Identify Top5 Markers and do Cluster Annotation

1. load libraries

2. Load Seurat Object

All_samples_Merged <- readRDS("../0-Seurat_RDS_OBJECT_FINAL/Seurat_object_Final_changes/All_samples_Merged_with_STCAT_Annotation_final-5-09-2025.rds")

DefaultAssay(All_samples_Merged) <- "RNA"

All_samples_Merged <- NormalizeData(
    All_samples_Merged,
    normalization.method = "LogNormalize",
    scale.factor = 1e4,
    verbose = TRUE
  )

Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

3.find Top markers

Idents(All_samples_Merged) <- "seurat_clusters"

# ---------------------------------------------------------
# 2️⃣ Find marker genes per cluster
SS_markers <- FindAllMarkers(
  All_samples_Merged,
  only.pos = TRUE,
  min.pct = 0.25,
  logfc.threshold = 0.25,
  min.pct.diff = 0.2
  
)

library(dplyr)

# Precise blacklist for uninformative genes
blacklist_patterns <- c(
  "^TRAV", "^TRBV", "^TRGV", "^TRDV", "^TRBC", "^TRAC", "^TRDC", "^TRGC", # TCR
  "^IGH", "^IGK", "^IGL", "^IGJ",                                         # Ig genes
  "^RPL", "^RPS",                                                         # ribosomal
  "^MT-",                                                                 # mitochondria
  "^HBA", "^HBB", "^HB[ABZ]",                                             # hemoglobins
  "^NEAT1$", "^MALAT1$",                                                  # optional lncRNAs
  "^XIST$"                              )

blacklist_regex <- paste(blacklist_patterns, collapse = "|")

# Preview which markers will be removed
to_remove <- SS_markers %>%
  filter(grepl(blacklist_regex, gene, ignore.case = TRUE))
message("Rows to remove: ", nrow(to_remove))
head(to_remove$gene)

[1] "TRAV17"  "TRAV9-2" "RPL22L1" "RPL7"    "RPL35A"  "NEAT1"  

# Filter markers (keep important metabolic/proliferation genes)
SS_markers_filtered <- SS_markers %>%
  filter(!grepl(blacklist_regex, gene, ignore.case = TRUE))

4. Top5 Markers

library(dplyr)

# ---------------------------------------------------------
# Save filtered markers
write.csv(SS_markers_filtered, file = "SS_markers_filtered.csv", row.names = FALSE)

# ---------------------------------------------------------
# Extract top 25 markers per cluster
top25_markers <- SS_markers_filtered %>%
  filter(p_val_adj < 0.05) %>%  # ensure statistical significance
  group_by(cluster) %>%
  slice_max(order_by = avg_log2FC, n = 25) %>%
  ungroup()

write.csv(top25_markers, file = "SS_markers_top25.csv", row.names = FALSE)

# ---------------------------------------------------------
# Extract top 5 markers per cluster
top5_markers <- SS_markers_filtered %>%
  filter(p_val_adj < 0.05) %>%  # ensure statistical significance
  group_by(cluster) %>%
  slice_max(order_by = avg_log2FC, n = 5) %>%
  ungroup()

write.csv(top5_markers, file = "SS_markers_top5.csv", row.names = FALSE)

message("Filtered markers, top25, and top5 markers saved successfully.")

nbiSweden Approach-Top25

DefaultAssay(All_samples_Merged) <- "SCT"

par(mfrow = c(2, 5), mar = c(4, 6, 3, 1))
for (i in unique(top25_markers$cluster)) {
    barplot(sort(setNames(top25_markers$avg_log2FC, top25_markers$gene)[top25_markers$cluster == i], F),
        horiz = T, las = 1, main = paste0(i, " vs. rest"), border = "white", yaxs = "i"
    )
    abline(v = c(0, 0.25), lty = c(1, 2))
}

NA

nbiSweden Approach-Top5

par(mfrow = c(2, 5), mar = c(4, 6, 3, 1))
for (i in unique(top5_markers$cluster)) {
    barplot(sort(setNames(top5_markers$avg_log2FC, top5_markers$gene)[top5_markers$cluster == i], F),
        horiz = T, las = 1, main = paste0(i, " vs. rest"), border = "white", yaxs = "i"
    )
    abline(v = c(0, 0.25), lty = c(1, 2))
}

NA

Chatomics approach

nbiSweden Approach

DotPlot(All_samples_Merged, features = rev(as.character(unique(top5_markers$gene))), group.by = "seurat_clusters", assay = "RNA") + coord_flip()

scCustomize Dotplot

library(scCustomize)
Clustered_DotPlot(seurat_object = All_samples_Merged, features = top5_markers$gene, k = 6)

[[1]]

[[2]]

scCustomize::Clustered_DotPlot(All_samples_Merged, features = top5_markers$gene,
                               plot_km_elbow = FALSE)