.adjust < 0.05, fallback 0.25)

No enriched pathways found for this cluster.

11.0.11 Cluster 10 - Top Pathways (p.adjust < 0.05, fallback 0.25)

No enriched pathways found for this cluster.

11.0.12 Cluster 11 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 11 top pathways
database Description GeneRatio p.adjust Count geneID
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION Hallmark HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION 11/60 5.68e-03 11 FUCA1/SAT1/IL32/TIMP1/FN1/SPOCK1/LGALS1/IGFBP3/BASP1/ID2/ITGB1
GO:0009416 GO_BP response to light stimulus 8/93 7.82e-03 8 CDKN1A/RGS9/NMU/TIMP1/BHLHE40/MDM2/ID2/ITGB1
R-HSA-1474228 Reactome Degradation of the extracellular matrix 6/72 2.67e-02 6 FN1/MMP25/ADAM8/TIMP1/CTSD/FURIN
GO:0019058 GO_BP viral life cycle 12/93 5.00e-02 12 APOBEC3G/SLAMF1/IL32/LGALS1/FURIN/GPR15/APOBEC3C/ITGB7/IFITM2/GBP2/ITGB1/IFITM3

11.0.13 Cluster 12 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 12 top pathways
database Description GeneRatio p.adjust Count geneID
HALLMARK_TNFA_SIGNALING_VIA_NFKB Hallmark HALLMARK_TNFA_SIGNALING_VIA_NFKB 19/49 3.02e-06 19 SERPINE1/CSF2/DUSP5/TNFSF9/DUSP4/RNF19B/PHLDA1/IER3/SDC4/PPP1R15A/ATF3/DUSP1/SQSTM1/TNF/TNFAIP8/CFLAR/TRIB1/PTGER4/STAT5A
GO:0008625 GO_BP extrinsic apoptotic signaling pathway via death domain receptors 8/86 2.28e-02 8 SERPINE1/LGALS3/ATF3/BAG3/TNF/DAPK1/CFLAR/TNFSF10
GO:2001237 GO_BP negative regulation of extrinsic apoptotic signaling pathway 8/86 2.28e-02 8 SERPINE1/CSF2/HSPA1B/HSPA1A/LGALS3/LMNA/TNF/CFLAR
GO:0097191 GO_BP extrinsic apoptotic signaling pathway 12/86 2.28e-02 12 SERPINE1/CSF2/HSPA1B/HSPA1A/LGALS3/ATF3/LMNA/BAG3/TNF/DAPK1/CFLAR/TNFSF10
GO:0042981 GO_BP regulation of apoptotic process 30/86 2.28e-02 30 SERPINE1/CSF2/CCL3/HSPA1B/CD40/HSPA1A/TNFSF9/PHLDA1/RGCC/IER3/LGALS3/GADD45G/PTGIS/SERPINB9/CTLA4/MEF2C/ATF3/CYP1B1/LMNA/DUSP1/SQSTM1/BAG3/TNF/DAPK1/TNFAIP8/CFLAR/TNFSF10/ARHGAP10/ZEB2/STAT5A
GO:2001236 GO_BP regulation of extrinsic apoptotic signaling pathway 10/86 2.41e-02 10 SERPINE1/CSF2/HSPA1B/HSPA1A/LGALS3/ATF3/LMNA/TNF/CFLAR/TNFSF10
GO:0043067 GO_BP regulation of programmed cell death 30/86 2.60e-02 30 SERPINE1/CSF2/CCL3/HSPA1B/CD40/HSPA1A/TNFSF9/PHLDA1/RGCC/IER3/LGALS3/GADD45G/PTGIS/SERPINB9/CTLA4/MEF2C/ATF3/CYP1B1/LMNA/DUSP1/SQSTM1/BAG3/TNF/DAPK1/TNFAIP8/CFLAR/TNFSF10/ARHGAP10/ZEB2/STAT5A
GO:2001234 GO_BP negative regulation of apoptotic signaling pathway 9/86 2.60e-02 9 SERPINE1/CSF2/HSPA1B/HSPA1A/IER3/LGALS3/LMNA/TNF/CFLAR
GO:0043065 GO_BP positive regulation of apoptotic process 17/86 3.23e-02 17 CCL3/CD40/PHLDA1/RGCC/GADD45G/PTGIS/CTLA4/MEF2C/ATF3/CYP1B1/DUSP1/SQSTM1/TNF/DAPK1/TNFAIP8/CFLAR/TNFSF10
GO:0006915 GO_BP apoptotic process 33/86 3.23e-02 33 SERPINE1/CSF2/GZMB/CCL3/HSPA1B/CD40/HSPA1A/TNFSF9/PHLDA1/RGCC/IER3/LGALS3/GADD45G/PTGIS/SERPINB9/PPP1R15A/CTLA4/MEF2C/RYR2/ATF3/CYP1B1/LMNA/DUSP1/SQSTM1/BAG3/TNF/DAPK1/TNFAIP8/CFLAR/TNFSF10/ARHGAP10/ZEB2/STAT5A

11.0.14 Cluster 13 - Top Pathways (p.adjust < 0.05, fallback 0.25)

Cluster 13 top pathways
database Description GeneRatio p.adjust Count geneID
HALLMARK_INTERFERON_ALPHA_RESPONSE Hallmark HALLMARK_INTERFERON_ALPHA_RESPONSE 18/55 7.80e-11 18 IFIT2/OASL/IFIT3/CXCL10/IFI44/DHX58/IFIH1/ISG15/DDX60/PARP14/HERC6/EPSTI1/USP18/ISG20/OAS1/WARS1/RTP4/TENT5A
HALLMARK_INTERFERON_GAMMA_RESPONSE Hallmark HALLMARK_INTERFERON_GAMMA_RESPONSE 23/55 5.03e-10 23 IFIT2/OASL/IFIT3/CXCL10/IFIT1/IFI44/DHX58/IFIH1/ISG15/CCL5/PELI1/DDX60/PARP14/HERC6/EPSTI1/OAS2/USP18/ISG20/WARS1/RTP4/TNFAIP3/APOL6/MT2A
GO:0051607 GO_BP defense response to virus 19/88 5.08e-08 19 IFIT2/OASL/IFIT3/CXCL10/IFIT1/HERC5/DHX58/PMAIP1/IFIH1/ISG15/ZC3HAV1/DDX60/AIM2/OAS2/USP18/ISG20/OAS1/RTP4/TNFAIP3
GO:0009607 GO_BP response to biotic stimulus 40/88 5.35e-08 40 IFIT2/OASL/IFIT3/CXCL10/IFIT1/HERC5/NCF2/ARG2/CCL3/IFI44/DHX58/PMAIP1/IFIH1/IL1A/ISG15/PLCG2/CCL5/PELI1/ZC3HAV1/DDX60/PARP14/AIM2/HERC6/OAS2/USP18/PRKCE/ISG20/RGS1/OAS1/CCR7/SPIRE1/RTP4/TENT5A/NFKBIZ/KYNU/LTA/ABI3/TNFAIP3/RNF19B/IL4I1
GO:0009615 GO_BP response to virus 21/88 8.87e-08 21 IFIT2/OASL/IFIT3/CXCL10/IFIT1/HERC5/IFI44/DHX58/PMAIP1/IFIH1/ISG15/CCL5/ZC3HAV1/DDX60/AIM2/OAS2/USP18/ISG20/OAS1/RTP4/TNFAIP3
GO:0045087 GO_BP innate immune response 29/88 1.55e-07 29 IFIT2/OASL/IFIT3/CXCL10/IFIT1/HERC5/NCF2/ARG2/CCL3/DHX58/IFIH1/ISG15/PLCG2/CCL5/PELI1/ZC3HAV1/DDX60/PARP14/AIM2/OAS2/USP18/PRKCE/ISG20/OAS1/SPIRE1/NFKBIZ/KYNU/TNFAIP3/RNF19B
GO:0043207 GO_BP response to external biotic stimulus 38/88 1.75e-07 38 IFIT2/OASL/IFIT3/CXCL10/IFIT1/HERC5/NCF2/ARG2/CCL3/IFI44/DHX58/PMAIP1/IFIH1/IL1A/ISG15/PLCG2/CCL5/PELI1/ZC3HAV1/DDX60/PARP14/AIM2/HERC6/OAS2/USP18/PRKCE/ISG20/RGS1/OAS1/CCR7/SPIRE1/RTP4/TENT5A/NFKBIZ/KYNU/LTA/TNFAIP3/RNF19B
GO:0051707 GO_BP response to other organism 38/88 1.75e-07 38 IFIT2/OASL/IFIT3/CXCL10/IFIT1/HERC5/NCF2/ARG2/CCL3/IFI44/DHX58/PMAIP1/IFIH1/IL1A/ISG15/PLCG2/CCL5/PELI1/ZC3HAV1/DDX60/PARP14/AIM2/HERC6/OAS2/USP18/PRKCE/ISG20/RGS1/OAS1/CCR7/SPIRE1/RTP4/TENT5A/NFKBIZ/KYNU/LTA/TNFAIP3/RNF19B
GO:0140546 GO_BP defense response to symbiont 29/88 5.99e-07 29 IFIT2/OASL/IFIT3/CXCL10/IFIT1/HERC5/NCF2/ARG2/CCL3/DHX58/IFIH1/ISG15/PLCG2/CCL5/PELI1/ZC3HAV1/DDX60/PARP14/AIM2/OAS2/USP18/PRKCE/ISG20/OAS1/SPIRE1/NFKBIZ/KYNU/TNFAIP3/RNF19B
GO:0098542 GO_BP defense response to other organism 29/88 6.77e-07 29 IFIT2/OASL/IFIT3/CXCL10/IFIT1/HERC5/NCF2/ARG2/CCL3/DHX58/IFIH1/ISG15/PLCG2/CCL5/PELI1/ZC3HAV1/DDX60/PARP14/AIM2/OAS2/USP18/PRKCE/ISG20/OAS1/SPIRE1/NFKBIZ/KYNU/TNFAIP3/RNF19B

NA

12 Step 12. Cross-Check: Proposed Name vs Top Enriched Terms

proposed_names <- c(
  "0" = "MHC-II high aberrant state", "1" = "NK-like cytotoxic", "2" = "Th2-like",
  "3" = "Naive/CD4 reference", "4" = "Migratory/Adhesion state", "5" = "Stem-like",
  "6" = "Th2-like (Activated)", "7" = "Cycling (G2/M)", "8" = "Metabolically reprogrammed",
  "9" = "GZMA-cytotoxic", "10" = "Central memory/CD4 reference", "11" = "Pro-inflammatory",
  "12" = "GZMB-high inflammatory", "13" = "IFN stimulated"
)

validation_table <- all_long_ora %>%
  dplyr::filter(p.adjust < 0.05) %>%
  dplyr::group_by(cluster) %>%
  dplyr::arrange(p.adjust) %>%
  dplyr::slice_head(n = 3) %>%
  dplyr::summarise(Top3_Terms = paste0(Description, " [", database, "] (p.adj=",
                                  formatC(p.adjust, format = "e", digits = 2), ")",
                                  collapse = "; "),
             .groups = "drop") %>%
  dplyr::mutate(Proposed_Name = proposed_names[as.character(cluster)]) %>%
  dplyr::select(cluster, Proposed_Name, Top3_Terms)

knitr::kable(validation_table, caption = "Proposed name vs. top ORA-enriched terms (top100 markers, all databases)")
Proposed name vs. top ORA-enriched terms (top100 markers, all databases)
cluster Proposed_Name Top3_Terms
0 MHC-II high aberrant state Tuberculosis [KEGG] (p.adj=3.91e-02); Viral myocarditis [KEGG] (p.adj=3.91e-02)
1 NK-like cytotoxic Natural killer cell mediated cytotoxicity [KEGG] (p.adj=5.00e-02)
11 Pro-inflammatory HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION [Hallmark] (p.adj=5.68e-03); response to light stimulus [GO_BP] (p.adj=7.82e-03); Degradation of the extracellular matrix [Reactome] (p.adj=2.67e-02)
12 GZMB-high inflammatory HALLMARK_TNFA_SIGNALING_VIA_NFKB [Hallmark] (p.adj=3.02e-06); extrinsic apoptotic signaling pathway via death domain receptors [GO_BP] (p.adj=2.28e-02); negative regulation of extrinsic apoptotic signaling pathway [GO_BP] (p.adj=2.28e-02)
13 IFN stimulated HALLMARK_INTERFERON_ALPHA_RESPONSE [Hallmark] (p.adj=7.80e-11); HALLMARK_INTERFERON_GAMMA_RESPONSE [Hallmark] (p.adj=5.03e-10); defense response to virus [GO_BP] (p.adj=5.08e-08)
5 Stem-like Developmental Biology [Reactome] (p.adj=4.91e-02)
7 Cycling (G2/M) chromosome segregation [GO_BP] (p.adj=1.08e-32); mitotic cell cycle process [GO_BP] (p.adj=4.90e-29); nuclear chromosome segregation [GO_BP] (p.adj=2.56e-28)

13 Step 13. Notes

  • This script uses over-representation analysis (ORA) – the standard method used in single-cell RNA-seq workflows to validate/name clusters from marker gene lists.
  • Background universe = all genes appearing anywhere in the marker table across all clusters.
  • Four databases tested per cluster: GO:BP (enrichGO), KEGG (enrichKEGG), Reactome (enrichPathway), Hallmark (enricher with msigdbr Hallmark sets).
  • Dotplots follow standard clusterProfiler::dotplot() convention: x-axis = GeneRatio, size = gene count, color = p.adjust.
  • Step 11 adds a readable results table per cluster (not just a plot), listing top pathway name, database, GeneRatio, p.adjust, gene count, and the actual overlapping gene IDs.
  • If a cluster shows “No enriched pathways found,” this is a genuine result – the top100 genes don’t strongly match any curated pathway/GO term, common for transitional or poorly characterized states.
---
title: "Over-Representation Analysis (ORA) on Top100 Marker Genes per Cluster"
subtitle: "GO:BP + KEGG + Reactome + Hallmark, with per-cluster dotplots"
author: "Nasir Mahmood Abbasi"
date: "`r format(Sys.time(), '%B %d, %Y')`"
output:
  html_notebook:
    number_sections: true
    toc: true
    toc_float:
      collapsed: true
    theme: journal
---



# Step 1. Load Libraries

```{r libraries}
library(readxl)
library(clusterProfiler)
library(org.Hs.eg.db)
library(ReactomePA)
library(enrichplot)
library(msigdbr)
library(openxlsx)
library(stringr)
library(purrr)
library(tibble)
library(tidyr)
library(ggplot2)
library(dplyr)   # load LAST so dplyr verbs win the masking conflict
```

# Step 2. Load Top100 Marker Table

```{r load-data}
markers <- read_excel("../Supplementary_Table_S6.xlsx") %>%
  rename_with(tolower) %>%
  mutate(cluster = as.character(cluster))

# Fix known outdated/renamed gene symbols
symbol_updates <- c("QARS" = "QARS1", "CARS" = "CARS1", "WARS" = "WARS1")
markers <- markers %>%
  mutate(gene = ifelse(gene %in% names(symbol_updates), symbol_updates[gene], gene)) %>%
  filter(gene != "46083.0")   # remove spreadsheet artifact

clusters_list <- as.character(sort(as.numeric(unique(markers$cluster))))
cat("Clusters found (numeric order):", paste(clusters_list, collapse = ", "), "\n")

print(colnames(markers))
```

# Step 3. Build Background Universe and Per-Cluster Gene Lists

```{r background-universe}
# Background = all genes that appear anywhere in the marker table (all clusters combined)
background_genes <- unique(markers$gene)
cat("Background universe size:", length(background_genes), "genes\n")

# Map SYMBOL -> ENTREZID (needed for enrichKEGG and ReactomePA)
gene_map <- bitr(background_genes, fromType = "SYMBOL", toType = "ENTREZID",
                  OrgDb = org.Hs.eg.db, drop = TRUE)

background_entrez <- unique(gene_map$ENTREZID)

get_cluster_genes <- function(cluster_id, marker_df, top_n = 100) {
  marker_df %>%
    filter(cluster == cluster_id) %>%
    distinct(gene, avg_log2fc) %>%
    arrange(desc(avg_log2fc)) %>%
    slice_head(n = top_n) %>%
    pull(gene)
}

cluster_gene_lists <- setNames(
  lapply(clusters_list, get_cluster_genes, marker_df = markers),
  clusters_list
)

sapply(cluster_gene_lists, length)
```

# Step 4. Load Hallmark Gene Sets (for enricher())

```{r hallmark-sets}
hallmark_sets <- msigdbr(species = "Homo sapiens", collection = "H") %>%
  distinct(gs_name, gene_symbol)
```

# Step 5. Run ORA for Each Cluster (GO:BP, KEGG, Reactome, Hallmark)

```{r run-ora}
all_results <- list()

for (cl in clusters_list) {
  message("Running ORA for cluster ", cl)

  genes_symbol <- cluster_gene_lists[[cl]]
  genes_entrez <- gene_map$ENTREZID[gene_map$SYMBOL %in% genes_symbol]

  res_list <- list()

  # GO:BP
  res_list$GO_BP <- tryCatch(
    enrichGO(gene = genes_symbol, universe = background_genes,
             OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
             ont = "BP", pAdjustMethod = "BH",
             pvalueCutoff = 1, qvalueCutoff = 1),
    error = function(e) { message("  GO:BP failed: ", e$message); NULL })

  # KEGG
  res_list$KEGG <- tryCatch(
    enrichKEGG(gene = genes_entrez, universe = background_entrez,
               organism = "hsa", pAdjustMethod = "BH",
               pvalueCutoff = 1, qvalueCutoff = 1),
    error = function(e) { message("  KEGG failed: ", e$message); NULL })

  # Reactome
  res_list$Reactome <- tryCatch(
    enrichPathway(gene = genes_entrez, universe = background_entrez,
                   organism = "human", pAdjustMethod = "BH",
                   pvalueCutoff = 1, qvalueCutoff = 1, readable = TRUE),
    error = function(e) { message("  Reactome failed: ", e$message); NULL })

  # Hallmark (via generic enricher)
  res_list$Hallmark <- tryCatch(
    enricher(gene = genes_symbol, universe = background_genes,
             TERM2GENE = hallmark_sets, pAdjustMethod = "BH",
             pvalueCutoff = 1, qvalueCutoff = 1),
    error = function(e) { message("  Hallmark failed: ", e$message); NULL })

  all_results[[cl]] <- res_list
}
```

# Step 6. Export All Enrichment Tables to Excel

```{r export-excel}
wb <- createWorkbook()

for (cl in names(all_results)) {
  res <- all_results[[cl]]
  if (is.null(res)) next

  for (ont in names(res)) {
    obj <- res[[ont]]
    if (is.null(obj) || nrow(as.data.frame(obj)) == 0) next

    sheet_name <- substr(paste0("C", cl, "_", ont), 1, 31)
    addWorksheet(wb, sheet_name)
    writeData(wb, sheet_name, as.data.frame(obj))
  }
}

saveWorkbook(wb, "Cluster_ORA_Results_Top100.xlsx", overwrite = TRUE)
```

# Step 7. Combine Top Terms per Cluster into One Long Table

```{r combine-long}
build_long_ora <- function(all_results) {
  purrr::map_dfr(names(all_results), function(cl) {
    res <- all_results[[cl]]
    purrr::map_dfr(names(res), function(db) {
      obj <- res[[db]]
      if (is.null(obj)) return(NULL)
      df <- as.data.frame(obj)
      if (nrow(df) == 0) return(NULL)
      df <- as.data.frame(df)   # strip any lingering S4/tibble subclass
      df$cluster <- cl
      df$database <- db
      dplyr::select(df, cluster, database, ID, Description, GeneRatio, BgRatio,
                     pvalue, p.adjust, qvalue, geneID, Count)
    })
  })
}

all_long_ora <- build_long_ora(all_results)
write.csv(all_long_ora, "Cluster_ORA_Results_long.csv", row.names = FALSE)

cat("Total enriched terms across all clusters/databases:", nrow(all_long_ora), "\n")
```

# Step 8. Check How Many Significant Terms per Cluster

```{r check-significance}
all_long_ora %>%
  group_by(cluster) %>%
  summarise(n_sig_padj05 = sum(p.adjust < 0.05, na.rm = TRUE),
            n_sig_padj25 = sum(p.adjust < 0.25, na.rm = TRUE),
            n_total_terms = n(),
            min_padj = suppressWarnings(min(p.adjust, na.rm = TRUE))) %>%
  arrange(as.numeric(as.character(cluster)))
```

# Step 9. Parse GeneRatio to Numeric (needed for dotplot x-axis)

```{r parse-generatio}
parse_ratio <- function(x) {
  parts <- str_split(x, "/", simplify = TRUE)
  as.numeric(parts[, 1]) / as.numeric(parts[, 2])
}

all_long_ora <- all_long_ora %>%
  mutate(GeneRatioNum = parse_ratio(GeneRatio))
```

# Step 10. Dotplot per Cluster (GeneRatio x, Count size, p.adjust color)

```{r dotplot-per-cluster, results='asis', fig.width=9, fig.height=6}
plot_cluster_ora_dotplot <- function(cluster_id, long_df, n_top = 10, sig_cutoff = 0.05) {

  df <- long_df %>%
    filter(cluster == cluster_id, p.adjust < sig_cutoff) %>%
    arrange(p.adjust) %>%
    slice_head(n = n_top)

  used_cutoff <- sig_cutoff

  # Fallback if nothing passes 0.05
  if (nrow(df) == 0) {
    df <- long_df %>%
      filter(cluster == cluster_id, p.adjust < 0.25) %>%
      arrange(p.adjust) %>%
      slice_head(n = n_top)
    used_cutoff <- 0.25
  }

  if (nrow(df) == 0) {
    cat("**No enriched terms reached p.adjust < 0.25 for cluster", cluster_id, "**\n\n")
    return(invisible(NULL))
  }

  df <- df %>%
    mutate(Description = str_wrap(Description, width = 40),
           label = paste0(Description, " [", database, "]"))

  p <- ggplot(df, aes(x = GeneRatioNum, y = reorder(label, GeneRatioNum),
                       size = Count, color = p.adjust)) +
    geom_point() +
    scale_color_gradient(low = "#B2182B", high = "#4393C3", name = "p.adjust") +
    scale_size_continuous(name = "Gene count", range = c(3, 9)) +
    labs(title = paste0("Cluster ", cluster_id, " - Top100 ORA (p.adjust<", used_cutoff, ")"),
         x = "Gene Ratio", y = NULL) +
    theme_minimal(base_size = 11) +
    theme(axis.text.y = element_text(size = 9))

  print(p)
  ggsave(filename = paste0("cluster", cluster_id, "_top100_ORA_dotplot.png"),
         plot = p, width = 9, height = 6, dpi = 300)
  cat("\n\n")
}

for (cl in clusters_list) {
  cat("\n## Cluster", cl, "- ORA Dotplot\n\n")
  plot_cluster_ora_dotplot(cl, all_long_ora)
}
```

# Step 11. Table of Top Pathways per Cluster (Printed Individually)

```{r pathways-table-per-cluster, results='asis'}
for (cl in clusters_list) {
  cat("\n### Cluster", cl, "- Top Pathways (p.adjust < 0.05, fallback 0.25)\n\n")

  df <- all_long_ora %>%
    dplyr::filter(cluster == cl, p.adjust < 0.05) %>%
    dplyr::arrange(p.adjust)

  if (nrow(df) == 0) {
    df <- all_long_ora %>%
      dplyr::filter(cluster == cl, p.adjust < 0.25) %>%
      dplyr::arrange(p.adjust)
  }

  if (nrow(df) == 0) {
    cat("No enriched pathways found for this cluster.\n\n")
    next
  }

  df_show <- df %>%
    dplyr::slice_head(n = 10) %>%
    dplyr::select(database, Description, GeneRatio, p.adjust, Count, geneID) %>%
    dplyr::mutate(p.adjust = formatC(p.adjust, format = "e", digits = 2))

  print(knitr::kable(df_show, caption = paste0("Cluster ", cl, " top pathways")))
  cat("\n\n")
}

```

# Step 12. Cross-Check: Proposed Name vs Top Enriched Terms

```{r annotation-check}
proposed_names <- c(
  "0" = "MHC-II high aberrant state", "1" = "NK-like cytotoxic", "2" = "Th2-like",
  "3" = "Naive/CD4 reference", "4" = "Migratory/Adhesion state", "5" = "Stem-like",
  "6" = "Th2-like (Activated)", "7" = "Cycling (G2/M)", "8" = "Metabolically reprogrammed",
  "9" = "GZMA-cytotoxic", "10" = "Central memory/CD4 reference", "11" = "Pro-inflammatory",
  "12" = "GZMB-high inflammatory", "13" = "IFN stimulated"
)

validation_table <- all_long_ora %>%
  dplyr::filter(p.adjust < 0.05) %>%
  dplyr::group_by(cluster) %>%
  dplyr::arrange(p.adjust) %>%
  dplyr::slice_head(n = 3) %>%
  dplyr::summarise(Top3_Terms = paste0(Description, " [", database, "] (p.adj=",
                                  formatC(p.adjust, format = "e", digits = 2), ")",
                                  collapse = "; "),
             .groups = "drop") %>%
  dplyr::mutate(Proposed_Name = proposed_names[as.character(cluster)]) %>%
  dplyr::select(cluster, Proposed_Name, Top3_Terms)

knitr::kable(validation_table, caption = "Proposed name vs. top ORA-enriched terms (top100 markers, all databases)")
```

# Step 13. Notes

- This script uses **over-representation analysis (ORA)** -- the standard method used in
  single-cell RNA-seq workflows to validate/name clusters from marker gene lists.
- Background universe = all genes appearing anywhere in the marker table across all clusters.
- Four databases tested per cluster: GO:BP (`enrichGO`), KEGG (`enrichKEGG`), Reactome
  (`enrichPathway`), Hallmark (`enricher` with msigdbr Hallmark sets).
- Dotplots follow standard `clusterProfiler::dotplot()` convention: x-axis = GeneRatio,
  size = gene count, color = p.adjust.
- Step 11 adds a readable results table per cluster (not just a plot), listing top pathway
  name, database, GeneRatio, p.adjust, gene count, and the actual overlapping gene IDs.
- If a cluster shows "No enriched pathways found," this is a genuine result -- the top100
  genes don't strongly match any curated pathway/GO term, common for transitional or
  poorly characterized states.

