Run scATOMIC-26-09-2025
Nasir Mahmood Abbasi
2025-09-25
load libraries————————————
1. Load SS seurat object
SS <- readRDS("../0-Seurat_RDS_OBJECT_FINAL/Seurat_object_Final_changes/All_samples_Merged_with_STCAT_Annotation_final-5-09-2025.rds")
# Subset SS for cell line L2
SS_L2 <- subset(SS, subset = cell_line == "L2")
rm(SS)
2. Running scATOMIC
library(reticulate)
use_virtualenv("/home/bioinfo/.virtualenvs/r-reticulate", required=TRUE)
py_config()python: /home/bioinfo/.virtualenvs/r-reticulate/bin/python
libpython: /usr/lib/python3.12/config-3.12-x86_64-linux-gnu/libpython3.12.so
pythonhome: /home/bioinfo/.virtualenvs/r-reticulate:/home/bioinfo/.virtualenvs/r-reticulate
version: 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0]
numpy: /home/bioinfo/.virtualenvs/r-reticulate/lib/python3.12/site-packages/numpy
numpy_version: 1.26.4
NOTE: Python version was forced by use_python() functionpy_module_available("magic")[1] TRUElibrary(scATOMIC)
library(plyr)
library(dplyr)
library(data.table)
library(randomForest)
library(caret)
library(parallel)
library(reticulate)
library(Rmagic)
library(Matrix)
library(Seurat)
library(agrmt)
library(cutoff.scATOMIC)
library(copykat)
library(ggplot2)
DefaultAssay(SS_L2) <- "RNA"
library(Matrix)
# Example: subsetting L2 but keeping all individual cells
sparse_matrix_L2 <- GetAssayData(SS_L2, assay = "RNA", slot = "counts")
# Remove genes with zero counts
nonzero_genes <- rowSums(sparse_matrix_L2) > 0
sparse_matrix_L2_filtered <- sparse_matrix_L2[nonzero_genes, ]
# Run scATOMIC
cell_predictions_L2 <- run_scATOMIC(sparse_matrix_L2_filtered)[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 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"# Inspect top predictions
head(cell_predictions_L2)$layer_1
$layer_2_non_blood
$layer_3_non_stromal
$layer_4_non_GI
$layer_5_breast_lung_prostate
$layer_2_bloodNARunning scATOMIC
SS <- create_summary_matrix(prediction_list = cell_predictions_L2, use_CNVs = F, modify_results = T, mc.cores = 1, raw_counts = sparse_matrix_L2, min_prop = 0.5 )
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|======================================================================================================| 100%[1] "Added MAGIC output to MAGIC_RNA. To use it, pass assay='MAGIC_RNA' to downstream methods or set seurat_object@active.assay <- 'MAGIC_RNA'."
[1] "Added MAGIC output to MAGIC_RNA. To use it, pass assay='MAGIC_RNA' to downstream methods or set seurat_object@active.assay <- 'MAGIC_RNA'."
[1] "Added MAGIC output to MAGIC_RNA. To use it, pass assay='MAGIC_RNA' to downstream methods or set seurat_object@active.assay <- 'MAGIC_RNA'."
[1] "Added MAGIC output to MAGIC_RNA. To use it, pass assay='MAGIC_RNA' to downstream methods or set seurat_object@active.assay <- 'MAGIC_RNA'."
[1] "Added MAGIC output to MAGIC_RNA. To use it, pass assay='MAGIC_RNA' to downstream methods or set seurat_object@active.assay <- 'MAGIC_RNA'."
[1] "Added MAGIC output to MAGIC_RNA. To use it, pass assay='MAGIC_RNA' to downstream methods or set seurat_object@active.assay <- 'MAGIC_RNA'."
[1] "Added MAGIC output to MAGIC_RNA. To use it, pass assay='MAGIC_RNA' to downstream methods or set seurat_object@active.assay <- 'MAGIC_RNA'."
[1] "Sample classification confidence = 0.33"table(SS$scATOMIC_pred)
Blood Cell CD4 or CD8 T cell CD8+ T cell
678 189 33
Effector/Memory CD4+ T cells Effector/Memory CD8+ T cells Lung Cancer Cell
3931 1051 4
Naive CD4+ T cells Normal Tissue Cell T regulatory cells
17 7 25 3. Visualizing Results
4. Visualizing Results non interactive
5. Adding Results to Seurat object
SS_L2 <- AddMetaData(SS_L2, SS)
6. plot our results
DimPlot(SS_L2, group.by = "scATOMIC_pred") + ggtitle("L2 from Cell lines") + labs(fill="scATOMIC Annotations") 
NA
NA7. Adding Results to Seurat object-All samples Merged
All_cell_Lines <- readRDS("../0-Seurat_RDS_OBJECT_FINAL/Seurat_object_Final_changes/All_samples_Merged_with_STCAT_Annotation_final-5-09-2025.rds")
All_cell_Lines <- AddMetaData(All_cell_Lines, SS)
8. plot our results
DimPlot(All_cell_Lines, group.by = "scATOMIC_pred") + ggtitle("L2 from Cell lines") + labs(fill="scATOMIC Annotations") 
NA
NAsessionInfo()
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