3-Transfer scATOMIC and Cross-Method Annotation Comparison Table - Herrera Dataset

0.1 load libraries————————————

1 Load RDS with all annotations

# Load main object with all annotations
All_samples_Merged <- readRDS("/home/bioinfo/1-Thesis_Final_Year_2025/2025-Year3_Analysis/1-scRNA_RESULTS-19-11-2025/17-Herrera_DATA_CELL_Annotation/All_samples_Herrera_with_all_annotations_ATLAS-Herrera-03-02-2026.rds")

# Load scATOMIC object
scATOMIC_obj <- readRDS("/home/bioinfo/1-Thesis_Final_Year_2025/2025-Year3_Analysis/1-scRNA_RESULTS-19-11-2025/17-Herrera_DATA_CELL_Annotation/All_samples_Herrera_scATOMIC_Herrera_04-02-2026.rds")

Idents(All_samples_Merged) <- "seurat_clusters"
cat("Clusters in All_samples_Merged:\n")

Clusters in All_samples_Merged:

print(table(All_samples_Merged@active.ident))

   2    3    0   12    6   17    5    4    1   15   14   18    9   13    7    8   11   16   10 
3241 1006  185  124  404   44  223 2278   92   48  183   10   84  150  230  923  727    4  829 

1.1 Check metadata columns

cat("\nscATOMIC_obj metadata columns:\n")

scATOMIC_obj metadata columns:

print(colnames(scATOMIC_obj@meta.data))

 [1] "orig.ident"      "nCount_RNA"      "nFeature_RNA"    "sample_id"       "condition"       "tissue"         
 [7] "percent.mt"      "RNA_snn_res.0.5" "seurat_clusters" "RNA_snn_res.0.2" "DE_group"        "clonotype"      
[13] "TCR_alpha"       "TCR_beta"        "tissue_group"    "scATOMIC_pred"  

1.2 Check metadata columns

## Transfer scATOMIC labels to main object

# Transfer scATOMIC predictions
All_samples_Merged$scATOMIC_annotation <- scATOMIC_obj@meta.data[colnames(All_samples_Merged), "scATOMIC_pred"]

# Verify transfer
cat("\nTransferred scATOMIC annotations:\n")

Transferred scATOMIC annotations:

print(table(All_samples_Merged$scATOMIC_annotation, useNA = "always"))

                                       Any Cell                                          B Cell 
                                             12                                             362 
                                     Blood Cell                                   CD14 Monocyte 
                                             63                                             885 
                                  CD16 Monocyte                               CD4 or CD8 T cell 
                                             88                                              99 
                                    CD4+ T cell                                CD8 T or NK cell 
                                            581                                              70 
                                    CD8+ T cell                                            cDC1 
                                             71                                               3 
                                           cDC2                                  Dendritic Cell 
                                            120                                               1 
                   Effector/Memory CD4+ T cells                    Effector/Memory CD8+ T cells 
                                           1455                                            1354 
                         Exhausted CD8+ T cells                                            HSPC 
                                              5                                              13 
                   Macrophage or Dendritic Cell                                      MAIT cells 
                                             27                                             159 
                                       Monocyte                              Naive CD4+ T cells 
                                             18                                            3992 
                             Naive CD8+ T cells                             Natural killer cell 
                                            745                                             328 
                                            pDC                                     Plasmablast 
                                             22                                               3 
                                   T or NK Cell                              T regulatory cells 
                                              3                                              64 
Terminally differentiated effector CD4+ T cells                     Tfh/Th1 helper CD4+ T cells 
                                              1                                             208 
                                           <NA> 
                                             33 

# Check coverage
cat("\nCells with scATOMIC annotation:", sum(!is.na(All_samples_Merged$scATOMIC_annotation)), "\n")

Cells with scATOMIC annotation: 10752 

cat("Total cells:", ncol(All_samples_Merged), "\n")

Total cells: 10785 

cat("Coverage:", round(sum(!is.na(All_samples_Merged$scATOMIC_annotation))/ncol(All_samples_Merged)*100, 2), "%\n")

Coverage: 99.69 %

# Verify the column was added
cat("\nNew column added to metadata:\n")

New column added to metadata:

cat("scATOMIC_annotation:", "scATOMIC_annotation" %in% colnames(All_samples_Merged@meta.data), "\n")

scATOMIC_annotation: TRUE 

2 Create annotation summary table

library(dplyr)
library(tidyr)
library(purrr)

# Define annotation methods (removed STCAT)
methods <- c(
  "predicted.id",             # scPred
  "predicted.celltype.l2",    # Azimuth (l2 prediction)
  "singler.immune",           # SingleR
  "scATOMIC_annotation"       # scATOMIC
)

# Create summary - most common label per cluster for each method
annotation_summary <- map_dfr(methods, function(m) {
  df <- All_samples_Merged@meta.data
  df %>%
    filter(!is.na(.data[[m]])) %>%
    group_by(seurat_clusters, label = .data[[m]]) %>%
    summarise(n = n(), .groups = "drop") %>%
    group_by(seurat_clusters) %>%
    slice_max(n, n = 1, with_ties = FALSE) %>%
    mutate(method = m)
})

# Set method order
annotation_summary$method <- factor(annotation_summary$method, levels = methods)

head(annotation_summary)

# Save annotation summary table
#write.csv(annotation_summary, "annotation_summary_Herrera_with_scATOMIC_04-02-2026.csv", row.names = FALSE)

3 Save RDS

## Save updated object with scATOMIC annotations

# saveRDS(All_samples_Merged, 
#         file = "/home/bioinfo/1-Thesis_Final_Year_2025/2025-Year3_Analysis/1-scRNA_RESULTS-19-11-2025/17-Herrera_DATA_CELL_Annotation/All_samples_Herrera_with_scATOMIC_04-02-2026.rds")

cat("\n✓ Saved updated object with scATOMIC annotations!\n")

✓ Saved updated object with scATOMIC annotations!

cat("File: All_samples_Herrera_with_scATOMIC_04-02-2026.rds\n")

File: All_samples_Herrera_with_scATOMIC_04-02-2026.rds

cat("Location: 17-Herrera_DATA_CELL_Annotation/\n")

Location: 17-Herrera_DATA_CELL_Annotation/

cat("Ready for Cross-Method Annotation Comparison!\n")

Ready for Cross-Method Annotation Comparison!