Tables of celltypes based on cell_lines
Nasir Mahmood Abbasi
2024-12-13
1. load libraries
#Differential Expression Analysis # 2. load seurat object
#Load Seurat Object L7
load("../../../../0-IMP-OBJECTS/Harmony_integrated_All_samples_Merged_with_PBMC10x_with_harmony_clustering.Robj")
All_samples_MergedAn object of class Seurat
64169 features across 59355 samples within 6 assays
Active assay: SCT (27417 features, 3000 variable features)
3 layers present: counts, data, scale.data
5 other assays present: RNA, ADT, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3
6 dimensional reductions calculated: integrated_dr, ref.umap, pca, umap, harmony, umap.harmonyDimPlot(All_samples_Merged, reduction = "umap.harmony", group.by = "cell_line",label = T, label.box = T)
DimPlot(All_samples_Merged, reduction = "umap.harmony", group.by = "Harmony_snn_res.0.9",label = T, label.box = T)
#Celltype tables based on clusters
3. celltype_l1
library(dplyr)
library(pheatmap)
library(tidyr)
# List of annotations
annotations <- c("l1", "l2", "l3")
# Loop through each annotation to generate tables and heatmaps
for (annotation in annotations) {
# Create the column name dynamically for the current annotation
cell_type_column <- paste0("predicted.celltype.", annotation)
# Check if the column exists in the metadata of the Seurat object
if (!(cell_type_column %in% colnames(All_samples_Merged@meta.data))) {
cat("Skipping annotation", annotation, "- Column not found in metadata!\n")
next
}
# Create a table of cell line vs. cell types for the current annotation
cell_line_celltype_table <- table(All_samples_Merged$cell_line, All_samples_Merged@meta.data[[cell_type_column]])
# Print the table to check its structure
cat("Cell_line vs Cell Type table for", annotation, "\n")
print(cluster_celltype_table)
# Save the table as a CSV file
write.csv(cell_line_celltype_table, paste0("cell_line_celltype_", annotation, "_table.csv"))
# Convert the table to a numeric matrix
cell_line_celltype_matrix <- as.matrix(cell_line_celltype_table)
# Display the heatmap inline in the notebook
pheatmap(cell_line_celltype_matrix,
cluster_rows = TRUE,
cluster_cols = TRUE,
display_numbers = TRUE,
color = colorRampPalette(c("white", "red"))(50),
main = paste("Cell_line vs Cell Type (", annotation, ") Heatmap"))
# Save the heatmap as PNG (Optional)
png(paste0("cell_line_celltype_", annotation, "_heatmap.png"), width = 1800, height = 800)
pheatmap(cluster_celltype_matrix,
cell_line_rows = TRUE,
cell_line_cols = TRUE,
display_numbers = TRUE,
color = colorRampPalette(c("white", "red"))(50),
main = paste("Cell_line vs Cell Type (", annotation, ") Heatmap"))
dev.off()
# Save the heatmap as PDF (Optional)
pdf(paste0("cell_line_celltype_", annotation, "_heatmap.pdf"), width = 20, height = 10)
pheatmap(cell_line_celltype_matrix,
cell_line_rows = TRUE,
cell_line_cols = TRUE,
display_numbers = TRUE,
color = colorRampPalette(c("white", "red"))(50),
main = paste("Cell_line vs Cell Type (", annotation, ") Heatmap"))
dev.off()
}Cell_line vs Cell Type table for l1
ASDC_pDC B intermediate kappa B intermediate lambda B memory kappa B memory lambda B naive kappa B naive lambda
L1 0 0 0 0 0 0 0
L2 0 0 0 0 0 0 0
L3 0 0 2 7 3 0 0
L4 0 1 48 1 41 0 37
L5 0 0 4 7 7 0 0
L6 0 0 2 22 29 0 0
L7 0 0 3 55 26 0 0
PBMC 0 75 371 38 131 267 192
PBMC_10x 4 51 129 42 30 645 48
CD14 Mono CD16 Mono CD4 CTL CD4 Naive CD4 Proliferating CD4 TCM_1 CD4 TCM_2 CD4 TCM_3 CD4 TEM_1 CD4 TEM_2 CD4 TEM_3
L1 0 0 0 0 2462 1932 652 436 1 0 0
L2 0 0 0 0 2852 6 250 12 0 0 0
L3 1 0 0 0 5452 6 870 4 0 0 0
L4 15 0 0 9 5391 35 516 6 0 0 0
L5 9 0 0 0 4732 0 165 13 0 0 0
L6 0 0 0 0 4003 7 536 7 0 0 0
L7 22 0 0 0 4115 3 482 26 0 0 0
PBMC 755 2 27 528 0 4057 265 267 4 17 50
PBMC_10x 3043 124 1 1525 6 1449 144 359 8 17 1
CD8 Naive CD8 Naive_2 CD8 Proliferating CD8 TCM_1 CD8 TCM_2 CD8 TCM_3 CD8 TEM_1 CD8 TEM_2 CD8 TEM_3 CD8 TEM_4
L1 0 0 0 0 298 0 0 0 0 0
L2 0 0 0 8 6 1 0 0 0 0
L3 0 0 0 0 0 0 0 0 0 0
L4 0 0 0 0 0 0 0 0 0 0
L5 0 1 0 0 0 0 0 0 0 0
L6 0 1 1 0 0 0 0 1 0 0
L7 0 0 1 0 0 0 0 0 0 0
PBMC 291 63 0 137 80 77 91 32 17 2
PBMC_10x 969 47 0 115 43 17 67 62 15 12
CD8 TEM_5 CD8 TEM_6 cDC1 cDC2_1 cDC2_2 dnT_2 gdT_1 gdT_3 HSPC ILC MAIT NK Proliferating NK_1 NK_2 NK_3 NK_4
L1 0 1 0 0 0 2 0 0 0 0 0 38 0 0 0 0
L2 0 10 0 0 0 3 0 0 0 0 0 2785 0 0 0 0
L3 0 0 0 0 1 0 0 0 62 1 0 6 0 0 0 0
L4 0 0 1 1 3 2 0 0 7 3 0 24 0 0 0 0
L5 0 1 17 1 12 2 0 0 1036 0 0 11 0 0 0 0
L6 0 1 36 0 4 6 0 0 214 0 0 259 0 0 0 0
L7 0 1 16 4 33 2 0 0 493 0 0 38 0 0 0 0
PBMC 27 1 28 2 46 42 33 1 17 4 16 1 42 18 22 3
PBMC_10x 1 36 13 40 83 31 52 15 12 3 229 5 7 386 20 30
NK_CD56bright pDC Plasma Platelet Treg Memory Treg Naive
L1 0 0 0 0 3 0
L2 0 0 0 0 2 0
L3 0 0 0 0 13 0
L4 0 0 0 0 9 0
L5 0 0 0 0 4 0
L6 0 0 0 0 19 0
L7 0 0 0 0 11 0
PBMC 1 0 9 1 200 4
PBMC_10x 16 56 10 31 89 24
Cell_line vs Cell Type table for l2
ASDC_pDC B intermediate kappa B intermediate lambda B memory kappa B memory lambda B naive kappa B naive lambda
L1 0 0 0 0 0 0 0
L2 0 0 0 0 0 0 0
L3 0 0 2 7 3 0 0
L4 0 1 48 1 41 0 37
L5 0 0 4 7 7 0 0
L6 0 0 2 22 29 0 0
L7 0 0 3 55 26 0 0
PBMC 0 75 371 38 131 267 192
PBMC_10x 4 51 129 42 30 645 48
CD14 Mono CD16 Mono CD4 CTL CD4 Naive CD4 Proliferating CD4 TCM_1 CD4 TCM_2 CD4 TCM_3 CD4 TEM_1 CD4 TEM_2 CD4 TEM_3
L1 0 0 0 0 2462 1932 652 436 1 0 0
L2 0 0 0 0 2852 6 250 12 0 0 0
L3 1 0 0 0 5452 6 870 4 0 0 0
L4 15 0 0 9 5391 35 516 6 0 0 0
L5 9 0 0 0 4732 0 165 13 0 0 0
L6 0 0 0 0 4003 7 536 7 0 0 0
L7 22 0 0 0 4115 3 482 26 0 0 0
PBMC 755 2 27 528 0 4057 265 267 4 17 50
PBMC_10x 3043 124 1 1525 6 1449 144 359 8 17 1
CD8 Naive CD8 Naive_2 CD8 Proliferating CD8 TCM_1 CD8 TCM_2 CD8 TCM_3 CD8 TEM_1 CD8 TEM_2 CD8 TEM_3 CD8 TEM_4
L1 0 0 0 0 298 0 0 0 0 0
L2 0 0 0 8 6 1 0 0 0 0
L3 0 0 0 0 0 0 0 0 0 0
L4 0 0 0 0 0 0 0 0 0 0
L5 0 1 0 0 0 0 0 0 0 0
L6 0 1 1 0 0 0 0 1 0 0
L7 0 0 1 0 0 0 0 0 0 0
PBMC 291 63 0 137 80 77 91 32 17 2
PBMC_10x 969 47 0 115 43 17 67 62 15 12
CD8 TEM_5 CD8 TEM_6 cDC1 cDC2_1 cDC2_2 dnT_2 gdT_1 gdT_3 HSPC ILC MAIT NK Proliferating NK_1 NK_2 NK_3 NK_4
L1 0 1 0 0 0 2 0 0 0 0 0 38 0 0 0 0
L2 0 10 0 0 0 3 0 0 0 0 0 2785 0 0 0 0
L3 0 0 0 0 1 0 0 0 62 1 0 6 0 0 0 0
L4 0 0 1 1 3 2 0 0 7 3 0 24 0 0 0 0
L5 0 1 17 1 12 2 0 0 1036 0 0 11 0 0 0 0
L6 0 1 36 0 4 6 0 0 214 0 0 259 0 0 0 0
L7 0 1 16 4 33 2 0 0 493 0 0 38 0 0 0 0
PBMC 27 1 28 2 46 42 33 1 17 4 16 1 42 18 22 3
PBMC_10x 1 36 13 40 83 31 52 15 12 3 229 5 7 386 20 30
NK_CD56bright pDC Plasma Platelet Treg Memory Treg Naive
L1 0 0 0 0 3 0
L2 0 0 0 0 2 0
L3 0 0 0 0 13 0
L4 0 0 0 0 9 0
L5 0 0 0 0 4 0
L6 0 0 0 0 19 0
L7 0 0 0 0 11 0
PBMC 1 0 9 1 200 4
PBMC_10x 16 56 10 31 89 24
Cell_line vs Cell Type table for l3
ASDC_pDC B intermediate kappa B intermediate lambda B memory kappa B memory lambda B naive kappa B naive lambda
L1 0 0 0 0 0 0 0
L2 0 0 0 0 0 0 0
L3 0 0 2 7 3 0 0
L4 0 1 48 1 41 0 37
L5 0 0 4 7 7 0 0
L6 0 0 2 22 29 0 0
L7 0 0 3 55 26 0 0
PBMC 0 75 371 38 131 267 192
PBMC_10x 4 51 129 42 30 645 48
CD14 Mono CD16 Mono CD4 CTL CD4 Naive CD4 Proliferating CD4 TCM_1 CD4 TCM_2 CD4 TCM_3 CD4 TEM_1 CD4 TEM_2 CD4 TEM_3
L1 0 0 0 0 2462 1932 652 436 1 0 0
L2 0 0 0 0 2852 6 250 12 0 0 0
L3 1 0 0 0 5452 6 870 4 0 0 0
L4 15 0 0 9 5391 35 516 6 0 0 0
L5 9 0 0 0 4732 0 165 13 0 0 0
L6 0 0 0 0 4003 7 536 7 0 0 0
L7 22 0 0 0 4115 3 482 26 0 0 0
PBMC 755 2 27 528 0 4057 265 267 4 17 50
PBMC_10x 3043 124 1 1525 6 1449 144 359 8 17 1
CD8 Naive CD8 Naive_2 CD8 Proliferating CD8 TCM_1 CD8 TCM_2 CD8 TCM_3 CD8 TEM_1 CD8 TEM_2 CD8 TEM_3 CD8 TEM_4
L1 0 0 0 0 298 0 0 0 0 0
L2 0 0 0 8 6 1 0 0 0 0
L3 0 0 0 0 0 0 0 0 0 0
L4 0 0 0 0 0 0 0 0 0 0
L5 0 1 0 0 0 0 0 0 0 0
L6 0 1 1 0 0 0 0 1 0 0
L7 0 0 1 0 0 0 0 0 0 0
PBMC 291 63 0 137 80 77 91 32 17 2
PBMC_10x 969 47 0 115 43 17 67 62 15 12
CD8 TEM_5 CD8 TEM_6 cDC1 cDC2_1 cDC2_2 dnT_2 gdT_1 gdT_3 HSPC ILC MAIT NK Proliferating NK_1 NK_2 NK_3 NK_4
L1 0 1 0 0 0 2 0 0 0 0 0 38 0 0 0 0
L2 0 10 0 0 0 3 0 0 0 0 0 2785 0 0 0 0
L3 0 0 0 0 1 0 0 0 62 1 0 6 0 0 0 0
L4 0 0 1 1 3 2 0 0 7 3 0 24 0 0 0 0
L5 0 1 17 1 12 2 0 0 1036 0 0 11 0 0 0 0
L6 0 1 36 0 4 6 0 0 214 0 0 259 0 0 0 0
L7 0 1 16 4 33 2 0 0 493 0 0 38 0 0 0 0
PBMC 27 1 28 2 46 42 33 1 17 4 16 1 42 18 22 3
PBMC_10x 1 36 13 40 83 31 52 15 12 3 229 5 7 386 20 30
NK_CD56bright pDC Plasma Platelet Treg Memory Treg Naive
L1 0 0 0 0 3 0
L2 0 0 0 0 2 0
L3 0 0 0 0 13 0
L4 0 0 0 0 9 0
L5 0 0 0 0 4 0
L6 0 0 0 0 19 0
L7 0 0 0 0 11 0
PBMC 1 0 9 1 200 4
PBMC_10x 16 56 10 31 89 24
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