Run scATOMIC-04-02-2026-HerreraDATA
Nasir Mahmood Abbasi
2026-02-04
1 Load libraries
2 Load SS-Herrera seurat object
# Load full Seurat object
All_samples <- readRDS("/home/bioinfo/1-Thesis_Final_Year_2025/2025-Year3_Analysis/1-scRNA_RESULTS-19-11-2025/1-Biomarkers_Validation_with_Public_Data-August2025/Herrera_Data/Herrara_TCR_AB/Analysis/Sezary_Blood_Skin_vs_HC_TCR_filtered.rds")
DefaultAssay(All_samples) <- "RNA"
Idents(All_samples) <- "orig.ident"
3 Running scATOMIC and Loop through each cell group
# Define groups exactly as in metadata
cell_groups <- c("Healthy_Blood", "Healthy_Skin", "SS1_Blood", "SS1_Skin",
"SS2_Blood", "SS2_Skin", "SS3_Blood", "SS3_Skin",
"SS4_Blood", "SS4_Skin", "SS5_Blood", "SS6_Blood")
# Two containers: summaries + per-cell predictions
summaries_all <- list()
predictions_all <- list()
for (grp in cell_groups){
message("Processing group: ", grp)
tryCatch({
# Subset Seurat object
subset_obj <- subset(All_samples, idents = grp)
# Check that the subset has cells
if(ncol(subset_obj) == 0){
warning("No cells found for group: ", grp)
next
}
message(" - Found ", ncol(subset_obj), " cells")
# Get counts and filter zero-count genes (use 'layer' for v5 compatibility)
sparse_matrix <- GetAssayData(subset_obj, assay="RNA", layer="counts")
sparse_matrix_filtered <- sparse_matrix[rowSums(sparse_matrix) > 0, ]
message(" - Running scATOMIC...")
# Run scATOMIC
cell_predictions <- run_scATOMIC(sparse_matrix_filtered)
# Create summary matrix
summary_matrix <- create_summary_matrix(
prediction_list = cell_predictions,
use_CNVs = FALSE,
modify_results = TRUE,
mc.cores = 1,
raw_counts = sparse_matrix,
min_prop = 0.5
)
summaries_all[[grp]] <- summary_matrix
predictions_all[[grp]] <- summary_matrix
# Interactive tree (optional)
tree_results_Interactive <- scATOMICTree(
predictions_list = cell_predictions,
summary_matrix = summary_matrix,
interactive_mode = TRUE,
collapsed = TRUE,
save_results = FALSE,
height = 700,
width = 1000
)
print(tree_results_Interactive)
# Non-interactive tree (optional)
tree_results_Non_Interactive <- scATOMICTree(
predictions_list = cell_predictions,
summary_matrix = summary_matrix,
interactive_mode = FALSE,
collapsed = TRUE,
save_results = FALSE
)
print(tree_results_Non_Interactive)
message(" ✓ Completed successfully for ", grp)
}, error = function(e){
warning("✗ Error processing group ", grp, ": ", e$message)
})
# Clean up memory properly
rm(list = ls(pattern = "^(subset_obj|sparse_matrix|cell_predictions|summary_matrix|tree_results)"))
gc()
}[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 4 CD8 NK"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD8 NK"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
[1] "Starting Layer 3 Myeloid"
[1] "Done Layer 3 Myeloid"
[1] "Starting Layer 4 Dendritic"
[1] "nothing to score in this layer"
[1] "Done Layer 4 Dendritic"
[1] "Starting Layer 5 cDC"
[1] "nothing to score in this layer"
[1] "Done Layer 5 cDC"
[1] "Starting Layer 4 Macrophage Monocyte"
[1] "nothing to score in this layer"
[1] "Done Layer 4 Macrophage Monocyte"
[1] "Starting Layer 5 Monocyte"
[1] "nothing to score in this layer"
[1] "Done Layer 5 Monocyte"
[1] "Starting Layer 3 B Cell"
[1] "Done Layer 3 B Cell"
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|===============================================================================================| 100%[1] "Sample classification confidence = 1.00"[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Non Blood"
[1] "Done Layer 2 Non Blood"
[1] "Starting Layer 3 Non Stromal"
[1] "Done Layer 3 Non Stromal"
[1] "Starting Layer 4 Non GI"
[1] "Done Layer 4 Non GI"
[1] "Starting Layer 5 Breast Lung Prostate"
[1] "nothing to score in this layer"
[1] "Done Layer 5 Breast Lung Prostate"
[1] "Starting Layer 4 Soft Tissue Neuro"
[1] "Done Layer 4 Soft Tissue Neuro"
[1] "Starting Layer 3 Normal Stromal"
[1] "Done Layer 3 Normal Stromal"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 4 CD8 NK"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD8 NK"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
[1] "Starting Layer 3 Myeloid"
[1] "Done Layer 3 Myeloid"
[1] "Starting Layer 4 Dendritic"
[1] "nothing to score in this layer"
[1] "Done Layer 4 Dendritic"
[1] "Starting Layer 5 cDC"
[1] "nothing to score in this layer"
[1] "Done Layer 5 cDC"
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|===============================================================================================| 100%[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 4 CD8 NK"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD8 NK"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
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|===============================================================================================| 100%[1] "Sample classification confidence = 1.00"[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
|
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|===============================================================================================| 100%[1] "Sample classification confidence = 1.00"[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 4 CD8 NK"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD8 NK"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
[1] "Starting Layer 3 Myeloid"
[1] "Done Layer 3 Myeloid"
[1] "Starting Layer 4 Macrophage Monocyte"
[1] "nothing to score in this layer"
[1] "Done Layer 4 Macrophage Monocyte"
[1] "Starting Layer 5 Monocyte"
[1] "nothing to score in this layer"
[1] "Done Layer 5 Monocyte"
|
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|
|===============================================================================================| 100%[1] "Sample classification confidence = 1.00"[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 4 CD8 NK"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD8 NK"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
|
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|
|===============================================================================================| 100%[1] "Sample classification confidence = 1.00"[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 4 CD8 NK"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD8 NK"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
|
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|===============================================================================================| 100%[1] "Sample classification confidence = 0.96"[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
|
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|
|===============================================================================================| 100%[1] "Sample classification confidence = 0.87"[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 4 CD8 NK"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD8 NK"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
[1] "Starting Layer 3 Myeloid"
[1] "Done Layer 3 Myeloid"
[1] "Starting Layer 4 Dendritic"
[1] "nothing to score in this layer"
[1] "Done Layer 4 Dendritic"
[1] "Starting Layer 5 cDC"
[1] "nothing to score in this layer"
[1] "Done Layer 5 cDC"
[1] "Starting Layer 4 Macrophage Monocyte"
[1] "nothing to score in this layer"
[1] "Done Layer 4 Macrophage Monocyte"
[1] "Starting Layer 5 Monocyte"
[1] "nothing to score in this layer"
[1] "Done Layer 5 Monocyte"
|
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|
|===============================================================================================| 100%[1] "Sample classification confidence = 1.00"[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 4 CD8 NK"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD8 NK"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
|
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|
|===============================================================================================| 100%[1] "Sample classification confidence = 1.00"[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 4 CD8 NK"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD8 NK"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
[1] "Starting Layer 3 B Cell"
[1] "Done Layer 3 B Cell"
|
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|===============================================================================================| 100%[1] "Sample classification confidence = 1.00"[1] "Starting Layer 1"
[1] "Done Layer 1"
[1] "Starting Layer 2 Blood"
[1] "Done Layer 2 Blood"
[1] "Starting Layer 3 TNK"
[1] "Done Layer 3 TNK"
[1] "Starting Layer 4 CD4 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD4 CD8"
[1] "Starting Layer 4 CD8 NK"
[1] "nothing to score in this layer"
[1] "Done Layer 4 CD8 NK"
[1] "Starting Layer 5 CD4"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD4"
[1] "Starting Layer 5 CD8"
[1] "nothing to score in this layer"
[1] "Done Layer 5 CD8"
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|===============================================================================================| 100%[1] "Sample classification confidence = 1.00"# Verify results after loop
message("\n=== Summary ===")
message("Completed ", length(predictions_all), " out of ", length(cell_groups), " groups")
if(length(predictions_all) > 0){
message("Successful groups: ", paste(names(predictions_all), collapse=", "))
} else {
warning("No groups completed successfully!")
}
3.1 Adding Results to Seurat object
# Create empty vector for predictions
All_samples$scATOMIC_pred <- NA
# Loop through groups and assign predictions per cell
for(grp in names(predictions_all)){
preds <- predictions_all[[grp]]$scATOMIC_pred
cells <- rownames(predictions_all[[grp]])
# Assign predictions to Seurat object
All_samples$scATOMIC_pred[cells] <- preds
}
# Verify assignment
cat("Total cells:", ncol(All_samples), "\n")Total cells: 10785 cat("Cells with predictions:", sum(!is.na(All_samples$scATOMIC_pred)), "\n")Cells with predictions: 10752 cat("Cells without predictions:", sum(is.na(All_samples$scATOMIC_pred)), "\n\n")Cells without predictions: 33 # Check prediction distribution
print(table(All_samples$scATOMIC_pred, 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 3.2 plot our results
DimPlot(All_samples, group.by="sample_id", reduction = "umap")
DimPlot(All_samples, group.by="sample_id", reduction = "umap", label = T, repel = T)
DimPlot(All_samples, group.by="sample_id", reduction = "umap", label = T,label.box = T, repel = T)
DimPlot(All_samples, group.by="seurat_clusters", reduction = "umap", label = T,label.box = T, repel = T)
DimPlot(All_samples, group.by="scATOMIC_pred", reduction = "umap") +
ggtitle("scATOMIC Annotations for All Cell Lines and Controls") +
labs(fill="scATOMIC")
DimPlot(All_samples, group.by="scATOMIC_pred", reduction = "umap", label = T, repel = T) +
ggtitle("scATOMIC Annotations for All Cell Lines and Controls") +
labs(fill="scATOMIC")
3.3 plot our results
## Plot our results
library(ggplot2)
# Main UMAP plot
DimPlot(All_samples, group.by="scATOMIC_pred", reduction = "umap",
label = TRUE, repel = TRUE, label.size = 3) +
ggtitle("scATOMIC Annotations for All Samples") +
labs(fill="scATOMIC") +
theme(legend.position = "right")
# Split by sample_id
DimPlot(All_samples, group.by="scATOMIC_pred", reduction = "umap",
split.by = "sample_id", ncol = 4, label = FALSE) +
ggtitle("scATOMIC Annotations by Sample")
# Split by condition (if you have a condition column)
if("condition" %in% colnames(All_samples@meta.data)){
DimPlot(All_samples, group.by="scATOMIC_pred", reduction = "umap",
split.by = "condition", label = TRUE) +
ggtitle("scATOMIC Annotations: Healthy vs SS")
}
NA
NA
NA4 Table of predictions
# ========================
# 1. Heatmap: Clusters × Predictions
# ========================
library(pheatmap)
library(ggplot2)
# Cluster-level contingency table
table_clusters <- table(All_samples$seurat_clusters, All_samples$scATOMIC_pred)
prop_clusters <- prop.table(as.matrix(table_clusters), margin = 1)
# Heatmap
pheatmap(
prop_clusters,
cluster_rows = TRUE,
cluster_cols = TRUE,
color = colorRampPalette(c("white", "red"))(100),
main = "scATOMIC Predictions per Cluster"
)
# ========================
# 2. Barplot: Clusters × Predictions
# ========================
df_clusters <- as.data.frame(table(All_samples$seurat_clusters, All_samples$scATOMIC_pred))
colnames(df_clusters) <- c("Cluster", "Prediction", "Count")
ggplot(df_clusters, aes(x = Cluster, y = Count, fill = Prediction)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
ggtitle("scATOMIC Predictions by Cluster") +
theme_minimal()
# ========================
# 3. Heatmap: Cell lines × Predictions
# ========================
# Cell line-level contingency table
table_cellline <- table(All_samples$orig.ident, All_samples$scATOMIC_pred)
prop_cellline <- prop.table(as.matrix(table_cellline), margin = 1)
# Heatmap
pheatmap(
prop_cellline,
cluster_rows = TRUE,
cluster_cols = TRUE,
color = colorRampPalette(c("white", "red"))(100),
main = "scATOMIC Predictions per Samples"
)
# ========================
# 4. Barplot: Cell lines × Predictions
# ========================
df_cellline <- as.data.frame(table(All_samples$orig.ident, All_samples$scATOMIC_pred))
colnames(df_cellline) <- c("CellLine", "Prediction", "Count")
ggplot(df_cellline, aes(x = CellLine, y = Count, fill = Prediction)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
ggtitle("scATOMIC Predictions by Samples") +
theme_minimal()
4.1 Table of predictions
## Table of predictions
library(pheatmap)
library(ggplot2)
# ========================
# 1. By Seurat Clusters
# ========================
# Contingency table
table_clusters <- table(All_samples$seurat_clusters, All_samples$scATOMIC_pred)
prop_clusters <- prop.table(as.matrix(table_clusters), margin = 1)
# Heatmap
pheatmap(
prop_clusters,
cluster_rows = TRUE,
cluster_cols = TRUE,
color = colorRampPalette(c("white", "red"))(100),
main = "scATOMIC Predictions per Seurat Cluster",
fontsize = 8
)
# Barplot
df_clusters <- as.data.frame(table(All_samples$seurat_clusters, All_samples$scATOMIC_pred))
colnames(df_clusters) <- c("Cluster", "Prediction", "Count")
ggplot(df_clusters, aes(x = Cluster, y = Count, fill = Prediction)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
ggtitle("scATOMIC Predictions by Seurat Cluster") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
legend.position = "right")
# ========================
# 2. By Sample ID (Biological groups)
# ========================
# Contingency table
table_sample <- table(All_samples$sample_id, All_samples$scATOMIC_pred)
prop_sample <- prop.table(as.matrix(table_sample), margin = 1)
# Heatmap
pheatmap(
prop_sample,
cluster_rows = FALSE,
cluster_cols = TRUE,
color = colorRampPalette(c("white", "red"))(100),
main = "scATOMIC Predictions per Sample",
fontsize = 8
)
# Barplot
df_sample <- as.data.frame(table(All_samples$sample_id, All_samples$scATOMIC_pred))
colnames(df_sample) <- c("Sample", "Prediction", "Count")
# Order samples biologically
df_sample$Sample <- factor(df_sample$Sample,
levels = c("Healthy_Blood", "Healthy_Skin",
"SS1_Blood", "SS1_Skin",
"SS2_Blood", "SS2_Skin",
"SS3_Blood", "SS3_Skin",
"SS4_Blood", "SS4_Skin",
"SS5_Blood", "SS6_Blood"))
ggplot(df_sample, aes(x = Sample, y = Count, fill = Prediction)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
ggtitle("scATOMIC Predictions by Sample (Blood vs Skin)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
legend.position = "right")
# ========================
# 3. By Patient (orig.ident)
# ========================
# Contingency table
table_patient <- table(All_samples$orig.ident, All_samples$scATOMIC_pred)
prop_patient <- prop.table(as.matrix(table_patient), margin = 1)
# Heatmap
pheatmap(
prop_patient,
cluster_rows = FALSE,
cluster_cols = TRUE,
color = colorRampPalette(c("white", "red"))(100),
main = "scATOMIC Predictions per Patient",
fontsize = 10
)
# Barplot
df_patient <- as.data.frame(table(All_samples$orig.ident, All_samples$scATOMIC_pred))
colnames(df_patient) <- c("Patient", "Prediction", "Count")
ggplot(df_patient, aes(x = Patient, y = Count, fill = Prediction)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
ggtitle("scATOMIC Predictions by Patient") +
theme_minimal() +
theme(axis.text.x = element_text(size = 10),
legend.position = "right")
# ========================
# 4. By Tissue (Blood vs Skin)
# ========================
# Contingency table
table_tissue <- table(All_samples$tissue, All_samples$scATOMIC_pred)
prop_tissue <- prop.table(as.matrix(table_tissue), margin = 1)
# Heatmap
pheatmap(
prop_tissue,
cluster_rows = FALSE,
cluster_cols = TRUE,
color = colorRampPalette(c("white", "red"))(100),
main = "scATOMIC Predictions per Tissue Type",
fontsize = 10
)
# Barplot
df_tissue <- as.data.frame(table(All_samples$tissue, All_samples$scATOMIC_pred))
colnames(df_tissue) <- c("Tissue", "Prediction", "Count")
ggplot(df_tissue, aes(x = Tissue, y = Count, fill = Prediction)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
ggtitle("scATOMIC Predictions by Tissue Type") +
theme_minimal() +
theme(axis.text.x = element_text(size = 12),
legend.position = "right")
# ========================
# 5. By Condition (Healthy vs SS)
# ========================
# Contingency table
table_condition <- table(All_samples$condition, All_samples$scATOMIC_pred)
prop_condition <- prop.table(as.matrix(table_condition), margin = 1)
# Heatmap
pheatmap(
prop_condition,
cluster_rows = FALSE,
cluster_cols = TRUE,
color = colorRampPalette(c("white", "red"))(100),
main = "scATOMIC Predictions per Condition",
fontsize = 12
)
# Barplot
df_condition <- as.data.frame(table(All_samples$condition, All_samples$scATOMIC_pred))
colnames(df_condition) <- c("Condition", "Prediction", "Count")
ggplot(df_condition, aes(x = Condition, y = Count, fill = Prediction)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
ggtitle("scATOMIC Predictions: Healthy vs Sézary Syndrome") +
theme_minimal() +
theme(axis.text.x = element_text(size = 12),
legend.position = "right")
# ========================
# 6. Summary Tables
# ========================
cat("\n=== Sample × scATOMIC Prediction ===\n")
=== Sample × scATOMIC Prediction ===print(addmargins(table(All_samples$seurat_clusters, All_samples$scATOMIC_pred, useNA = "ifany")))
Any Cell B Cell Blood Cell CD14 Monocyte CD16 Monocyte CD4 or CD8 T cell CD4+ T cell
0 0 0 0 0 0 5 23
1 0 0 1 81 3 0 1
10 0 0 1 787 2 0 0
11 0 0 0 0 0 8 157
12 0 0 1 0 0 6 3
13 0 0 3 2 0 0 1
14 1 0 3 1 0 1 14
15 11 0 3 0 0 1 2
16 0 1 0 0 0 0 0
17 0 13 6 14 0 0 0
18 0 0 0 0 0 1 0
2 0 0 0 0 0 12 235
3 0 0 1 0 0 15 1
4 0 1 2 0 0 48 121
5 0 0 1 0 0 0 0
6 0 347 41 0 0 1 0
7 0 0 0 0 0 1 20
8 0 0 0 0 0 0 3
9 0 0 0 0 83 0 0
Sum 12 362 63 885 88 99 581
CD8 T or NK cell CD8+ T cell cDC1 cDC2 Dendritic Cell Effector/Memory CD4+ T cells
0 0 8 0 0 0 77
1 0 0 0 0 0 0
10 0 0 0 8 0 0
11 0 1 0 0 0 54
12 0 3 0 0 0 88
13 0 0 3 112 1 0
14 0 2 0 0 0 48
15 0 0 0 0 0 3
16 0 0 0 0 0 2
17 0 0 0 0 0 1
18 0 0 0 0 0 0
2 0 4 0 0 0 656
3 9 36 0 0 0 22
4 56 12 0 0 0 298
5 5 0 0 0 0 0
6 0 2 0 0 0 2
7 0 0 0 0 0 12
8 0 3 0 0 0 192
9 0 0 0 0 0 0
Sum 70 71 3 120 1 1455
Effector/Memory CD8+ T cells Exhausted CD8+ T cells HSPC Macrophage or Dendritic Cell MAIT cells
0 2 0 0 0 1
1 0 0 0 1 0
10 0 0 0 19 0
11 0 0 0 0 0
12 5 4 0 0 0
13 0 0 0 1 0
14 56 1 0 0 0
15 0 0 13 1 0
16 0 0 0 0 0
17 0 0 0 5 0
18 8 0 0 0 0
2 11 0 0 0 0
3 815 0 0 0 59
4 445 0 0 0 99
5 6 0 0 0 0
6 5 0 0 0 0
7 0 0 0 0 0
8 1 0 0 0 0
9 0 0 0 0 0
Sum 1354 5 13 27 159
Monocyte Naive CD4+ T cells Naive CD8+ T cells Natural killer cell pDC Plasmablast T or NK Cell
0 0 41 24 0 0 0 1
1 5 0 0 0 0 0 0
10 12 0 0 0 0 0 0
11 0 456 0 0 0 0 0
12 0 6 2 1 0 0 0
13 0 0 0 1 22 0 0
14 0 4 3 7 0 0 0
15 0 4 2 0 0 0 0
16 0 1 0 0 0 0 0
17 0 0 1 0 0 3 0
18 0 1 0 0 0 0 0
2 0 2147 3 5 0 0 0
3 0 0 20 15 0 0 0
4 0 452 651 89 0 0 2
5 0 0 0 208 0 0 0
6 0 1 2 2 0 0 0
7 0 196 0 0 0 0 0
8 0 683 37 0 0 0 0
9 1 0 0 0 0 0 0
Sum 18 3992 745 328 22 3 3
T regulatory cells Terminally differentiated effector CD4+ T cells Tfh/Th1 helper CD4+ T cells
0 2 0 0
1 0 0 0
10 0 0 0
11 49 0 2
12 3 0 1
13 0 0 0
14 1 0 41
15 0 0 0
16 0 0 0
17 0 0 0
18 0 0 0
2 9 0 159
3 0 1 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 1
8 0 0 4
9 0 0 0
Sum 64 1 208
<NA> Sum
0 1 185
1 0 92
10 0 829
11 0 727
12 1 124
13 4 150
14 0 183
15 8 48
16 0 4
17 1 44
18 0 10
2 0 3241
3 12 1006
4 2 2278
5 3 223
6 1 404
7 0 230
8 0 923
9 0 84
Sum 33 10785cat("\n=== Sample × scATOMIC Prediction ===\n")
=== Sample × scATOMIC Prediction ===print(addmargins(table(All_samples$sample_id, All_samples$scATOMIC_pred, useNA = "ifany")))
Any Cell B Cell Blood Cell CD14 Monocyte CD16 Monocyte CD4 or CD8 T cell CD4+ T cell
Healthy_Blood 3 354 43 809 68 56 149
Healthy_Skin 0 0 0 0 0 0 0
SS1_Blood 3 0 1 0 0 1 91
SS1_Skin 0 0 0 0 0 1 8
SS2_Blood 0 0 10 76 18 12 54
SS2_Skin 0 0 0 0 0 0 0
SS3_Blood 0 0 0 0 0 0 4
SS3_Skin 0 0 0 0 0 0 0
SS4_Blood 0 0 4 0 2 9 33
SS4_Skin 1 0 3 0 0 0 30
SS5_Blood 0 8 2 0 0 15 164
SS6_Blood 5 0 0 0 0 5 48
Sum 12 362 63 885 88 99 581
CD8 T or NK cell CD8+ T cell cDC1 cDC2 Dendritic Cell Effector/Memory CD4+ T cells
Healthy_Blood 61 25 3 117 1 356
Healthy_Skin 0 0 0 0 0 0
SS1_Blood 2 3 0 0 0 209
SS1_Skin 0 0 0 0 0 14
SS2_Blood 4 4 0 0 0 166
SS2_Skin 0 0 0 0 0 9
SS3_Blood 0 4 0 0 0 227
SS3_Skin 0 0 0 0 0 13
SS4_Blood 1 8 0 3 0 46
SS4_Skin 0 0 0 0 0 0
SS5_Blood 1 3 0 0 0 143
SS6_Blood 1 24 0 0 0 272
Sum 70 71 3 120 1 1455
Effector/Memory CD8+ T cells Exhausted CD8+ T cells HSPC Macrophage or Dendritic Cell
Healthy_Blood 674 0 13 27
Healthy_Skin 0 0 0 0
SS1_Blood 120 0 0 0
SS1_Skin 0 0 0 0
SS2_Blood 288 0 0 0
SS2_Skin 11 0 0 0
SS3_Blood 30 0 0 0
SS3_Skin 1 1 0 0
SS4_Blood 54 0 0 0
SS4_Skin 1 4 0 0
SS5_Blood 100 0 0 0
SS6_Blood 75 0 0 0
Sum 1354 5 13 27
MAIT cells Monocyte Naive CD4+ T cells Naive CD8+ T cells Natural killer cell pDC
Healthy_Blood 103 15 488 696 298 22
Healthy_Skin 0 0 0 0 0 0
SS1_Blood 2 0 1201 0 2 0
SS1_Skin 0 0 7 0 0 0
SS2_Blood 0 3 278 3 18 0
SS2_Skin 0 0 0 1 1 0
SS3_Blood 0 0 687 38 3 0
SS3_Skin 0 0 0 1 0 0
SS4_Blood 6 0 203 4 0 0
SS4_Skin 0 0 0 0 1 0
SS5_Blood 0 0 465 0 3 0
SS6_Blood 48 0 663 2 2 0
Sum 159 18 3992 745 328 22
Plasmablast T or NK Cell T regulatory cells
Healthy_Blood 0 3 2
Healthy_Skin 0 0 0
SS1_Blood 0 0 0
SS1_Skin 0 0 1
SS2_Blood 0 0 1
SS2_Skin 0 0 3
SS3_Blood 0 0 0
SS3_Skin 0 0 4
SS4_Blood 0 0 0
SS4_Skin 0 0 0
SS5_Blood 3 0 50
SS6_Blood 0 0 3
Sum 3 3 64
Terminally differentiated effector CD4+ T cells Tfh/Th1 helper CD4+ T cells <NA> Sum
Healthy_Blood 0 0 0 4386
Healthy_Skin 0 0 33 33
SS1_Blood 0 0 0 1635
SS1_Skin 0 27 0 58
SS2_Blood 0 0 0 935
SS2_Skin 0 13 0 38
SS3_Blood 1 3 0 997
SS3_Skin 0 3 0 23
SS4_Blood 0 0 0 373
SS4_Skin 0 144 0 184
SS5_Blood 0 2 0 959
SS6_Blood 0 16 0 1164
Sum 1 208 33 10785cat("\n=== Condition × scATOMIC Prediction ===\n")
=== Condition × scATOMIC Prediction ===print(addmargins(table(All_samples$condition, All_samples$scATOMIC_pred, useNA = "ifany")))
Any Cell B Cell Blood Cell CD14 Monocyte CD16 Monocyte CD4 or CD8 T cell CD4+ T cell
Healthy 3 354 43 809 68 56 149
SS 9 8 20 76 20 43 432
Sum 12 362 63 885 88 99 581
CD8 T or NK cell CD8+ T cell cDC1 cDC2 Dendritic Cell Effector/Memory CD4+ T cells
Healthy 61 25 3 117 1 356
SS 9 46 0 3 0 1099
Sum 70 71 3 120 1 1455
Effector/Memory CD8+ T cells Exhausted CD8+ T cells HSPC Macrophage or Dendritic Cell
Healthy 674 0 13 27
SS 680 5 0 0
Sum 1354 5 13 27
MAIT cells Monocyte Naive CD4+ T cells Naive CD8+ T cells Natural killer cell pDC
Healthy 103 15 488 696 298 22
SS 56 3 3504 49 30 0
Sum 159 18 3992 745 328 22
Plasmablast T or NK Cell T regulatory cells Terminally differentiated effector CD4+ T cells
Healthy 0 3 2 0
SS 3 0 62 1
Sum 3 3 64 1
Tfh/Th1 helper CD4+ T cells <NA> Sum
Healthy 0 33 4419
SS 208 0 6366
Sum 208 33 10785cat("\n=== Tissue × scATOMIC Prediction ===\n")
=== Tissue × scATOMIC Prediction ===print(addmargins(table(All_samples$tissue, All_samples$scATOMIC_pred, useNA = "ifany")))
Any Cell B Cell Blood Cell CD14 Monocyte CD16 Monocyte CD4 or CD8 T cell CD4+ T cell
Blood 11 362 60 885 88 98 543
Skin 1 0 3 0 0 1 38
Sum 12 362 63 885 88 99 581
CD8 T or NK cell CD8+ T cell cDC1 cDC2 Dendritic Cell Effector/Memory CD4+ T cells
Blood 70 71 3 120 1 1419
Skin 0 0 0 0 0 36
Sum 70 71 3 120 1 1455
Effector/Memory CD8+ T cells Exhausted CD8+ T cells HSPC Macrophage or Dendritic Cell
Blood 1341 0 13 27
Skin 13 5 0 0
Sum 1354 5 13 27
MAIT cells Monocyte Naive CD4+ T cells Naive CD8+ T cells Natural killer cell pDC Plasmablast
Blood 159 18 3985 743 326 22 3
Skin 0 0 7 2 2 0 0
Sum 159 18 3992 745 328 22 3
T or NK Cell T regulatory cells Terminally differentiated effector CD4+ T cells
Blood 3 56 1
Skin 0 8 0
Sum 3 64 1
Tfh/Th1 helper CD4+ T cells <NA> Sum
Blood 21 0 10449
Skin 187 33 336
Sum 208 33 10785cat("\n=== Patient × scATOMIC Prediction ===\n")
=== Patient × scATOMIC Prediction ===print(addmargins(table(All_samples$orig.ident, All_samples$scATOMIC_pred, useNA = "ifany")))
Any Cell B Cell Blood Cell CD14 Monocyte CD16 Monocyte CD4 or CD8 T cell CD4+ T cell
H19 3 354 43 809 68 56 149
H20 8 8 3 0 0 22 311
H21 0 0 10 76 18 12 54
H24 0 0 0 0 0 0 4
H25 1 0 7 0 2 9 63
Sum 12 362 63 885 88 99 581
CD8 T or NK cell CD8+ T cell cDC1 cDC2 Dendritic Cell Effector/Memory CD4+ T cells
H19 61 25 3 117 1 356
H20 4 30 0 0 0 638
H21 4 4 0 0 0 175
H24 0 4 0 0 0 240
H25 1 8 0 3 0 46
Sum 70 71 3 120 1 1455
Effector/Memory CD8+ T cells Exhausted CD8+ T cells HSPC Macrophage or Dendritic Cell MAIT cells
H19 674 0 13 27 103
H20 295 0 0 0 50
H21 299 0 0 0 0
H24 31 1 0 0 0
H25 55 4 0 0 6
Sum 1354 5 13 27 159
Monocyte Naive CD4+ T cells Naive CD8+ T cells Natural killer cell pDC Plasmablast T or NK Cell
H19 15 488 696 298 22 0 3
H20 0 2336 2 7 0 3 0
H21 3 278 4 19 0 0 0
H24 0 687 39 3 0 0 0
H25 0 203 4 1 0 0 0
Sum 18 3992 745 328 22 3 3
T regulatory cells Terminally differentiated effector CD4+ T cells Tfh/Th1 helper CD4+ T cells
H19 2 0 0
H20 54 0 45
H21 4 0 13
H24 4 1 6
H25 0 0 144
Sum 64 1 208
<NA> Sum
H19 33 4419
H20 0 3816
H21 0 973
H24 0 1020
H25 0 557
Sum 33 107855 Save RDS
# Save updated Seurat object
saveRDS(All_samples, file="All_samples_Merged_scATOMIC_Herrera_04-02-2026.rds")
# Also save predictions separately for easy access
scATOMIC_results <- list(
predictions_all = predictions_all,
summaries_all = summaries_all,
successful_groups = names(predictions_all),
failed_groups = setdiff(cell_groups, names(predictions_all))
)
saveRDS(scATOMIC_results, file="scATOMIC_results_Herrera_04-02-2026.rds")
cat("\nSaved successfully!\n")
Saved successfully!5.1 sessionInfo()
sessionInfo()
---
title: "Run scATOMIC-04-02-2026-HerreraDATA"
author: Nasir Mahmood Abbasi
date: "`r Sys.Date()`"
output:
  html_notebook:
    number_sections: true
    toc: true
    toc_float:
      collapsed: true
    theme: journal
---


# Load libraries
```{r setup, include=FALSE}
library(Seurat)
library(dplyr)
library(patchwork)
library(ggplot2)
library(pheatmap)
library(Matrix)
library(data.table)
library(plyr)
library(randomForest)
library(caret)
library(parallel)
library(reticulate)
library(Rmagic)
library(scATOMIC)
library(agrmt)
library(cutoff.scATOMIC)
library(copykat)
```

# Load SS-Herrera seurat object
```{r loadSeurat}

# Load full Seurat object
All_samples <- readRDS("/home/bioinfo/1-Thesis_Final_Year_2025/2025-Year3_Analysis/1-scRNA_RESULTS-19-11-2025/1-Biomarkers_Validation_with_Public_Data-August2025/Herrera_Data/Herrara_TCR_AB/Analysis/Sezary_Blood_Skin_vs_HC_TCR_filtered.rds")

DefaultAssay(All_samples) <- "RNA"

# Set identity to sample_id
Idents(All_samples) <- "sample_id"


```

# Running scATOMIC and Loop through each cell group
```{r}
# Define groups exactly as in metadata
cell_groups <- c("Healthy_Blood", "Healthy_Skin", "SS1_Blood", "SS1_Skin", 
                 "SS2_Blood", "SS2_Skin", "SS3_Blood", "SS3_Skin", 
                 "SS4_Blood", "SS4_Skin", "SS5_Blood", "SS6_Blood")

# Two containers: summaries + per-cell predictions
summaries_all    <- list()
predictions_all  <- list()

for (grp in cell_groups){
  
  message("Processing group: ", grp)
  
  tryCatch({
    
    # Subset Seurat object
    subset_obj <- subset(All_samples, idents = grp)
    
    # Check that the subset has cells
    if(ncol(subset_obj) == 0){
      warning("No cells found for group: ", grp)
      next
    }
    
    message("  - Found ", ncol(subset_obj), " cells")
    
    # Get counts and filter zero-count genes (use 'layer' for v5 compatibility)
    sparse_matrix <- GetAssayData(subset_obj, assay="RNA", layer="counts")
    sparse_matrix_filtered <- sparse_matrix[rowSums(sparse_matrix) > 0, ]
    
    message("  - Running scATOMIC...")
    # Run scATOMIC
    cell_predictions <- run_scATOMIC(sparse_matrix_filtered)
    
    # Create summary matrix
    summary_matrix <- create_summary_matrix(
      prediction_list = cell_predictions,
      use_CNVs = FALSE,
      modify_results = TRUE,
      mc.cores = 1,
      raw_counts = sparse_matrix,
      min_prop = 0.5
    )
    
    summaries_all[[grp]] <- summary_matrix
    predictions_all[[grp]] <- summary_matrix
    
    # Interactive tree (optional)
    tree_results_Interactive <- scATOMICTree(
      predictions_list = cell_predictions,
      summary_matrix = summary_matrix,
      interactive_mode = TRUE,
      collapsed = TRUE,
      save_results = FALSE,
      height = 700,
      width = 1000
    )
    print(tree_results_Interactive)
    
    # Non-interactive tree (optional)
    tree_results_Non_Interactive <- scATOMICTree(
      predictions_list = cell_predictions,
      summary_matrix = summary_matrix,
      interactive_mode = FALSE,
      collapsed = TRUE,
      save_results = FALSE
    )
    print(tree_results_Non_Interactive)
    
    message("  ✓ Completed successfully for ", grp)
    
  }, error = function(e){
    warning("✗ Error processing group ", grp, ": ", e$message)
  })
  
  # Clean up memory properly
  rm(list = ls(pattern = "^(subset_obj|sparse_matrix|cell_predictions|summary_matrix|tree_results)"))
  gc()
}

# Verify results after loop
message("\n=== Summary ===")
message("Completed ", length(predictions_all), " out of ", length(cell_groups), " groups")
if(length(predictions_all) > 0){
  message("Successful groups: ", paste(names(predictions_all), collapse=", "))
} else {
  warning("No groups completed successfully!")
}

```



## Adding Results to Seurat object
```{r, fig.height=12, fig.width= 16}

# Create empty vector for predictions
All_samples$scATOMIC_pred <- NA

# Loop through groups and assign predictions per cell
for(grp in names(predictions_all)){
  preds <- predictions_all[[grp]]$scATOMIC_pred
  cells <- rownames(predictions_all[[grp]])
  
  # Assign predictions to Seurat object
  All_samples$scATOMIC_pred[cells] <- preds
}

# Verify assignment
cat("Total cells:", ncol(All_samples), "\n")
cat("Cells with predictions:", sum(!is.na(All_samples$scATOMIC_pred)), "\n")
cat("Cells without predictions:", sum(is.na(All_samples$scATOMIC_pred)), "\n\n")

# Check prediction distribution
print(table(All_samples$scATOMIC_pred, useNA = "always"))
```

## plot our results
```{r, fig.height=12, fig.width= 16}

DimPlot(All_samples, group.by="sample_id", reduction = "umap")

DimPlot(All_samples, group.by="sample_id", reduction = "umap", label = T, repel = T)

DimPlot(All_samples, group.by="sample_id", reduction = "umap", label = T,label.box = T, repel = T)

DimPlot(All_samples, group.by="seurat_clusters", reduction = "umap", label = T,label.box = T, repel = T)

DimPlot(All_samples, group.by="scATOMIC_pred", reduction = "umap") + 
  ggtitle("scATOMIC Annotations for All Cell Lines and Controls") + 
  labs(fill="scATOMIC")
DimPlot(All_samples, group.by="scATOMIC_pred", reduction = "umap", label = T, repel = T) + 
  ggtitle("scATOMIC Annotations for All Cell Lines and Controls") + 
  labs(fill="scATOMIC")

```
## plot our results
```{r, fig.height=12, fig.width= 16}

## Plot our results
library(ggplot2)

# Main UMAP plot
DimPlot(All_samples, group.by="scATOMIC_pred", reduction = "umap", 
        label = TRUE, repel = TRUE, label.size = 3) + 
  ggtitle("scATOMIC Annotations for All Samples") + 
  labs(fill="scATOMIC") +
  theme(legend.position = "right")

# Split by sample_id
DimPlot(All_samples, group.by="scATOMIC_pred", reduction = "umap", 
        split.by = "sample_id", ncol = 4, label = FALSE) + 
  ggtitle("scATOMIC Annotations by Sample")

# Split by condition (if you have a condition column)
if("condition" %in% colnames(All_samples@meta.data)){
  DimPlot(All_samples, group.by="scATOMIC_pred", reduction = "umap", 
          split.by = "condition", label = TRUE) + 
    ggtitle("scATOMIC Annotations: Healthy vs SS")
}



```

# Table of predictions
```{r, fig.height=8, fig.width=12}

# ========================
# 1. Heatmap: Clusters × Predictions
# ========================

library(pheatmap)
library(ggplot2)

# Cluster-level contingency table
table_clusters <- table(All_samples$seurat_clusters, All_samples$scATOMIC_pred)
prop_clusters  <- prop.table(as.matrix(table_clusters), margin = 1)

# Heatmap
pheatmap(
  prop_clusters,
  cluster_rows = TRUE,
  cluster_cols = TRUE,
  color = colorRampPalette(c("white", "red"))(100),
  main = "scATOMIC Predictions per Cluster"
)

# ========================
# 2. Barplot: Clusters × Predictions
# ========================

df_clusters <- as.data.frame(table(All_samples$seurat_clusters, All_samples$scATOMIC_pred))
colnames(df_clusters) <- c("Cluster", "Prediction", "Count")

ggplot(df_clusters, aes(x = Cluster, y = Count, fill = Prediction)) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent) +
  ggtitle("scATOMIC Predictions by Cluster") +
  theme_minimal()

# ========================
# 3. Heatmap: Cell lines × Predictions
# ========================

# Cell line-level contingency table
table_cellline <- table(All_samples$orig.ident, All_samples$scATOMIC_pred)
prop_cellline  <- prop.table(as.matrix(table_cellline), margin = 1)

# Heatmap
pheatmap(
  prop_cellline,
  cluster_rows = TRUE,
  cluster_cols = TRUE,
  color = colorRampPalette(c("white", "red"))(100),
  main = "scATOMIC Predictions per Samples"
)

# ========================
# 4. Barplot: Cell lines × Predictions
# ========================

df_cellline <- as.data.frame(table(All_samples$orig.ident, All_samples$scATOMIC_pred))
colnames(df_cellline) <- c("CellLine", "Prediction", "Count")

ggplot(df_cellline, aes(x = CellLine, y = Count, fill = Prediction)) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent) +
  ggtitle("scATOMIC Predictions by Samples") +
  theme_minimal()

```

## Table of predictions
```{r, fig.height=8, fig.width=12}

## Table of predictions
library(pheatmap)
library(ggplot2)

# ========================
# 1. By Seurat Clusters
# ========================

# Contingency table
table_clusters <- table(All_samples$seurat_clusters, All_samples$scATOMIC_pred)
prop_clusters  <- prop.table(as.matrix(table_clusters), margin = 1)

# Heatmap
pheatmap(
  prop_clusters,
  cluster_rows = TRUE,
  cluster_cols = TRUE,
  color = colorRampPalette(c("white", "red"))(100),
  main = "scATOMIC Predictions per Seurat Cluster",
  fontsize = 8
)

# Barplot
df_clusters <- as.data.frame(table(All_samples$seurat_clusters, All_samples$scATOMIC_pred))
colnames(df_clusters) <- c("Cluster", "Prediction", "Count")

ggplot(df_clusters, aes(x = Cluster, y = Count, fill = Prediction)) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent) +
  ggtitle("scATOMIC Predictions by Seurat Cluster") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        legend.position = "right")

# ========================
# 2. By Sample ID (Biological groups)
# ========================

# Contingency table
table_sample <- table(All_samples$sample_id, All_samples$scATOMIC_pred)
prop_sample  <- prop.table(as.matrix(table_sample), margin = 1)

# Heatmap
pheatmap(
  prop_sample,
  cluster_rows = FALSE,
  cluster_cols = TRUE,
  color = colorRampPalette(c("white", "red"))(100),
  main = "scATOMIC Predictions per Sample",
  fontsize = 8
)

# Barplot
df_sample <- as.data.frame(table(All_samples$sample_id, All_samples$scATOMIC_pred))
colnames(df_sample) <- c("Sample", "Prediction", "Count")

# Order samples biologically
df_sample$Sample <- factor(df_sample$Sample, 
                           levels = c("Healthy_Blood", "Healthy_Skin",
                                     "SS1_Blood", "SS1_Skin",
                                     "SS2_Blood", "SS2_Skin",
                                     "SS3_Blood", "SS3_Skin",
                                     "SS4_Blood", "SS4_Skin",
                                     "SS5_Blood", "SS6_Blood"))

ggplot(df_sample, aes(x = Sample, y = Count, fill = Prediction)) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent) +
  ggtitle("scATOMIC Predictions by Sample (Blood vs Skin)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        legend.position = "right")

# ========================
# 3. By Patient (orig.ident)
# ========================

# Contingency table
table_patient <- table(All_samples$orig.ident, All_samples$scATOMIC_pred)
prop_patient  <- prop.table(as.matrix(table_patient), margin = 1)

# Heatmap
pheatmap(
  prop_patient,
  cluster_rows = FALSE,
  cluster_cols = TRUE,
  color = colorRampPalette(c("white", "red"))(100),
  main = "scATOMIC Predictions per Patient",
  fontsize = 10
)

# Barplot
df_patient <- as.data.frame(table(All_samples$orig.ident, All_samples$scATOMIC_pred))
colnames(df_patient) <- c("Patient", "Prediction", "Count")

ggplot(df_patient, aes(x = Patient, y = Count, fill = Prediction)) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent) +
  ggtitle("scATOMIC Predictions by Patient") +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 10),
        legend.position = "right")

# ========================
# 4. By Tissue (Blood vs Skin)
# ========================

# Contingency table
table_tissue <- table(All_samples$tissue, All_samples$scATOMIC_pred)
prop_tissue  <- prop.table(as.matrix(table_tissue), margin = 1)

# Heatmap
pheatmap(
  prop_tissue,
  cluster_rows = FALSE,
  cluster_cols = TRUE,
  color = colorRampPalette(c("white", "red"))(100),
  main = "scATOMIC Predictions per Tissue Type",
  fontsize = 10
)

# Barplot
df_tissue <- as.data.frame(table(All_samples$tissue, All_samples$scATOMIC_pred))
colnames(df_tissue) <- c("Tissue", "Prediction", "Count")

ggplot(df_tissue, aes(x = Tissue, y = Count, fill = Prediction)) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent) +
  ggtitle("scATOMIC Predictions by Tissue Type") +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12),
        legend.position = "right")

# ========================
# 5. By Condition (Healthy vs SS)
# ========================

# Contingency table
table_condition <- table(All_samples$condition, All_samples$scATOMIC_pred)
prop_condition  <- prop.table(as.matrix(table_condition), margin = 1)

# Heatmap
pheatmap(
  prop_condition,
  cluster_rows = FALSE,
  cluster_cols = TRUE,
  color = colorRampPalette(c("white", "red"))(100),
  main = "scATOMIC Predictions per Condition",
  fontsize = 12
)

# Barplot
df_condition <- as.data.frame(table(All_samples$condition, All_samples$scATOMIC_pred))
colnames(df_condition) <- c("Condition", "Prediction", "Count")

ggplot(df_condition, aes(x = Condition, y = Count, fill = Prediction)) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent) +
  ggtitle("scATOMIC Predictions: Healthy vs Sézary Syndrome") +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12),
        legend.position = "right")

# ========================
# 6. Summary Tables
# ========================
cat("\n=== Sample × scATOMIC Prediction ===\n")
print(addmargins(table(All_samples$seurat_clusters, All_samples$scATOMIC_pred, useNA = "ifany")))

cat("\n=== Sample × scATOMIC Prediction ===\n")
print(addmargins(table(All_samples$sample_id, All_samples$scATOMIC_pred, useNA = "ifany")))

cat("\n=== Condition × scATOMIC Prediction ===\n")
print(addmargins(table(All_samples$condition, All_samples$scATOMIC_pred, useNA = "ifany")))

cat("\n=== Tissue × scATOMIC Prediction ===\n")
print(addmargins(table(All_samples$tissue, All_samples$scATOMIC_pred, useNA = "ifany")))

cat("\n=== Patient × scATOMIC Prediction ===\n")
print(addmargins(table(All_samples$orig.ident, All_samples$scATOMIC_pred, useNA = "ifany")))

```

# Save RDS
```{r}

# Save updated Seurat object
saveRDS(All_samples, file="All_samples_Merged_scATOMIC_Herrera_04-02-2026.rds")

# Also save predictions separately for easy access
scATOMIC_results <- list(
  predictions_all = predictions_all,
  summaries_all = summaries_all,
  successful_groups = names(predictions_all),
  failed_groups = setdiff(cell_groups, names(predictions_all))
)

saveRDS(scATOMIC_results, file="scATOMIC_results_Herrera_04-02-2026.rds")

cat("\nSaved successfully!\n")



```

## sessionInfo()
```{r}

sessionInfo()


```