plot1cell a package for advanced single cell data visualization

1. load libraries

library(dplyr)
library(Seurat)
library(ComplexHeatmap)
# https://github.com/immunogenomics/presto
library(presto)
library(tictoc)

library(plot1cell)

2. Load Seurat Object

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("../../To_Transfer_between_computers/23-Harmony_Integration/0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_Selected_0.8_ADT_Normalized_cleaned_mt.robj")

3. Circlize plot to visualize cell clustering and meta data

All_samples_Merged$Condition <- ifelse(
  All_samples_Merged$cell_line %in% c("L1", "L2", "L3", "L4", "L5", "L6", "L7"),  # your malignant lines
  "Malignant",
  "Control"
)

library(plot1cell)

SS.integrated <- All_samples_Merged

###Check and see the meta data info on your Seurat object
colnames(SS.integrated@meta.data)  

 [1] "orig.ident"                         "nCount_RNA"                         "nFeature_RNA"                      
 [4] "nCount_ADT"                         "nFeature_ADT"                       "nUMI"                              
 [7] "ngene"                              "cell_line"                          "Patient_origin"                    
[10] "Patient_Immunophenotype"            "condition_of_amplification_in_vivo" "culture_medium"                    
[13] "Stromal_cells"                      "Cell_line_Immunophenotype"          "TP53_mutation"                     
[16] "age_at_diagnosis"                   "stage_diagnosis"                    "stage_analysis"                    
[19] "Treatments_analysis"                "TCRVB2"                             "CD3_M"                             
[22] "CD30_M"                             "CCR4_M"                             "CD162_BL"                          
[25] "CD26_BL"                            "CD7_M"                              "CLA_BD"                            
[28] "CD4_BD"                             "CCR7_M"                             "CD45RO_BD"                         
[31] "CD45RA_BD"                          "CellName"                           "percent.mt"                        
[34] "CD26_BD"                            "CD45RA_M"                           "predicted.celltype.l1.score"       
[37] "predicted.celltype.l1"              "predicted.celltype.l2.score"        "predicted.celltype.l2"             
[40] "predicted.celltype.l3.score"        "predicted.celltype.l3"              "mapping.score"                     
[43] "percent.rb"                         "nCount_SCT"                         "nFeature_SCT"                      
[46] "S.Score"                            "G2M.Score"                          "Phase"                             
[49] "old.ident"                          "CC.Difference"                      "SCT_snn_res.0.4"                   
[52] "SCT_snn_res.0.5"                    "SCT_snn_res.0.6"                    "SCT_snn_res.0.7"                   
[55] "SCT_snn_res.0.8"                    "seurat_clusters"                    "harmony_res_0.1"                   
[58] "harmony_res_0.2"                    "harmony_res_0.3"                    "harmony_res_0.4"                   
[61] "harmony_res_0.5"                    "harmony_res_0.6"                    "harmony_res_0.7"                   
[64] "harmony_res_0.8"                    "harmony_res_0.9"                    "harmony_res_1"                     
[67] "harmony_res_1.2"                    "predicted.celltype.l1_backup"       "predicted.celltype.l2_backup"      
[70] "predicted.celltype.l3_backup"       "Condition"                         

###Prepare data for ploting
circ_data <- prepare_circlize_data(SS.integrated, scale = 0.8 )
set.seed(1234)
cluster_colors<-rand_color(length(levels(SS.integrated)))
group_colors<-rand_color(length(names(table(SS.integrated$Condition))))
rep_colors<-rand_color(length(names(table(SS.integrated$cell_line))))

###plot and save figures
png(filename =  'circlize_plot.png', width = 6, height = 6,units = 'in', res = 300)
plot_circlize(circ_data,do.label = T, pt.size = 0.01, col.use = cluster_colors ,bg.color = 'white', kde2d.n = 200, repel = T, label.cex = 0.6)
add_track(circ_data, group = "Condition", colors = group_colors, track_num = 2) ## can change it to one of the columns in the meta data of your seurat object
add_track(circ_data, group = "cell_line",colors = rep_colors, track_num = 3) ## can change it to one of the columns in the meta data of your seurat object
dev.off()

null device 
          1 

plot_circlize(circ_data,do.label = T, pt.size = 0.01, col.use = cluster_colors ,bg.color = 'white', kde2d.n = 200, repel = T, label.cex = 0.6)

add_track(circ_data, group = "Condition", colors = group_colors, track_num = 2) ## can change it to one of the columns in the meta data of your seurat object
add_track(circ_data, group = "cell_line",colors = rep_colors, track_num = 3) ## can change it to one of the columns in the meta data of your seurat object

Dotplot to show gene expression across groups

png(filename = 'dotplot_CLIC1.png', width = 4, height = 6, units = 'in', res = 100)
complex_dotplot_single(seu_obj = SS.integrated, feature = "CLIC1", groups = "Condition")
dev.off()

null device 
          1 

complex_dotplot_single(seu_obj = SS.integrated, feature = "CLIC1", groups = "Condition")

png(filename =  'dotplot_IL4.png', width = 4, height = 6,units = 'in', res = 100)
complex_dotplot_single(seu_obj = SS.integrated, feature = "IL4", groups = "Condition")

dev.off()

png 
  2 

complex_dotplot_single(seu_obj = SS.integrated, feature = "IL4", groups = "Condition")


png(filename =  'dotplot_IL7R.png', width = 4, height = 6,units = 'in', res = 100)

complex_dotplot_single(seu_obj = SS.integrated, feature = "IL7R", groups = "Condition")
dev.off()

png 
  2 

complex_dotplot_single(seu_obj = SS.integrated, feature = "IL7R", groups = "Condition")

Dotplot to show gene expression across groups

SS.integrated@meta.data$Status <- plyr::mapvalues(
  SS.integrated@meta.data$Condition, 
  from = c("Control", "Malignant"),
  to = c("Healthy", "Malignant")
)
SS.integrated@meta.data$Status <- as.character(SS.integrated@meta.data$Status)


png(filename = 'dotplot_single_split.png', width = 4, height = 6, units = 'in', res = 100)
complex_dotplot_single(seu_obj = SS.integrated, feature = "CLIC1", groups = "Condition", splitby = "Status")

NULL

dev.off()

null device 
          1 

complex_dotplot_single(seu_obj = SS.integrated, feature = "CLIC1", groups = "Condition", splitby = "Status")

NULL

png(filename = 'dotplot_more_groups.png', width = 8, height = 6, units = 'in', res = 100)
complex_dotplot_single(
  seu_obj = SS.integrated,
  feature = "CLIC1",
  groups = c("Condition", "orig.ident")  # you can replace "orig.ident" if you have another replicates info
)

NULL

dev.off()

png 
  2 

complex_dotplot_single(
  seu_obj = SS.integrated,
  feature = "CLIC1",
  groups = c("Condition", "orig.ident")  # you can replace "orig.ident" if you have another replicates info
)

NULL

# 1. Create Condition (if you haven't already)
# Assuming you have orig.ident with L1–L7 (malignant) and PBMC1–2 (control)
SS.integrated@meta.data$Condition <- plyr::mapvalues(
  SS.integrated@meta.data$orig.ident,
  from = levels(factor(SS.integrated@meta.data$orig.ident)),  # dynamically get L1–L7 + PBMCs
  to = c(rep("Malignant", 7), rep("Control", 2))
)
SS.integrated@meta.data$Condition <- as.character(SS.integrated@meta.data$Condition)

# 2. Create ReplicateID (fake replicate names just for plotting clarity)
SS.integrated@meta.data$ReplicateID <- plyr::mapvalues(
  SS.integrated@meta.data$orig.ident,
  from = names(table(SS.integrated@meta.data$orig.ident)),
  to = c(paste0("Malignant_", 1:7), paste0("Control_", 1:2))
)
SS.integrated@meta.data$ReplicateID <- as.character(SS.integrated@meta.data$ReplicateID)

# 3. Create GroupType (instead of Phase, because you already have Phase for cell cycle)
SS.integrated@meta.data$GroupType <- ifelse(
  SS.integrated@meta.data$Condition == "Malignant",
  "SS", "Normal"
)

# 4. Plot
png(filename = 'dotplot_more_groups_split.png', width = 9, height = 6, units = 'in', res = 200)
complex_dotplot_single(
  seu_obj = SS.integrated,
  feature = "CLIC1",  # You can replace with any gene of interest
  groups = c("Condition", "orig.ident"),  # Condition first, then sample names
  splitby = c("GroupType", "ReplicateID")  # Splitting based on GroupType and sample replicate IDs
)

Avis : attributes are not identical across measure variables; they will be dropped

dev.off()

png 
  2 

complex_dotplot_single(
  seu_obj = SS.integrated,
  feature = "CLIC1",  # You can replace with any gene of interest
  groups = c("Condition", "orig.ident"),  # Condition first, then sample names
  splitby = c("GroupType", "ReplicateID")  # Splitting based on GroupType and sample replicate IDs
)

Avis : attributes are not identical across measure variables; they will be dropped

Violin plot to show gene expression across groups

# Ensure Group2 is correctly assigned in your metadata
SS.integrated$Group2 <- plyr::mapvalues(SS.integrated$Condition, 
                                         from = c("Control", "Malignant"),  # Original group values
                                         to = c("Ctrl", "Malignant"))       # New mapped values

# Ensure Group2 is a factor with correct levels
SS.integrated$Group2 <- factor(SS.integrated$Group2, levels = c("Ctrl", "Malignant"))

# Check the distribution of Group2 to ensure both groups have cells
print(table(SS.integrated$Group2))  # This should show counts for "Ctrl" and "Malignant"

     Ctrl Malignant 
     8610     40695 

# Check if "Malignant" is present in seurat_clusters or your celltype variable
# If using clusters or other identifiers, modify accordingly
print(table(SS.integrated$seurat_clusters))  # Check the distribution of clusters (or celltype)

   0    1    2    3    4    5    6    7    8    9   10   11   12   13 
6789 5275 4663 4661 4086 3634 3536 3409 3338 3273 3212 1675 1063  691 

# Genes of interest
genes_of_interest <- c("CLIC1", "MYL6B", "TUBA1C", "UBE2C", "NME1", "PLK1", 
                       "BTG1", "IKZF1", "PIK3IP1", "RIPOR2", "TXNIP", "IL7R", 
                       "CDKN1B", "FOXP1", "CD247")

# Check if all genes are present in the dataset
missing_genes <- genes_of_interest[!genes_of_interest %in% rownames(SS.integrated)]
if (length(missing_genes) > 0) {
  print(paste("Missing genes:", paste(missing_genes, collapse = ", ")))
} else {
  print("All genes are present in the dataset.")
}

[1] "All genes are present in the dataset."

# Now create the heatmap, ensuring the correct grouping and celltype
png(filename = 'heatmap_group_sezary.png', width = 4, height = 8, units = 'in', res = 100)

# Make sure to adjust the celltype argument based on valid clusters or celltypes in your dataset
complex_heatmap_unique(
    seu_obj = SS.integrated,              # Seurat object
    celltype = "Malignant",                # Adjust if you want a specific celltype or cluster
    group = "Group2",                      # Grouping variable (Control vs Malignant)
    gene_highlight = genes_of_interest     # Genes you want to highlight in the heatmap
)

The following grouping variables have 1 value and will be ignored: ident
All grouping variables have 1 value only. Computing across all cells.
Avis : No DE genes identifiedAvis : The following tests were not performed: Avis : When testing Malignant versus all:
    Cells in one or both identity groups are not present in the data requestedErreur dans unique_list[[i]] : indice hors limites