Tables of celllines based on clusters harmony0.9

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_Merged

An 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.harmony

DimPlot(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 cluster

3. celltype_l1

# Create a contingency table of cell line vs clusters
cluster_cell_line_table <- table(All_samples_Merged$cell_line, All_samples_Merged$Harmony_snn_res.0.9)

# Print the table to check its structure
print(cluster_cell_line_table)

          
              0    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18   19   20   21   22
  L1          2    0    0  125    0    0    0    1 2012    0 3038   16    0    0    0    1    0   25  593    0   12    0    0
  L2          0    1    0 4159    0    1    2    1 1722    0    9    0    0    0    0    0    0    7   22    0   11    0    0
  L3          2 3472 2670    0    0    1   30    1    0    0    1    0    4  129    0    1    3    6    1   68   37    0    0
  L4          0 1736 1744    0    0    3  520    3    0    0    2    7  158 1840    1   12   27   18    2   57   18    0    2
  L5          1    0    1    0 1827    6  423 2998    0  272    0    0    0    0    0    0  141   12    3  320   17    0    1
  L6          1   10    4    0  550    2 1535   92    0 2506    0    0    0    0    1    0  151   23    8  107  158    0    0
  L7          1    2    1    0 1879    4 1249  715    0  835    0    0    0    0    0    0  538   19    2   57   29    0    0
  PBMC     2694    0    1    0    0 2685   24    0    4    0    6   51 1096    0  416  744    0  428    2    3   81    3   91
  PBMC_10x 3413    2    2    0    0 1199   30    0    7    0   14 2792  936    1  930  130    6  219   15    0  209  222    0
          
             23   24
  L1          0    0
  L2          0    0
  L3          2    0
  L4          0    0
  L5          0    0
  L6          0    0
  L7          0    0
  PBMC        6   19
  PBMC_10x   35    0

# Save the table as a CSV file
write.csv(cluster_cell_line_table, "cluster_cell_line_table.csv")

# Convert the table to a numeric matrix for heatmap generation
cluster_cell_line_matrix <- as.matrix(cluster_cell_line_table)

# Generate the heatmap using pheatmap
library(pheatmap)
pheatmap(cluster_cell_line_matrix,
         cluster_rows = TRUE,    # Cluster rows (cell lines)
         cluster_cols = TRUE,    # Cluster columns (clusters)
         display_numbers = TRUE, # Display cell counts in the heatmap
         color = colorRampPalette(c("white", "red"))(50),  # Color gradient from white to red
         main = "Cell Line vs Cluster Heatmap")

# Save the heatmap as a PNG (Optional)
png("cluster_cell_line_heatmap.png", width = 2200, height = 600)

pheatmap(cluster_cell_line_matrix,
         cluster_rows = TRUE,
         cluster_cols = TRUE,
         display_numbers = TRUE,
         color = colorRampPalette(c("white", "red"))(50),
         main = "Cell Line vs Cluster Heatmap")
dev.off()

png 
  3 

# Save the heatmap as a PDF (Optional)
pdf("cluster_cell_line_heatmap.pdf", width = 22, height = 8)

pheatmap(cluster_cell_line_matrix,
         cluster_rows = TRUE,
         cluster_cols = TRUE,
         display_numbers = TRUE,
         color = colorRampPalette(c("white", "red"))(50),
         main = "Cell Line vs Cluster Heatmap")
dev.off()

png 
  3