ADT normalization- Margin = 1 and Visualized by dittoseq heatmap

1. load libraries

2. Load Seurat Object

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
SS_All_samples_Merged <- load("/home/bioinfo/0-imp_Robj/All_Normal-PBMC_Abnormal-cellLines_T_cells_Merged_Annotated_UMAP_on_Clusters_to_USE.Robj")

All_samples_Merged

An object of class Seurat 
62625 features across 46976 samples within 6 assays 
Active assay: SCT (25901 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
 4 dimensional reductions calculated: pca, umap, integrated_dr, ref.umap

DimPlot(All_samples_Merged, group.by = "cell_line", label = T, label.box = T)

3. Normalize ADT-Margin1

rownames(All_samples_Merged[["ADT"]])

 [1] "CD274"  "CD30"   "CD40"   "CD3"    "CD45RA" "CD7"    "CCR4"   "CD4"    "CD25"   "CD45RO" "PD1"    "CD44"   "CD5"    "CXCR3" 
[15] "CCR6"   "CD62L"  "CCR7"   "CD95"   "TCRab"  "CXCR4"  "CD2"    "CD28"   "CD127"  "CD45"   "CD26"   "CCR10"  "CCR8"   "CD19"  

# Perform normalization and scaling
All_samples_Merged <- NormalizeData(All_samples_Merged, normalization.method = "CLR", margin = 1, assay = "ADT")

Normalizing across features

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DefaultAssay(All_samples_Merged) <- "ADT"

genes <- rownames(All_samples_Merged[["ADT"]])

# Replace NA values in the 'Patient_origin' column with 'PBMC'
All_samples_Merged@meta.data$Patient_origin[All_samples_Merged@meta.data$Patient_origin == "NA"] <- "PBMC"


library(dittoSeq)

dittoHeatmap(All_samples_Merged, genes,
    annot.by = c("Patient_origin", "cell_line"))

# Load additional libraries for color palettes
library(RColorBrewer)

# Define a custom color palette for the heatmap
my_colors <- colorRampPalette(brewer.pal(9, "YlGnBu"))(100)  # Example: Yellow to Blue

# Generate the heatmap with custom colors
dittoHeatmap(
  All_samples_Merged, 
  genes,
  annot.by = c("Patient_origin", "cell_line"),
  heatmap.colors = my_colors,  # Apply custom heatmap colors
  scaled.to.max = TRUE         # Optionally, scale data for better contrast
)

NA
NA

Save the Seurat object as an Robj file

# save(All_samples_Merged, file = "/home/bioinfo/0-imp_Robj/All_Samples_Merged_NormalizedADT_Margin1.Robj")

4. Normalize ADT-Margin2

rownames(All_samples_Merged[["ADT"]])
# Perform normalization and scaling
All_samples_Merged <- NormalizeData(All_samples_Merged, normalization.method = "CLR", margin = 2, assay = "ADT")

DefaultAssay(All_samples_Merged) <- "ADT"

genes <- rownames(All_samples_Merged[["ADT"]])

# Replace NA values in the 'Patient_origin' column with 'PBMC'
All_samples_Merged@meta.data$Patient_origin[All_samples_Merged@meta.data$Patient_origin == "NA"] <- "PBMC"


library(dittoSeq)

dittoHeatmap(All_samples_Merged, genes,
    annot.by = c("Patient_origin", "cell_line"))


# Load additional libraries for color palettes
library(RColorBrewer)

# Define a custom color palette for the heatmap
my_colors <- colorRampPalette(brewer.pal(9, "YlGnBu"))(100)  # Example: Yellow to Blue

# Generate the heatmap with custom colors
dittoHeatmap(
  All_samples_Merged, 
  genes,
  annot.by = c("Patient_origin", "cell_line"),
  heatmap.colors = my_colors,  # Apply custom heatmap colors
  scaled.to.max = TRUE         # Optionally, scale data for better contrast
)

Save the Seurat object as an Robj file

# save(All_samples_Merged, file = "/home/bioinfo/0-imp_Robj/All_Samples_Merged_NormalizedADT_Margin2.Robj")