Single-cell Visualization Customization: Custom Color and Order t-SNE and Proportion Plot

Author: SeekGene

Time: 17 min

Words: 3.4k words

Updated: 2026-02-27

Reads: 0 times

3' scRNA-seq 5' + Immune Profiling Cell Annotation FFPE scRNA-seq Notebooks Spatial-seq scATAC + RNA-seq scFAST-seq scMethyl + RNA-seq

Environment Setup

# Load necessary R packages
library(Seurat)      # For single-cell data analysis
library(tidyverse)   # Data processing and visualization tools
library(ggplot2)     # Plotting package
library(grid)        # Low-level graphics system

# Define color scheme for cell types
celltype_colors <- c(
  "T cell" = "#2A85BD",
  "Mono_Macro" = "#FF6700",
  "NK" = "#FF96B2",
  "mDC" = "#A6A6A6",
  "Plasma" = "#F8D76E",
  "Mast" = "#42C274",
  "B cell" = "#9370DB",
  "pDC" = "#89CFF0"
)
group_colors <- c(
  "S150" = "#1A66B1",
  "S133" = "#DB0308",
  "S134" = "#BA170D",
  "S135" = "#FF6700",
  "S158" = "#A6A6A6",
  "S158" = "#F8D568",
  "S159" = "#FFAFC5",
  "S149" = "#91D2F1"
)

Data Loading

# Read Seurat object and metadata
seurat.obj <- readRDS("data/AY1739512568405/input.rds")
meta <- read.table("data/AY1739512568405/meta.tsv", header=T, sep="\t", row.names = 1)
# Add metadata to Seurat object
obj <- AddMetaData(seurat.obj, meta)
# Set default analysis data to RNA
DefaultAssay(obj) = "RNA"
# View first few rows of metadata
head(obj@meta.data)

A data.frame: 6 × 14
	orig.ident	nCount_RNA	nFeature_RNA	Sample	mito	raw_Sample	Tissue	Patient	resolution.0.6_d20	mitorelatedgenes	CellAnnotation	celltype	mergedcelltype	Oesophagus
	<chr>	<int>	<int>	<chr>	<dbl>	<chr>	<chr>	<chr>	<int>	<dbl>	<chr>	<chr>	<chr>	<chr>
AAACCTGAGATACACA-1_1	SeuratProject	2797	1425	S150T	4.2903	GSE145370_S150T	Tumor	S150	1	3.3249911	T_NK Cell	T cell	CD8_T	T or NK Cell
AAACCTGAGCTAACTC-1_1	SeuratProject	2790	1349	S150T	1.1470	GSE145370_S150T	Tumor	S150	5	1.0035842	Macrophage	Mono_Macro	Macrophage	Macrophage
AAACCTGAGGAGCGAG-1_1	SeuratProject	1768	1054	S150T	4.7511	GSE145370_S150T	Tumor	S150	1	4.0158371	T_NK Cell	T cell	CD8_T	T or NK Cell
AAACCTGAGGGAAACA-1_1	SeuratProject	4455	2017	S150T	2.2896	GSE145370_S150T	Tumor	S150	8	1.8855219	T_NK Cell	T cell	CD8_T	T or NK Cell
AAACCTGAGTCCCACG-1_1	SeuratProject	1422	861	S150T	0.9845	GSE145370_S150T	Tumor	S150	1	0.7032349	T_NK Cell	T cell	CD8_T	T or NK Cell
AAACCTGAGTGAACAT-1_1	SeuratProject	2522	1308	S150T	1.7843	GSE145370_S150T	Tumor	S150	4	1.5463918	T_NK Cell	T cell	Treg	T or NK Cell

Data Filtering

# View unique values in data
unique(obj@meta.data$Tissue)    # View treatment groups
unique(obj@meta.data$Patient)   # View patient groups
unique(obj@meta.data$celltype)  # View cell types
unique(obj@meta.data$Sample)    # View sample IDs

'Tumor'
'Adjacent'

'S150'
'S133'
'S134'
'S135'
'S158'
'S159'
'S149'

'T cell'
'Mono_Macro'
'NK'
'mDC'
'Plasma'
'Other'
'Mast'
'B cell'
'pDC'

'S150T'
'S133T'
'S134T'
'S135T'
'S158T'
'S159T'
'S149T'
'S150A'
'S133A'
'S134A'
'S135A'
'S158A'
'S159A'
'S149A'

## t-SNE Visualization

'T cell'
'Mono_Macro'
'NK'
'mDC'
'Plasma'
'Mast'
'B cell'
'pDC'

t-SNE Visualization

###################
# Draw regular tSNE plot #
###################

# Set figure size
options(repr.plot.height=8, repr.plot.width=8)
# Create basic tSNE plot
p1 <- DimPlot(obj,
              reduction = "tsne",           # Use tSNE reduction results
              cols = celltype_colors,       # Use predefined color scheme
              label = T,                    # Show labels
              label.size = 4,               # Label size
              pt.size = 2.0,               # Point size
              group.by = "celltype")        # Group by cell type
# Remove legend
p1 <- p1 + guides(color = FALSE) 
p1

output

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set \`raster=FALSE\`

Warning message:
“The scale argument of \`guides()\` cannot be \`FALSE\`. Use "none" instead as
of ggplot2 3.3.4.”

###################
# Draw improved tSNE plot #
###################

# Set figure size
options(repr.plot.height=8, repr.plot.width=8)
# Get tSNE coordinate range
tsne_coords <- FetchData(obj, vars = c("tSNE_1", "tSNE_2"))
x_range <- range(tsne_coords$tSNE_1)
y_range <- range(tsne_coords$tSNE_2)

# Calculate axis start and end positions
x_start <- x_range[1] * 1.1    # x-axis start, extend left by 10%
x_end <- x_range[2] * 0.001    # x-axis end
y_start <- y_range[1] * 1.1    # y-axis start, extend down by 10%
y_end <- y_range[2] * 0.001    # y-axis end

# Calculate axis label positions
x_middle <- (x_start + x_end) / 2    # x-axis label position
y_middle <- (y_start + y_end) / 2    # y-axis label position
# Create improved tSNE plot
p1_new <- DimPlot(obj, 
                  reduction = "tsne",
                  cols = celltype_colors, 
                  label = TRUE,
                  label.size = 4, 
                  pt.size = 2.0, 
                  group.by = "celltype") +
  guides(color = FALSE) +                   # Remove legend
  labs(title = "celltype") +               # Add title
  theme_classic() +                        # Use classic theme
  # Custom theme settings
  theme(
    axis.line = element_blank(),           # Remove axis lines
    axis.text = element_blank(),           # Remove axis text
    axis.ticks = element_blank(),          # Remove ticks
    axis.title = element_blank(),          # Remove axis titles
    plot.title = element_text(size = 14,   # Set title style
                            hjust = 0.5,    
                            vjust = 1),     
    panel.background = element_blank(),    # Remove panel background
    plot.background = element_blank()      # Remove plot background
  ) +
  # Add x-axis line and arrow
  annotate("segment", 
           x = x_start,           
           xend = x_end,          
           y = y_start,           
           yend = y_start,        
           arrow = arrow(length = unit(0.3, "cm")), 
           color = "black") +
  # Add y-axis line and arrow
  annotate("segment", 
           x = x_start,           
           xend = x_start,        
           y = y_start,           
           yend = y_end,          
           arrow = arrow(length = unit(0.3, "cm")), 
           color = "black") +
  # Add x-axis label
  annotate("text",
           x = x_middle,          
           y = y_start - 2,       
           label = "tSNE_1",
           size = 4) +
  # Add y-axis label
  annotate("text",
           x = x_start - 3,       
           y = y_middle,          
           label = "tSNE_2",
           angle = 0,             
           size = 4)
p1_new

output

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set \`raster=FALSE\`

Cell Count Plot

#############################
# Count cells and draw bar plot #
#############################

# Count number of cells for each cell type in different samples
counts <- table(obj@meta.data$celltype, obj@meta.data$Patient)
counts_df <- as.data.frame(counts)
# Rename columns
colnames(counts_df) <- c("celltype", "group", "Cell number")

# View data structure
head(counts_df)
unique(counts_df$group)
unique(counts_df$celltype)

A data.frame: 6 × 3
	celltype	group	Cell number
	<fct>	<fct>	<int>
1	B cell	S133	439
2	Mast	S133	1001
3	mDC	S133	1586
4	Mono_Macro	S133	3712
5	NK	S133	684
6	pDC	S133	31

S133
S134
S135
S149
S150
S158
S159

Levels:

'S133'
'S134'
'S135'
'S149'
'S150'
'S158'
'S159'

B cell
Mast
mDC
Mono_Macro
NK
pDC
Plasma
T cell

Levels:

'B cell'
'Mast'
'mDC'
'Mono_Macro'
'NK'
'pDC'
'Plasma'
'T cell'

# Set figure size
options(repr.plot.height=6, repr.plot.width=12)
# Create first version bar plot
p2 <- ggplot(counts_df, aes(x = celltype, y = `Cell number`, fill = group)) +
  # Create grouped bar plot
  geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
  # Set fill color and legend label
  scale_fill_manual(values = group_colors, 
                    labels = c('S133','S134','S135','S149','S150','S158','S159'), 
                    breaks = c('S133','S134','S135','S149','S150','S158','S159')) +
  # Set axis labels
  labs(x = NULL, y = "Cell number", fill = "") +
  theme_minimal() +
  # Custom theme settings
  theme(
    panel.grid = element_line(colour = "gray", size = 0.5),     # Grid line settings
    axis.text.x = element_text(color = "black", size = 14),     # x-axis text
    axis.text.y = element_text(color = "black", size = 14),     # y-axis text
    axis.title.x = element_text(color = "black", size = 18),    # x-axis title
    axis.title.y = element_text(color = "black", size = 18),    # y-axis title
    axis.ticks = element_line(color = "black", size = 0.5, lineend = "butt"),  # Ticks
    axis.ticks.length = unit(0.25, "cm"),                       # Tick length
    legend.text = element_text(size = 12),                      # Legend text
    legend.title = element_text(size = 14)                      # Legend title
  ) +
  # Add border
  geom_rect(xmin = 0.41, xmax = 8.6, ymin = -10, ymax = 10650, 
            fill = "transparent",  
            color = "black",       
            size = 0.3)              
p2

output

Warning message:
“The \`size\` argument of \`element_line()\` is deprecated as of ggplot2 3.4.0.
ℹ Please use the \`linewidth\` argument instead.”
Warning message:
“Using \`size\` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use \`linewidth\` instead.”

# Reorder group levels
counts_df$group <- factor(counts_df$group, 
                         levels = c('S149','S134','S135','S133','S150','S158','S159'))
# Create second version bar plot (after reordering)
options(repr.plot.height=6, repr.plot.width=12)
p2<-ggplot(counts_df, aes(x = celltype, y = `Cell number`, fill = group)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
  scale_fill_manual(values = group_colors) +
  labs(x = NULL, y = "Cell number", fill = "") +
  theme_minimal() +
  theme(
    panel.grid = element_line(colour = "gray", size = 0.5),
    axis.text.x = element_text(color = "black", size = 14),
    axis.text.y = element_text(color = "black", size = 14),
    axis.title.x = element_text(color = "black", size = 18),
    axis.title.y = element_text(color = "black", size = 18),
    axis.ticks = element_line(color = "black", size = 0.5, lineend = "butt"),
    axis.ticks.length = unit(0.25, "cm"),
    legend.text = element_text(size = 12),
    legend.title = element_text(size = 14)
  ) +
  geom_rect(xmin = 0.41, xmax = 8.6, ymin = -15, ymax = 10650, 
            fill = "transparent",  # Fill transparent
            color = "black",       # Border color black
            size = 0.3)              # Border width 1
p2

# Reorder cell types
counts_df$celltype <- factor(counts_df$celltype, 
                         levels = c('T cell','Mono_Macro','NK','mDC','Plasma','Mast','B cell','pDC'))
# Create third version bar plot (after reordering)
options(repr.plot.height=6, repr.plot.width=12)
p2<-ggplot(counts_df, aes(x = celltype, y = `Cell number`, fill = group)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
  scale_fill_manual(values = group_colors) +
  labs(x = NULL, y = "Cell number", fill = "") +
  theme_minimal() +
  theme(
    panel.grid = element_line(colour = "gray", size = 0.5),
    axis.text.x = element_text(color = "black", size = 14),
    axis.text.y = element_text(color = "black", size = 14),
    axis.title.x = element_text(color = "black", size = 18),
    axis.title.y = element_text(color = "black", size = 18),
    axis.ticks = element_line(color = "black", size = 0.5, lineend = "butt"),
    axis.ticks.length = unit(0.25, "cm"),
    legend.text = element_text(size = 12),
    legend.title = element_text(size = 14)
  ) +
  geom_rect(xmin = 0.41, xmax = 8.6, ymin = -15, ymax = 10650, 
            fill = "transparent",  # Fill transparent
            color = "black",       # Border color black
            size = 0.3)              # Border width 1
p2

Cell Proportion Stacked Plot

## Cell Proportion Stacked Plot

A data.frame: 6 × 2
	group	celltype
	<chr>	<chr>
1	S150	T cell
2	S150	Mono_Macro
3	S150	T cell
4	S150	T cell
5	S150	T cell
6	S150	T cell

'T cell'
'Mono_Macro'
'NK'
'mDC'
'Plasma'
'Mast'
'B cell'
'pDC'

'S150'
'S133'
'S134'
'S135'
'S158'
'S159'
'S149'

# Calculate proportion of different cell types in each sample
tbl <- celltype_group_df %>% 
  group_by(group, celltype) %>%                  # Group by group and cell type
  summarise(Count = n()) %>%                     # Calculate cell count per group
  group_by(group) %>%                           # Group by group
  mutate(Percent = Count / sum(Count) * 100)    # Calculate percentage

output

\`summarise()\` has grouped output by 'group'. You can override using the
\`.groups\` argument.

# Draw first version stacked bar plot
options(repr.plot.height=8, repr.plot.width=8)
p3 <- ggplot(tbl, aes(x = group, fill = celltype, y = Percent)) +
  geom_bar(stat = "identity") +                 # Create stacked bar chart
  theme_bw() +                                  # Use black and white theme
  scale_fill_manual(values = celltype_colors) + # Use predefined colors
  xlab(NULL) +                                  # Hide x-axis title
  ylab("Percent(%)") +                         # y-axis title
  labs(fill = "celltype") +                    # Legend title
  # Custom theme settings
  theme(axis.text.x  = element_text(color = "black", vjust = 1, hjust = 1, angle = 45),
        panel.grid = element_blank(),           # Remove grid lines
        legend.text = element_text(size = 12),  # Legend text size
        legend.title = element_text(size = 12), # Legend title size
        axis.text = element_text(size = 14),    # Axis text size
        axis.title = element_text(size = 14))   # Axis title size
p3

# Reorder group levels
tbl$group <- factor(tbl$group, 
                      levels = c('S149','S134','S135','S133','S150','S158','S159'))
# Draw second version stacked bar plot (after reordering)
options(repr.plot.height=8, repr.plot.width=8)
p3 <- ggplot(tbl, aes(x = group, fill = celltype, y = Percent)) +
  geom_bar(stat = "identity") +                 
  theme_bw() +                                  
  scale_fill_manual(values = celltype_colors) + 
  xlab(NULL) +                                  
  ylab("Percent(%)") +                         
  labs(fill = "celltype") +                    
  theme(axis.text.x  = element_text(color = "black", vjust = 1, hjust = 1, angle = 45),
        panel.grid = element_blank(),           
        legend.text = element_text(size = 12),  
        legend.title = element_text(size = 12), 
        axis.text = element_text(size = 14),    
        axis.title = element_text(size = 14))   
p3

# Reorder cell types
tbl$celltype <- factor(tbl$celltype, 
                      levels = c('T cell','Mono_Macro','NK','mDC','Plasma','Mast','B cell','pDC'))
# Draw third version stacked bar plot (after reordering)
options(repr.plot.height=8, repr.plot.width=8)
p3 <- ggplot(tbl, aes(x = group, fill = celltype, y = Percent)) +
  geom_bar(stat = "identity") +                 
  theme_bw() +                                  
  scale_fill_manual(values = celltype_colors) + 
  xlab(NULL) +                                  
  ylab("Percent(%)") +                         
  labs(fill = "celltype") +                    
  theme(axis.text.x  = element_text(color = "black", vjust = 1, hjust = 1, angle = 45),
        panel.grid = element_blank(),           
        legend.text = element_text(size = 12),  
        legend.title = element_text(size = 12), 
        axis.text = element_text(size = 14),    
        axis.title = element_text(size = 14))   
p3

###################
# Save all plots #
###################

# Save tSNE plot
ggsave("tsne.pdf", plot = p1_new, width = 8, height = 8)
# Save cell count bar plot
ggsave("cellnumber.pdf", plot = p2, width = 12, height = 6)
# Save cell proportion stacked plot
ggsave("cellratio.pdf", plot = p3, width = 8, height = 8)

Single-cell Visualization Customization: Custom Color and Order t-SNE and Proportion Plot ​

Environment Setup ​

Data Loading ​

Data Filtering ​

t-SNE Visualization ​

Cell Count Plot ​

Cell Proportion Stacked Plot ​

Single-cell Visualization Customization: Custom Color and Order t-SNE and Proportion Plot

Environment Setup

Data Loading

Data Filtering

t-SNE Visualization

Cell Count Plot

Cell Proportion Stacked Plot