TCR scplotter Visualization for Sezary Syndrome

1. Load libraries

# Load necessary libraries
library(Seurat)
library(scplotter)
library(dplyr)
library(ggplot2)
library(patchwork)
library(Matrix)

# IMPORTANT: Replace this with the actual path and command to load your object
TCR <- readRDS("../../0-robj/TCR_Seurat_with_cloneSize_12_11_2025_finalized.rds")



TCR

An object of class Seurat 
62900 features across 44333 samples within 6 assays 
Active assay: SCT (26176 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
 5 dimensional reductions calculated: integrated_dr, ref.umap, pca, umap, harmony

2. ClonalVolumePlot

ClonalVolumePlot(TCR)

ClonalVolumePlot

TCR_subset <- subset(TCR, subset = orig.ident != "CD4T_lab")


ClonalVolumePlot(TCR_subset)

ClonalVolumePlot

ClonalVolumePlot(TCR, x = "seurat_clusters")

ClonalVolumePlot

ClonalVolumePlot(TCR_subset, x = "seurat_clusters")

ClonalVolumePlot

ClonalVolumePlot(TCR, x = "Condition")

ClonalVolumePlot

ClonalVolumePlot(TCR_subset, x = "Condition")

ClonalVolumePlot

ClonalVolumePlot(TCR_subset,
    plot_type = "box", x = "Condition", comparisons = TRUE,
    group_by = "age_at_diagnosis"
)

ClonalVolumePlot

ClonalVolumePlot(TCR, group_by = "seurat_clusters")

ClonalVolumePlot

ClonalVolumePlot(TCR_subset, group_by = "seurat_clusters")

3.ClonalCompositionPlot

ClonalCompositionPlot(TCR)

ClonalCompositionPlot(TCR_subset)

ClonalCompositionPlot

ClonalCompositionPlot(TCR, method = "top")

ClonalCompositionPlot(TCR_subset, method = "top")

ClonalCompositionPlot

ClonalCompositionPlot(TCR,  plot_type = "ring")

ClonalCompositionPlot(TCR_subset,  plot_type = "ring")

ClonalCompositionPlot

ClonalCompositionPlot(TCR, group_by = "Condition", plot_type = "box", comparison = TRUE,
 clone_split = list(Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1))

ClonalCompositionPlot(TCR_subset, group_by = "Condition", plot_type = "box", comparison = TRUE,
 clone_split = list(Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1))

4.ClonalOverlapPlot

ClonalOverlapPlot(TCR)

ClonalOverlapPlot(TCR_subset)

ClonalOverlapPlot

ClonalOverlapPlot(TCR, clone_call = "strict", label_cutoff = 0,
  label_accuracy = 0.001, method = "morisita", full = FALSE)

ClonalOverlapPlot(TCR_subset, clone_call = "strict", label_cutoff = 0,
  label_accuracy = 0.001, method = "morisita", full = FALSE)

ClonalOverlapPlot

ClonalOverlapPlot(TCR, group_by = c("Condition", "orig.ident"))

ClonalOverlapPlot(TCR_subset, group_by = c("Condition", "orig.ident"))

ClonalOverlapPlot

ClonalOverlapPlot(TCR, group_by = "orig.ident", split_by = "Condition" )

ClonalOverlapPlot(TCR_subset, group_by = "orig.ident", split_by = "Condition")

4.ClonalGeneUsagePlot

ClonalGeneUsagePlot(TCR, top = 40, plot_type = "heatmap", group_by = "CTgene")


ClonalGeneUsagePlot(TCR_subset, top = 40, plot_type = "heatmap", group_by = "CTgene")

5.ClonalStatPlot

ClonalStatPlot(TCR_subset, group_by = "orig.ident", groups = c("L1", "L2"))