Practical Single-Cell RNA-seq Visualization with ggplot2

Last updated: 2025-11-02

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Introduction

Single-cell RNA-seq analysis packages like Seurat provide convenient wrapper functions for creating visualizations. While these wrappers are quick and easy to use, they can be limiting when you need to customize plots for publications or create novel visualizations.

The key insight: All Seurat wrapper functions are ultimately accessing data stored in the Seurat object and passing it to plotting functions. Once you understand how to extract this data into tidy dataframes, you can use ggplot2 to create any visualization you want with complete control.

In this tutorial, we’ll:

Analyze the classic PBMC 3k dataset following the Seurat tutorial
Compare Seurat’s wrapper functions with custom ggplot2 implementations
Show you how to access the underlying data from Seurat objects
Demonstrate the flexibility you gain with ggplot2

Learning objectives:

Understand where different data types are stored in Seurat objects
Extract metadata, expression data, and dimensional reductions into dataframes
Recreate common scRNA-seq plots using ggplot2
Gain the flexibility to create custom visualizations

Setup

knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE,
                      fig.width = 10, fig.height = 5)

# Load required packages
library(Seurat)
library(SeuratData)
library(ggplot2)
library(dplyr)
library(tidyr)
library(patchwork)  # For combining plots side-by-side

# Set a theme for consistent ggplot2 styling
theme_set(theme_bw(base_size = 12))

Load the PBMC 3k Dataset

The pbmc3k dataset contains 2,700 Peripheral Blood Mononuclear Cells (PBMCs) sequenced on the Illumina NextSeq 500. This is a widely-used example dataset for learning scRNA-seq analysis.

# Install the dataset if needed (uncomment the next line on first run)
# InstallData("pbmc3k")

# Load the dataset
data("pbmc3k")

# The object comes pre-loaded but let's look at it
#pbmc3k

pbmc3k<- UpdateSeuratObject(pbmc3k)

Understanding the Seurat object structure:

pbmc3k@assays - Contains expression matrices (counts, normalized data, scaled data)
pbmc3k@meta.data - Cell-level metadata (QC metrics, cluster assignments, etc.)
pbmc3k@reductions - Dimensional reductions (PCA, UMAP, etc.)

Quality Control Metrics

First, let’s calculate QC metrics. We’ll compute the percentage of mitochondrial genes per cell, which is a common QC metric.

# Calculate percentage of mitochondrial genes
pbmc3k[["percent.mt"]] <- PercentageFeatureSet(pbmc3k, pattern = "^MT-")

# View the metadata where QC metrics are stored
head(pbmc3k@meta.data)

               orig.ident nCount_RNA nFeature_RNA seurat_annotations percent.mt
AAACATACAACCAC     pbmc3k       2419          779       Memory CD4 T  3.0177759
AAACATTGAGCTAC     pbmc3k       4903         1352                  B  3.7935958
AAACATTGATCAGC     pbmc3k       3147         1129       Memory CD4 T  0.8897363
AAACCGTGCTTCCG     pbmc3k       2639          960         CD14+ Mono  1.7430845
AAACCGTGTATGCG     pbmc3k        980          521                 NK  1.2244898
AAACGCACTGGTAC     pbmc3k       2163          781       Memory CD4 T  1.6643551

Visualization 1: QC Violin Plots

Let’s compare Seurat’s VlnPlot() with a custom ggplot2 version.

# SEURAT WRAPPER VERSION
p1 <- VlnPlot(pbmc3k, features = c("nCount_RNA", "nFeature_RNA", "percent.mt"),
              ncol = 3, pt.size = 0.1)

# GGPLOT2 VERSION - Extracting data from Seurat object
# Key concept: Metadata is stored in pbmc3k@meta.data
qc_data <- pbmc3k@meta.data %>%
  select(nCount_RNA, nFeature_RNA, percent.mt) %>%
  mutate(cell_id = rownames(.)) %>%
  # Convert to long format for ggplot2
  pivot_longer(cols = c(nCount_RNA, nFeature_RNA, percent.mt),
               names_to = "metric",
               values_to = "value")

p2 <- ggplot(qc_data, aes(x = metric, y = value, fill = metric)) +
  geom_violin(scale = "width", trim = TRUE) +
  geom_jitter(size = 0.1, alpha = 0.3, width = 0.2) +
  facet_wrap(~ metric, scales = "free", ncol = 3) +
  labs(title = "QC Metrics (ggplot2 version)",
       x = NULL, y = "Value") +
  theme(legend.position = "none",
        axis.text.x = element_blank())

# Display side by side
p1/p2

What we learned:

QC metrics are stored in pbmc3k@meta.data as columns
We extracted the data, reshaped it to long format with pivot_longer()
Created violin plots with geom_violin() and overlaid points with geom_jitter()
Used facet_wrap() to create separate panels

Visualization 2: QC Scatter Plots

Feature-feature relationships help identify potential issues (e.g., correlation between UMI count and gene count).

# SEURAT WRAPPER VERSION
p1 <- FeatureScatter(pbmc3k, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") +
  labs(title = "Seurat wrapper")

# GGPLOT2 VERSION
# Again, we extract from metadata
scatter_data <- pbmc3k@meta.data %>%
  select(nCount_RNA, nFeature_RNA, percent.mt)

p2 <- ggplot(scatter_data, aes(x = nCount_RNA, y = nFeature_RNA)) +
  geom_point(alpha = 0.5, size = 0.5, color = "steelblue") +
  geom_smooth(method = "lm", color = "red", se = FALSE) +
  labs(title = "ggplot2 version",
       x = "Total UMI Counts",
       y = "Number of Genes Detected") +
  theme_bw()

# Display side by side
p1 + p2

What we learned:

Simple scatter plots require data from @meta.data
We can add trend lines with geom_smooth()
The patchwork package (+) makes side-by-side comparison easy

Filter, Normalize, and Find Variable Features

Now let’s continue with the standard Seurat workflow.

# Filter cells based on QC metrics
pbmc3k <- subset(pbmc3k, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)

# Normalize the data
pbmc3k <- NormalizeData(pbmc3k)

# Find variable features
pbmc3k <- FindVariableFeatures(pbmc3k, selection.method = "vst", nfeatures = 2000)

Visualization 3: Variable Feature Plot

This plot shows the most variable genes, which drive heterogeneity in the dataset.

# SEURAT WRAPPER VERSION
p1 <- VariableFeaturePlot(pbmc3k) +
  labs(title = "Seurat wrapper")

# GGPLOT2 VERSION
# Key concept: Variable feature info is stored in pbmc3k@assays$RNA@meta.features
# or we can access it with HVFInfo()
hvf_data <- HVFInfo(pbmc3k) %>%
  mutate(gene = rownames(.),
         variable = gene %in% VariableFeatures(pbmc3k))

# Get top 10 variable genes to label
top10_genes <- head(VariableFeatures(pbmc3k), 10)

p2 <- ggplot(hvf_data, aes(x = mean, y = variance.standardized)) +
  geom_point(aes(color = variable), alpha = 0.5, size = 0.8) +
  scale_color_manual(values = c("grey70", "red"),
                     labels = c("Non-variable", "Variable")) +
  geom_text(data = hvf_data %>% filter(gene %in% top10_genes),
            aes(label = gene), size = 3, nudge_y = 0.5) +
  labs(title = "ggplot2 version",
       x = "Average Expression",
       y = "Standardized Variance",
       color = "Feature Type") +
  theme_bw()

# Display side by side
p1 + p2

What we learned:

Variable feature information is accessed with HVFInfo()
Top variable genes are retrieved with VariableFeatures()
We can label specific genes with geom_text()
Color coding highlights different gene sets

Scaling and PCA

# Scale the data
pbmc3k <- ScaleData(pbmc3k)

# Run PCA
pbmc3k <- RunPCA(pbmc3k, features = VariableFeatures(pbmc3k))

Visualization 4: Elbow Plot

The elbow plot helps determine how many PCs to use for downstream analysis.

# SEURAT WRAPPER VERSION
p1 <- ElbowPlot(pbmc3k, ndims = 30) +
  labs(title = "Seurat wrapper")

# GGPLOT2 VERSION
# Key concept: PCA results are stored in pbmc3k@reductions$pca
# Standard deviations are in pbmc3k@reductions$pca@stdev
pca_variance <- data.frame(
  PC = 1:30,
  stdev = pbmc3k@reductions$pca@stdev[1:30]
) %>%
  mutate(variance_explained = stdev^2)

p2 <- ggplot(pca_variance, aes(x = PC, y = stdev)) +
  geom_point(size = 2, color = "steelblue") +
  geom_line(color = "steelblue") +
  labs(title = "ggplot2 version",
       x = "Principal Component",
       y = "Standard Deviation") +
  theme_bw()

# Display side by side
p1 + p2

What we learned:

PCA results are stored in pbmc3k@reductions$pca
Standard deviations are in the @stdev slot
We extracted PC variance and plotted it with geom_point() + geom_line()

Visualization 5: PCA Dim Plot

Visualize cells in PC space to see data structure.

# SEURAT WRAPPER VERSION
p1 <- DimPlot(pbmc3k, reduction = "pca", dims = c(1, 2)) +
  labs(title = "Seurat wrapper")

# GGPLOT2 VERSION
# Key concept: Cell embeddings are in pbmc3k@reductions$pca@cell.embeddings
pca_embeddings <- as.data.frame(pbmc3k@reductions$pca@cell.embeddings[, 1:2])
colnames(pca_embeddings) <- c("PC1", "PC2")

pca_embeddings<- cbind(pca_embeddings, pbmc3k@meta.data)

p2 <- ggplot(pca_embeddings, aes(x = PC1, y = PC2)) +
  geom_point(alpha = 0.5, size = 0.8, color = "red") +
  labs(title = "ggplot2 version",
       x = "PC 1", y = "PC 2") +
  theme_bw() +
  coord_fixed()  # Equal aspect ratio

# Display side by side
p1 + p2

What we learned:

Cell embeddings (coordinates in reduced dimensions) are in @reductions$pca@cell.embeddings
Each row is a cell, each column is a PC
We can easily switch which PCs to plot by selecting different columns

Clustering and UMAP

# Build nearest neighbor graph and cluster
pbmc3k <- FindNeighbors(pbmc3k, dims = 1:10)
pbmc3k <- FindClusters(pbmc3k, resolution = 0.5)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2638
Number of edges: 95927

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8728
Number of communities: 9
Elapsed time: 0 seconds

# Run UMAP
pbmc3k <- RunUMAP(pbmc3k, dims = 1:10)

Visualization 6: UMAP with Clusters

This is one of the most common scRNA-seq visualizations.

# SEURAT WRAPPER VERSION
p1 <- DimPlot(pbmc3k, reduction = "umap", label = TRUE) +
  labs(title = "Seurat wrapper") +
  NoLegend()

# GGPLOT2 VERSION
# Key concept: UMAP coordinates are in @reductions$umap@cell.embeddings
# Cluster assignments are in @meta.data$seurat_clusters
umap_data <- as.data.frame(pbmc3k@reductions$umap@cell.embeddings)
colnames(umap_data) <- c("UMAP1", "UMAP2")
umap_data$cluster <- pbmc3k@meta.data$seurat_clusters

# Calculate cluster centroids for labels
cluster_centers <- umap_data %>%
  group_by(cluster) %>%
  summarize(UMAP1 = median(UMAP1),
            UMAP2 = median(UMAP2))

p2 <- ggplot(umap_data, aes(x = UMAP1, y = UMAP2, color = cluster)) +
  geom_point(alpha = 0.6, size = 0.8) +
  geom_text(data = cluster_centers, aes(label = cluster),
            size = 5, color = "black", fontface = "bold") +
  labs(title = "ggplot2 version") +
  theme_bw() +
  theme(legend.position = "none") +
  coord_fixed()

# Display side by side
p1 + p2

What we learned:

UMAP coordinates are in @reductions$umap@cell.embeddings
Cluster assignments are in @meta.data$seurat_clusters
We can combine data from different slots
Calculated cluster centroids for label placement

Visualization 7: Feature Expression on UMAP

Show gene expression levels overlaid on UMAP.

# Let's look at some marker genes
genes <- c("CD3D", "CD14", "CD79A")

# SEURAT WRAPPER VERSION
p1 <- FeaturePlot(pbmc3k, features = genes, ncol = 1) +
  plot_annotation(title = "Seurat wrapper")

# GGPLOT2 VERSION
# Key concept: Expression data is in the assay, accessed with GetAssayData() or FetchData()
# FetchData() is convenient - it can pull from multiple sources at once

# Get UMAP coordinates and expression values
feature_data <- FetchData(pbmc3k, vars = c("umap_1", "umap_2", genes))

# Reshape for faceting
feature_long <- feature_data %>%
  pivot_longer(cols = all_of(genes),
               names_to = "gene",
               values_to = "expression")

p2 <- ggplot(feature_long, aes(x = umap_1, y = umap_2, color = expression)) +
  geom_point(alpha = 0.6, size = 0.5) +
  facet_wrap(~ gene, ncol = 1) +
  scale_color_gradientn(colors = c("lightgrey", "blue", "red", "darkred")) +
  labs(title = "ggplot2 version", color = "Expression") +
  theme_bw() +
  coord_fixed()

# Display side by side
p1 | p2

What we learned:

FetchData() is a powerful function that can retrieve:
- Dimensional reduction coordinates (UMAP_1, UMAP_2, PC_1, etc.)
- Gene expression values
- Metadata columns
- All in one convenient dataframe!
We reshaped the data to long format for faceting
Custom color scales with scale_color_gradientn()

Visualization 8: Split Visualization by Metadata

Sometimes you want to split visualizations by a metadata variable. Let’s add a mock “treatment” variable to demonstrate.

# Add a mock treatment variable (just for demonstration)
set.seed(42)
pbmc3k$treatment <- sample(c("Control", "Treated"), ncol(pbmc3k), replace = TRUE)

# SEURAT WRAPPER VERSION
p1 <- DimPlot(pbmc3k, reduction = "umap", split.by = "treatment") +
  plot_annotation(title = "Seurat wrapper")

# GGPLOT2 VERSION
# Extract UMAP coordinates, clusters, and metadata
umap_split <- as.data.frame(pbmc3k@reductions$umap@cell.embeddings)
colnames(umap_split) <- c("UMAP1", "UMAP2")
umap_split$cluster <- pbmc3k@meta.data$seurat_clusters
umap_split$treatment <- pbmc3k@meta.data$treatment

p2 <- ggplot(umap_split, aes(x = UMAP1, y = UMAP2, color = cluster)) +
  geom_point(alpha = 0.6, size = 0.5) +
  facet_wrap(~ treatment) +
  labs(title = "ggplot2 version") +
  theme_bw() +
  coord_fixed()

# Display
p1 / p2

What we learned:

Can combine multiple metadata columns with embedding coordinates
facet_wrap() enables splitting by any variable
Full control over layout and appearance

Finding Marker Genes

# Find markers for cluster 0
cluster0_markers <- FindMarkers(pbmc3k, ident.1 = 0, min.pct = 0.25)

# Find all markers
pbmc_markers <- FindAllMarkers(pbmc3k, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

# Get top 5 markers per cluster
top5_markers <- pbmc_markers %>%
  group_by(cluster) %>%
  top_n(n = 5, wt = avg_log2FC)

head(top5_markers, 15)

# A tibble: 15 × 7
# Groups:   cluster [3]
       p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene     
       <dbl>      <dbl> <dbl> <dbl>     <dbl> <fct>   <chr>    
 1 9.57e- 88       2.40 0.447 0.108 1.31e- 83 0       CCR7     
 2 1.35e- 51       2.14 0.342 0.103 1.86e- 47 0       LEF1     
 3 2.81e- 44       1.53 0.443 0.185 3.85e- 40 0       PIK3IP1  
 4 6.27e- 43       1.99 0.33  0.112 8.60e- 39 0       PRKCQ-AS1
 5 1.34e- 34       1.96 0.268 0.087 1.84e- 30 0       MAL      
 6 0               6.65 0.975 0.121 0         1       S100A8   
 7 0               6.18 0.996 0.215 0         1       S100A9   
 8 1.03e-295       5.98 0.667 0.027 1.42e-291 1       CD14     
 9 7.07e-139       7.28 0.299 0.004 9.70e-135 1       FOLR3    
10 3.38e-121       6.74 0.277 0.006 4.64e-117 1       S100A12  
11 3.44e- 59       1.63 0.651 0.245 4.71e- 55 2       CD2      
12 2.97e- 58       2.09 0.42  0.111 4.07e- 54 2       AQP3     
13 2.92e- 42       1.52 0.397 0.124 4.00e- 38 2       TRAT1    
14 5.03e- 34       1.87 0.263 0.07  6.90e- 30 2       CD40LG   
15 2.65e- 20       1.60 0.305 0.136 3.63e- 16 2       CORO1B

Visualization 9: Violin Plot by Cluster

Show expression of marker genes across clusters.

# Select a few top marker genes
selected_genes <- c("IL7R", "CCR7", "CD14", "LYZ", "MS4A1", "CD8A")

# SEURAT WRAPPER VERSION
p1 <- VlnPlot(pbmc3k, features = selected_genes, ncol = 2, pt.size = 0.1) +
  plot_annotation(title = "Seurat wrapper")

# GGPLOT2 VERSION
# Use FetchData to get cluster assignments and gene expression
violin_data <- FetchData(pbmc3k, vars = c("seurat_clusters", selected_genes))

# Reshape to long format
violin_long <- violin_data %>%
  pivot_longer(cols = all_of(selected_genes),
               names_to = "gene",
               values_to = "expression")

p2 <- ggplot(violin_long, aes(x = seurat_clusters, y = expression, fill = seurat_clusters)) +
  geom_violin(scale = "width", trim = TRUE) +
  facet_wrap(~ gene, scales = "free_y", ncol = 2) +
  labs(title = "ggplot2 version",
       x = "Cluster", y = "Expression Level") +
  theme_bw() +
  theme(legend.position = "none")

# Display
print(p1)

print(p2)

What we learned:

Combined cluster identity with gene expression using FetchData()
Used pivot_longer() to reshape for faceting
scales = "free_y" allows different y-axis scales per facet (useful when genes have different expression ranges)

Visualization 10: Marker Gene Heatmap

Heatmaps show expression patterns of marker genes across clusters.

# Get top 3 markers per cluster for a cleaner visualization
top3_markers <- pbmc_markers %>%
  group_by(cluster) %>%
  top_n(n = 3, wt = avg_log2FC)

# SEURAT WRAPPER VERSION
p1 <- DoHeatmap(pbmc3k, features = top3_markers$gene, size = 3, angle = 90) +
  labs(title = "Seurat wrapper") +
  theme(axis.text.y = element_text(size = 8))

# GGPLOT2 VERSION
# Key concept: We need the scaled expression matrix
# Get scaled data for the marker genes
scaled_expr <- GetAssayData(pbmc3k, layer = "scale.data")
markers_to_plot <- top3_markers$gene[top3_markers$gene %in% rownames(scaled_expr)]

# Extract scaled expression for these genes
heatmap_data <- as.data.frame(t(as.matrix(scaled_expr[markers_to_plot, ])))
heatmap_data$cluster <- pbmc3k@meta.data$seurat_clusters
heatmap_data$cell <- rownames(heatmap_data)

# Order cells by cluster
heatmap_data <- heatmap_data %>%
  arrange(cluster)

# Reshape to long format
heatmap_long <- heatmap_data %>%
  pivot_longer(cols = all_of(markers_to_plot),
               names_to = "gene",
               values_to = "expression")

# Create ordered factors for proper display
heatmap_long$cell <- factor(heatmap_long$cell, levels = unique(heatmap_data$cell))
heatmap_long$gene <- factor(heatmap_long$gene, levels = markers_to_plot)

p2 <- ggplot(heatmap_long, aes(x = cell, y = gene, fill = expression)) +
  geom_tile() +
  scale_fill_gradient2(low = "blue", mid = "white", high = "red",
                       midpoint = 0, limits = c(-2, 2)) +
  labs(title = "ggplot2 version", x = "Cells", y = "Genes", fill = "Scaled\nExpression") +
  theme_minimal() +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.text.y = element_text(size = 8),
        panel.grid = element_blank())

# Display
print(p1)

print(p2)

What we learned:

Scaled expression data is in GetAssayData(object, slot = "scale.data")
Genes are rows, cells are columns in the expression matrix
We transposed and reshaped the data for ggplot2
geom_tile() creates heatmaps
scale_fill_gradient2() creates a diverging color scale

Visualization 11: Dot Plot

Dot plots efficiently show both expression level (color) and percentage of cells expressing (size) a gene.

# Select genes to visualize
selected_genes <- c("IL7R", "CCR7", "CD14", "LYZ", "S100A4", "MS4A1",
                    "CD8A", "FCGR3A", "MS4A7", "GNLY", "NKG7")

# SEURAT WRAPPER VERSION
p1 <- DotPlot(pbmc3k, features = selected_genes) +
  labs(title = "Seurat wrapper") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# GGPLOT2 VERSION
# We need to calculate:
# 1. Average expression per cluster
# 2. Percentage of cells expressing per cluster

# Get expression data and cluster assignments
dotplot_data <- FetchData(pbmc3k, vars = c("seurat_clusters", selected_genes))

# Calculate summary statistics
dotplot_summary <- dotplot_data %>%
  pivot_longer(cols = all_of(selected_genes),
               names_to = "gene",
               values_to = "expression") %>%
  group_by(seurat_clusters, gene) %>%
  summarize(
    avg_expression = mean(expression),
    pct_expressed = sum(expression > 0) / n() * 100,
    .groups = "drop"
  )

p2 <- ggplot(dotplot_summary, aes(x = gene, y = seurat_clusters)) +
  geom_point(aes(size = pct_expressed, color = avg_expression)) +
  scale_color_gradient(low = "lightgrey", high = "red") +
  scale_size_continuous(range = c(0, 6)) +
  labs(title = "ggplot2 version",
       x = "Gene", y = "Cluster",
       color = "Average\nExpression",
       size = "% Expressed") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Display
p1 / p2

What we learned:

Dot plots require calculating summary statistics per group
Used group_by() and summarize() to compute averages and percentages
Mapped two variables to aesthetics: size and color
This demonstrates how ggplot2 gives you control over the calculation

Assign Cell Type Identity

Visualization 12: UMAP with Cell Type Labels

# SEURAT WRAPPER VERSION
p1 <- DimPlot(pbmc3k, reduction = "umap", group.by = "seurat_annotations", label = TRUE, repel = TRUE) +
  labs(title = "Seurat wrapper") +
  NoLegend()

# GGPLOT2 VERSION
umap_celltype <- as.data.frame(pbmc3k@reductions$umap@cell.embeddings)
colnames(umap_celltype) <- c("UMAP1", "UMAP2")
umap_celltype$celltype <- pbmc3k$seurat_annotations

# Calculate centroids for labels
celltype_centers <- umap_celltype %>%
  group_by(celltype) %>%
  summarize(UMAP1 = median(UMAP1),
            UMAP2 = median(UMAP2))

p2 <- ggplot(umap_celltype, aes(x = UMAP1, y = UMAP2, color = celltype)) +
  geom_point(alpha = 0.6, size = 0.8) +
  geom_text(data = celltype_centers, aes(label = celltype),
            size = 3, color = "black", fontface = "bold",
            check_overlap = TRUE) +
  labs(title = "ggplot2 version") +
  theme_bw() +
  theme(legend.position = "right") +
  coord_fixed() +
  NoLegend()

# Display
p1 + p2

What we learned:

Cell type annotations are just another metadata column
We accessed it the same way as cluster assignments
Full control over colors, label placement, and legend

Summary: Key Takeaways

Data Access Cheat Sheet

Data Type	Location	Access Method
Metadata (QC metrics, clusters, etc.)	`object@meta.data`	Direct access or `FetchData()`
Raw counts	`object@assays$RNA@counts`	`GetAssayData(slot = "counts")`
Normalized expression	`object@assays$RNA@data`	`GetAssayData(slot = "data")`
Scaled expression	`object@assays$RNA@scale.data`	`GetAssayData(slot = "scale.data")`
PCA embeddings	`object@reductions$pca@cell.embeddings`	Direct access or `FetchData("PC_1", "PC_2", ...)`
UMAP embeddings	`object@reductions$umap@cell.embeddings`	Direct access or `FetchData("UMAP_1", "UMAP_2")`
Variable features	Internal	`VariableFeatures()` or `HVFInfo()`
Combined data	Multiple sources	`FetchData()` - most convenient!

When to Use Seurat Wrappers vs ggplot2

Use Seurat wrappers when:

You need a quick exploratory visualization
The default styling meets your needs
You’re in the early stages of analysis

Use ggplot2 when:

You need publication-quality figures with specific styling
You want to combine data from multiple sources
You need custom calculations or transformations
You want to create novel visualizations not provided by Seurat
You need fine-grained control over every aspect of the plot

Final Thoughts

The power of understanding how to extract data from Seurat objects means you’re not limited to the visualizations provided by the package. You can:

Create entirely new plot types
Combine multiple data types in creative ways
Apply advanced statistical visualizations
Match journal or institutional style guidelines exactly
Integrate with other R packages and workflows

Practice exercise: Try recreating other Seurat plots we didn’t cover, such as:

RidgePlot() - Ridge plots for gene expression
VizDimLoadings() - Top genes in PC loadings
Custom multi-panel figures combining different data types

The key is always the same: understand where the data lives, extract it to a tidy dataframe, and use ggplot2 to visualize it!

Session Info

sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.2.0         tidyr_1.3.1             dplyr_1.1.4            
 [4] ggplot2_3.5.1           pbmc3k.SeuratData_3.1.4 SeuratData_0.2.2.9001  
 [7] Seurat_5.1.0            SeuratObject_5.0.2      sp_2.1-4               
[10] workflowr_1.7.1        

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3     rstudioapi_0.16.0      jsonlite_1.8.8        
  [4] magrittr_2.0.3         ggbeeswarm_0.7.2       spatstat.utils_3.1-0  
  [7] farver_2.1.2           rmarkdown_2.27         fs_1.6.4              
 [10] vctrs_0.6.5            ROCR_1.0-11            spatstat.explore_3.3-2
 [13] htmltools_0.5.8.1      sass_0.4.9             sctransform_0.4.1     
 [16] parallelly_1.38.0      KernSmooth_2.23-24     bslib_0.8.0           
 [19] htmlwidgets_1.6.4      ica_1.0-3              plyr_1.8.9            
 [22] plotly_4.10.4          zoo_1.8-12             cachem_1.1.0          
 [25] whisker_0.4.1          igraph_2.0.3           mime_0.12             
 [28] lifecycle_1.0.4        pkgconfig_2.0.3        Matrix_1.7-0          
 [31] R6_2.5.1               fastmap_1.2.0          fitdistrplus_1.2-1    
 [34] future_1.34.0          shiny_1.9.0            digest_0.6.36         
 [37] colorspace_2.1-1       ps_1.7.7               rprojroot_2.0.4       
 [40] tensor_1.5             RSpectra_0.16-2        irlba_2.3.5.1         
 [43] labeling_0.4.3         progressr_0.14.0       fansi_1.0.6           
 [46] spatstat.sparse_3.1-0  mgcv_1.9-1             httr_1.4.7            
 [49] polyclip_1.10-7        abind_1.4-5            compiler_4.4.1        
 [52] withr_3.0.0            fastDummies_1.7.4      highr_0.11            
 [55] MASS_7.3-60.2          rappdirs_0.3.3         tools_4.4.1           
 [58] vipor_0.4.7            lmtest_0.9-40          beeswarm_0.4.0        
 [61] httpuv_1.6.15          future.apply_1.11.2    goftest_1.2-3         
 [64] glue_1.8.0             callr_3.7.6            nlme_3.1-164          
 [67] promises_1.3.0         grid_4.4.1             Rtsne_0.17            
 [70] getPass_0.2-4          cluster_2.1.6          reshape2_1.4.4        
 [73] generics_0.1.3         gtable_0.3.5           spatstat.data_3.1-2   
 [76] data.table_1.15.4      utf8_1.2.4             spatstat.geom_3.3-2   
 [79] RcppAnnoy_0.0.22       ggrepel_0.9.5          RANN_2.6.1            
 [82] pillar_1.9.0           stringr_1.5.1          limma_3.60.4          
 [85] spam_2.10-0            RcppHNSW_0.6.0         later_1.3.2           
 [88] splines_4.4.1          lattice_0.22-6         survival_3.6-4        
 [91] deldir_2.0-4           tidyselect_1.2.1       miniUI_0.1.1.1        
 [94] pbapply_1.7-2          knitr_1.48             git2r_0.35.0          
 [97] gridExtra_2.3          scattermore_1.2        xfun_0.52             
[100] statmod_1.5.0          matrixStats_1.3.0      stringi_1.8.4         
[103] lazyeval_0.2.2         yaml_2.3.10            evaluate_0.24.0       
[106] codetools_0.2-20       tibble_3.2.1           cli_3.6.3             
[109] uwot_0.2.2             xtable_1.8-4           reticulate_1.38.0     
[112] munsell_0.5.1          processx_3.8.4         jquerylib_0.1.4       
[115] Rcpp_1.0.13            globals_0.16.3         spatstat.random_3.3-1 
[118] png_0.1-8              ggrastr_1.0.2          spatstat.univar_3.0-0 
[121] parallel_4.4.1         presto_1.0.0           dotCall64_1.1-1       
[124] listenv_0.9.1          viridisLite_0.4.2      scales_1.3.0          
[127] ggridges_0.5.6         crayon_1.5.3           leiden_0.4.3.1        
[130] purrr_1.0.2            rlang_1.1.4            cowplot_1.1.3

sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.2.0         tidyr_1.3.1             dplyr_1.1.4            
 [4] ggplot2_3.5.1           pbmc3k.SeuratData_3.1.4 SeuratData_0.2.2.9001  
 [7] Seurat_5.1.0            SeuratObject_5.0.2      sp_2.1-4               
[10] workflowr_1.7.1        

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3     rstudioapi_0.16.0      jsonlite_1.8.8        
  [4] magrittr_2.0.3         ggbeeswarm_0.7.2       spatstat.utils_3.1-0  
  [7] farver_2.1.2           rmarkdown_2.27         fs_1.6.4              
 [10] vctrs_0.6.5            ROCR_1.0-11            spatstat.explore_3.3-2
 [13] htmltools_0.5.8.1      sass_0.4.9             sctransform_0.4.1     
 [16] parallelly_1.38.0      KernSmooth_2.23-24     bslib_0.8.0           
 [19] htmlwidgets_1.6.4      ica_1.0-3              plyr_1.8.9            
 [22] plotly_4.10.4          zoo_1.8-12             cachem_1.1.0          
 [25] whisker_0.4.1          igraph_2.0.3           mime_0.12             
 [28] lifecycle_1.0.4        pkgconfig_2.0.3        Matrix_1.7-0          
 [31] R6_2.5.1               fastmap_1.2.0          fitdistrplus_1.2-1    
 [34] future_1.34.0          shiny_1.9.0            digest_0.6.36         
 [37] colorspace_2.1-1       ps_1.7.7               rprojroot_2.0.4       
 [40] tensor_1.5             RSpectra_0.16-2        irlba_2.3.5.1         
 [43] labeling_0.4.3         progressr_0.14.0       fansi_1.0.6           
 [46] spatstat.sparse_3.1-0  mgcv_1.9-1             httr_1.4.7            
 [49] polyclip_1.10-7        abind_1.4-5            compiler_4.4.1        
 [52] withr_3.0.0            fastDummies_1.7.4      highr_0.11            
 [55] MASS_7.3-60.2          rappdirs_0.3.3         tools_4.4.1           
 [58] vipor_0.4.7            lmtest_0.9-40          beeswarm_0.4.0        
 [61] httpuv_1.6.15          future.apply_1.11.2    goftest_1.2-3         
 [64] glue_1.8.0             callr_3.7.6            nlme_3.1-164          
 [67] promises_1.3.0         grid_4.4.1             Rtsne_0.17            
 [70] getPass_0.2-4          cluster_2.1.6          reshape2_1.4.4        
 [73] generics_0.1.3         gtable_0.3.5           spatstat.data_3.1-2   
 [76] data.table_1.15.4      utf8_1.2.4             spatstat.geom_3.3-2   
 [79] RcppAnnoy_0.0.22       ggrepel_0.9.5          RANN_2.6.1            
 [82] pillar_1.9.0           stringr_1.5.1          limma_3.60.4          
 [85] spam_2.10-0            RcppHNSW_0.6.0         later_1.3.2           
 [88] splines_4.4.1          lattice_0.22-6         survival_3.6-4        
 [91] deldir_2.0-4           tidyselect_1.2.1       miniUI_0.1.1.1        
 [94] pbapply_1.7-2          knitr_1.48             git2r_0.35.0          
 [97] gridExtra_2.3          scattermore_1.2        xfun_0.52             
[100] statmod_1.5.0          matrixStats_1.3.0      stringi_1.8.4         
[103] lazyeval_0.2.2         yaml_2.3.10            evaluate_0.24.0       
[106] codetools_0.2-20       tibble_3.2.1           cli_3.6.3             
[109] uwot_0.2.2             xtable_1.8-4           reticulate_1.38.0     
[112] munsell_0.5.1          processx_3.8.4         jquerylib_0.1.4       
[115] Rcpp_1.0.13            globals_0.16.3         spatstat.random_3.3-1 
[118] png_0.1-8              ggrastr_1.0.2          spatstat.univar_3.0-0 
[121] parallel_4.4.1         presto_1.0.0           dotCall64_1.1-1       
[124] listenv_0.9.1          viridisLite_0.4.2      scales_1.3.0          
[127] ggridges_0.5.6         crayon_1.5.3           leiden_0.4.3.1        
[130] purrr_1.0.2            rlang_1.1.4            cowplot_1.1.3

Practical Single-Cell RNA-seq Visualization with ggplot2

Your Name

2025-10-08

Introduction

Setup

Load the PBMC 3k Dataset

Quality Control Metrics

Visualization 1: QC Violin Plots

Visualization 2: QC Scatter Plots

Filter, Normalize, and Find Variable Features

Visualization 3: Variable Feature Plot

Scaling and PCA

Visualization 4: Elbow Plot

Visualization 5: PCA Dim Plot

Clustering and UMAP

Visualization 6: UMAP with Clusters

Visualization 7: Feature Expression on UMAP

Visualization 8: Split Visualization by Metadata

Finding Marker Genes

Visualization 9: Violin Plot by Cluster

Visualization 10: Marker Gene Heatmap

Visualization 11: Dot Plot

Assign Cell Type Identity

Visualization 12: UMAP with Cell Type Labels

Summary: Key Takeaways

Data Access Cheat Sheet

When to Use Seurat Wrappers vs ggplot2

Final Thoughts

Session Info