SeuratExplorer

A Shiny App for Exploring scRNA-seq Data Processed in Seurat

A simple, one-command package which runs an interactive dashboard capable of common visualizations for single cell RNA-seq. SeuratExplorer requires a processed Seurat object, which is saved as rds or qs2 file.

Why build this R package

Currently, there are no comprehensive tools for visualizing Seurat analysis results that are accessible to users without programming expertise. When bioinformatics analysts hand over results to end users, those without R programming knowledge often struggle to retrieve and re-analyze the data independently. SeuratExplorer is designed to bridge this gap by enabling such users to visualize and explore analysis results through an intuitive graphical interface. The only requirement for users is to configure R and RStudio on their computers and install SeuratExplorer. Alternatively, users can upload their Seurat object files to a server deployed with Shiny Server and SeuratExplorer for browser-based access.

Essentially, SeuratExplorer provides graphical interfaces for command-line tools from Seurat and other related packages, making complex analyses accessible without programming.

Key Features

No Coding Required: Interactive point-and-click interface for all analyses
Comprehensive Visualizations: 10+ plot types for exploring single-cell data
Multi-Assay Support: Works with scRNA-seq, scATAC-seq, spatial, and multi-omics data
Flexible Analysis: Find markers, explore correlations, and summarize features interactively
Publication-Ready Plots: Download high-quality PDF figures directly from the app
Batch Processing: Analyze multiple genes/clusters simultaneously
Real-time Results: See changes immediately as you adjust parameters
Data Export: Download analysis results in CSV format for further analysis

Installation

Install the latest version from github - Recommended:

if(!require(devtools)){install.packages("devtools")}
install_github("fentouxungui/SeuratExplorer", dependencies = TRUE)

Or install from CRAN:

# Install non-CRAN dependencies first
if (!require("BiocManager", quietly = TRUE)){
  install.packages("BiocManager")
}
BiocManager::install(c("ComplexHeatmap", "MAST", "limma", "DESeq2"))

# Install presto from GitHub
if(!require(devtools)){
  install.packages("devtools")
}
devtools::install_github("immunogenomics/presto")

# Install SeuratExplorer from CRAN
install.packages("SeuratExplorer")

System Requirements:

R (>= 4.1.0)
Seurat (>= 5.4.0)
SeuratObject (>= 5.3.0)
ggplot2 (>= 4.0.1)

Run app on local

library(SeuratExplorer)
launchSeuratExplorer()

You can customize the launch behavior with additional parameters:

verbose: Set to TRUE for debug messages (default: FALSE)
ReductionKeyWords: Keywords for dimension reduction options (default: c("umap","tsne"))
SplitOptionMaxLevel: Maximum levels for split options (default: 12)
MaxInputFileSize: Maximum upload file size in bytes (default: 20*1024^3, i.e., 20GB)

# Example with custom parameters
launchSeuratExplorer(
  verbose = TRUE,
  ReductionKeyWords = c("umap", "tsne", "pca"),
  MaxInputFileSize = 10*1024^3  # 10GB
)

Deploy on server

You can deploy this app on a Shiny Server, which allows users to visualize their data through a web interface by uploading the data to the server.

Live Demo: Upload an .rds or .qs2 file (up to 20GB) to the Demo Site. You can download sample demo data from GitHub.

# app.R
library(SeuratExplorer)
launchSeuratExplorer()

Assay option

Seurat Assay

The Assay class stores single cell data. For typical scRNA-seq experiments, a Seurat object will have a single Assay (“RNA”). This assay will also store multiple ‘transformations’ of the data, including raw counts (@counts slot), normalized data (@data slot), and scaled data for dimensional reduction (@scale.data slot).

SeuratExplorer supports assay switching, enabling compatibility with multiple data types, including:

Single-cell Modalities:

scRNA-seq: Usually the default ‘RNA’ assay containing gene expression data
scATAC-seq: Usually named with “ATAC” (chromatin accessibility) and “ACTIVITY” (derived gene activity scores)
Spatial Transcriptomics:
- Xenium data, usually named with ‘Xenium’
- Visium HD data, usually named with ‘Visium’
- Other spatial platforms (MERFISH, seqFISH, etc.)
CITE-seq: For antibody-derived tags (ADT) data, usually named with ‘ADT’ or ‘HTO’
Multi-omics: Any custom assay types following Seurat’s assay structure

Normalization Methods:

SCT assay: Using SCTransform normalization method
cellbender assay: Using cellbender background correction output
lsi assay: From Latent Semantic Indexing dimensionality reduction
Other custom normalization approaches

Assay Slots:

counts: Stores unnormalized data such as raw counts or TPMs
data: Normalized data matrix (log-normalized or other transformations)
scale.data: Scaled data matrix (used for dimensional reduction and visualization)

Assay Slot Support by Feature

Different visualization features support different assay slots:

Feature Plot: Supports counts, data, scale.data
Violin Plot: Supports counts, data, scale.data
Dot Plot: Supports data slot
Heatmap (Cell Level): Supports any available slot
Heatmap (Group Level): Supports data, scale.data
Ridge Plot: Supports counts, data, scale.data
DEGs Analysis: Supports counts, data
Top Expressed Features: Supports counts (by accumulation) or any slot (by cell)
Feature Summary: Supports data
Feature Correlation: Supports data

Introduction

Load data

Supports Seurat objects saved as .rds or .qs2 files
Supports data processed by Seurat V5 and older versions. Note: It may take some time to update older Seurat objects when loading data

Dimensional Reduction Plot

Supports selection of dimensionality reduction methods (UMAP, t-SNE, PCA, etc.)
Supports selection of cluster resolutions
Supports split plots
Supports highlighting selected clusters
Supports adjusting the height/width ratio of the plot
Supports displaying cluster labels
Supports adjusting label size
Supports adjusting point size
Supports downloading plots in PDF format (WYSIWYG)

Example plots:

Feature Plot

Supports simultaneous display of multiple genes (gene names are case-insensitive). Tip: Paste multiple gene names directly from Excel
Supports selection of dimensionality reduction methods
Supports split plots
Supports customizing colors for lowest and highest expression levels
Supports adjusting the height/width ratio of the plot
Supports adjusting point size
Supports downloading plots in PDF format (WYSIWYG)
Supports switching between assays containing any of the following slots: counts, data, scale.data

Example plots:

Violin Plot

Supports simultaneous display of multiple genes (gene names are case-insensitive). Tip: Paste multiple gene names directly from Excel
Supports selection of cluster resolutions
Supports split plots
Supports stacking and flipping plots, with color mapping selection
Supports adjusting point size and transparency
Supports adjusting font sizes on x and y axes
Supports adjusting the height/width ratio of the plot
Supports downloading plots in PDF format (WYSIWYG)
Supports switching between assays containing any of the following slots: counts, data, scale.data

Example plots:

Dot Plot

Supports simultaneous display of multiple genes (gene names are case-insensitive). Tip: Paste multiple gene names directly from Excel
Supports selection of cluster resolutions and subsetting clusters
Supports split plots
Supports clustering clusters
Supports rotating axes and flipping coordinates
Supports adjusting point size and transparency
Supports adjusting font sizes on x and y axes
Supports adjusting the height/width ratio of the plot
Supports downloading plots in PDF format (WYSIWYG)
Supports switching between assays containing the data slot

Example plots:

Heatmap for cell level expression

Supports simultaneous display of multiple genes (gene names are case-insensitive). Tip: Paste multiple gene names directly from Excel
Supports selection of cluster resolutions and reordering clusters
Supports adjusting font size and rotation angle of cluster labels, and flipping coordinates
Supports adjusting the height of group bars
Supports adjusting the gap size between groups
Supports adjusting the font size of gene names
Supports adjusting the height/width ratio of the plot
Supports downloading plots in PDF format (WYSIWYG)
Supports assay switching

Example plots:

Heatmap for group averaged expression

Supports simultaneous display of multiple genes (gene names are case-insensitive). Tip: Paste multiple gene names directly from Excel
Supports selection of cluster resolutions and reordering clusters
Supports adjusting font size and rotation angle of cluster labels
Supports adjusting the font size of gene names
Supports adjusting the height/width ratio of the plot
Supports downloading plots in PDF format (WYSIWYG)
Supports switching between assays containing any of the following slots: data, scale.data

Example plots:

Ridge Plot

Supports simultaneous display of multiple genes (gene names are case-insensitive). Tip: Paste multiple gene names directly from Excel
Supports selection of cluster resolutions and reordering clusters
Supports adjusting the number of columns
Supports stacking plots and color mapping
Supports adjusting font sizes on x and y axes
Supports adjusting the height/width ratio of the plot
Supports downloading plots in PDF format (WYSIWYG)
Supports switching between assays containing any of the following slots: counts, data, scale.data

Example plots:

Plot Cell Percentage

Supports faceting
Supports adjusting the height/width ratio of the plot
Supports downloading plots in PDF format (WYSIWYG)

Example plots:

Find Cluster Markers and DEGs Analysis

This analysis typically requires more computation time. Please wait patiently for results to complete. Important: Save your results before starting a new analysis, as previous results will be overwritten. Results can be downloaded in csv format.

Two analysis modes supported

Find markers for all clusters: Identifies marker genes for each cluster
Calculate DEGs for custom groups: Compare differential expression between two user-defined groups. You can subset cells before calculating DEGs between groups. By default, all cells from both groups are used

You can modify calculation parameters before starting the analysis.

Supports switching between assays containing any of the following slots: counts, data

Screen shots:

Output description

FindMarkers(object, …)

A data.frame with a ranked list of putative markers as rows, and associated statistics as columns (p-values, ROC score, etc., depending on the test used (test.use)). The following columns are always present:

avg_logFC: log fold-chage of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group

pct.1: The percentage of cells where the gene is detected in the first group

pct.2: The percentage of cells where the gene is detected in the second group

p_val_adj: Adjusted p-value, based on bonferroni correction using all genes in the dataset

Top Expressed Features

Highly expressed genes can reflect the main functions of cells. There are two approaches to identifying these genes: Find Top Genes by Cell identifies genes that are highly expressed in individual cells (even if only in a few cells), while Find Top Genes by Accumulated UMI counts tends to find genes with high accumulated expression across most cells in a cluster.

Supports assay switching

1. Find Top Genes by Cell

How it works

Step 1: For each cell, identify genes with high UMI percentage. For example, if a cell has 10,000 UMIs and the UMI percentage cutoff is set to 0.01, all genes with more than 10,000 × 0.01 = 100 UMIs are considered highly expressed in that cell.

Step 2: Summarize these genes for each cluster. First, collect all highly expressed genes in a cluster. For each gene, count the number of cells where it is highly expressed, and calculate the mean and median UMI percentage in those highly expressed cells.

Output description

celltype: The cluster name as defined by Choose A Cluster Resolution
total.cells: Total number of cells in this cluster
Gene: Gene that is highly expressed in at least 1 cell in this cluster
total.pos.cells: Number of cells that express this gene
total.UMI.pct: (All UMIs of this gene) / (Total UMIs of this cluster)
cut.Cells: Number of cells that highly express this gene
cut.pct.mean: Mean expression percentage in highly expressed cells
cut.pct.median: Median expression percentage in highly expressed cells

2. Find Top Genes by Mean UMI counts

For each cluster, calculate the top n highly expressed genes by mean UMI counts. Clusters with fewer than 3 cells will be skipped.

Supports switching between assays containing the counts slot

Output description

CellType: The cluster name as defined by Choose A Cluster Resolution
total.cells: Total number of cells in this cluster
Gene: The top n highly expressed genes
total.pos.cells: Number of cells that express this gene
MeanUMICounts: (Total accumulated UMI counts) / (Total cells of this cluster)
PCT: (Total accumulated UMI counts of the gene) / (Total UMIs of cluster cells)

Feature Summary

Summarize features of interest by cluster, including positive cell percentage and mean/median expression levels.

Supports switching between assays containing the data slot

Output description

celltype: The cluster name as defined by Choose A Cluster Resolution
TotalCells: Total number of cells in this cluster
Gene: The input genes
PCT: The percentage of cells expressing this gene in this cluster
Expr.mean: The mean normalized expression in this cluster
Expr.median: The median normalized expression in this cluster

Feature Correlation Analysis

Calculates correlation values for gene pairs within cells from a cluster. Supports both Pearson and Spearman correlation methods.

Supports switching between assays containing the data slot

Three analysis modes

Find Top Correlated Gene Pairs: Identifies the top 1000 correlated gene pairs
Find Correlated Genes for A Gene: Finds the most correlated genes for user-specified genes
Calculate Correlation for A Gene List: Calculates correlation values for all pairs in a user-provided gene list

Output description

GeneA: The first gene in a gene pair
GeneB: The second gene in a gene pair
correlation: The correlation value

Note: If no results are returned, this is typically because the input genes have very low expression levels. Very lowly expressed genes are removed before analysis.

Rename cluster names

You can rename cluster names, and changes will take effect immediately. However, the original Seurat object file is never modified. Once you close the session, the new annotations will be lost. You can download a mapping file of old names to new names and send it to a bioinformatician to request permanent changes to the Seurat object.

Search Features

All features (genes) extracted from the row names of the assay can be searched.

Supports switching between assays containing any of the following slots: counts, data, scale.data
Case-insensitive search for gene/feature names
View feature annotations for ATAC assays (requires Signac package)
Copy multiple feature names for use in other analysis modules

Cell Metadata

The metadata of all cells extracted from the meta.data slot of the Seurat object contains descriptive information for each cell, such as quality control metrics, cell type classifications, batch information, and experimental conditions. This metadata is crucial for organizing, filtering, integrating, and visualizing single-cell RNA-seq data.

Supports downloading cell metadata in csv format for further analysis

Structure of Seurat Object

The Seurat object is an S4 class in R designed to store and manage single-cell expression data and associated analyses. It is a highly structured and self-contained object, allowing for the integration of various data modalities and analytical results.

Key Slots and their Contents:

assays: This is a list containing one or more Assay objects. Each Assay object represents a specific type of expression data. Each Assay object itself contains slots like counts (raw data), data (normalized data), scale.data (scaled data), and meta.features (feature-level metadata).

meta.data: A data frame storing cell-level metadata. This includes information such as the number of features detected per cell (nFeature_RNA), original identity classes (orig.ident), and can be extended with additional information (e.g., cell type annotations, sample information).

active.assay: A character string indicating the name of the currently active or default assay for analysis.

active.ident: Stores the active cluster identity for each cell, typically resulting from clustering analyses.

reductions: A list of DimReduc objects, each representing a dimensionality reduction technique applied to the data (e.g., PCA, UMAP, tSNE). These objects store the lower-dimensional embeddings of the cells.

graphs: A list of Graph objects, typically storing nearest-neighbor graphs used in clustering and other analyses. images: For spatial transcriptomics data, this slot stores Image objects containing spatial image data and information linking spots to their physical locations.

project.name: A character string holding the name of the project.

misc: A list for storing miscellaneous information not fitting into other specific slots.

About

You’re reading the tutorial right now!

Data Preparation Tips

Preparing Your Seurat Object

For the best experience with SeuratExplorer, ensure your Seurat object contains:

Required Elements:
- At least one assay with data (RNA, ATAC, etc.)
- Dimensional reductions (umap, tsne, or pca)
- Cell metadata (meta.data slot) with cluster information
Recommended Elements:
- Multiple cluster resolutions (e.g., seurat_clusters at different resolutions)
- Sample or batch information in metadata
- Cell type annotations (can be added interactively in the app)
- Quality control metrics (nCount_RNA, nFeature_RNA, percent.mt, etc.)
File Formats:
- .rds: Standard R data format (smaller files, faster I/O)
- .qs2: QSZ compression format (better compression for large objects)
File Size Considerations:
- Maximum upload size: 20GB (configurable via MaxInputFileSize parameter)
- For large datasets, consider subsetting or using data with fewer cells
- Use .qs2 format for better compression of large objects

Example Preprocessing Code

# Basic Seurat preprocessing
library(Seurat)

# Load your data
seurat_obj <- Read10X(data.dir = "path/to/data")
seurat_obj <- CreateSeuratObject(counts = seurat_obj)

# Standard processing
seurat_obj <- NormalizeData(seurat_obj)
seurat_obj <- FindVariableFeatures(seurat_obj)
seurat_obj <- ScaleData(seurat_obj)
seurat_obj <- RunPCA(seurat_obj)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30)

# Clustering at multiple resolutions
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.4)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.8)

# Save for SeuratExplorer
saveRDS(seurat_obj, "my_seurat_object.rds")
# or
qs2::qs_save(seurat_obj, "my_seurat_object.qs2")

FAQ

Q: Can I use SeuratExplorer with Seurat v3 objects?

A: Yes! SeuratExplorer automatically updates old Seurat objects when loading. However, for very old versions (v2 or earlier), manual update using UpdateSeuratObject() may be required before use.

Q: Is my data uploaded to any server?

A: No. When running locally (launchSeuratExplorer()), all data stays on your computer. Only when deployed on a Shiny Server would data be uploaded to that server.

Q: Can I save my analysis results?

A: Yes! Most visualizations can be downloaded as PDF files. Analysis results (DEGs, feature summaries, etc.) can be downloaded as CSV files. Cluster name mappings can also be exported.

Q: What’s the difference between the two “Top Expressed Features” methods?

A: - Find Top Genes by Cell: Identifies genes that are highly expressed in individual cells, useful for finding cell-specific markers - Find Top Genes by Mean UMI: Finds genes with high average expression across all cells in a cluster, useful for identifying cluster characteristics

Q: How do I add custom color palettes?

A: While SeuratExplorer includes many predefined color palettes, you can use the getColors() function in your own R scripts to access these palettes for custom visualizations.

Key related packages

Core Dependencies

satijalab/seurat: Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC. SeuratExplorer builds upon Seurat’s powerful analysis capabilities to provide interactive visualization.
rstudio/shiny: Shiny is an R package that makes it easy to build interactive web apps straight from R. SeuratExplorer uses Shiny to create its interactive dashboard.

Visualization Dependencies

ggplot2: A system for declaratively creating graphics, based on “The Grammar of Graphics”. SeuratExplorer uses ggplot2 for all its visualizations.
ComplexHeatmap: Bioconductor package for making complex heatmaps with annotations.
shinydashboard: Create dashboards with Shiny. SeuratExplorer uses this for its admin-style interface.

Related Projects

Hla-Lab/SeuratExplorer: An interactive R shiny application for exploring scRNAseq data processed in Seurat (another implementation with similar goals).
junjunlab/scRNAtoolVis: Some useful functions to make your scRNA-seq plots more beautiful. Some code from this package has been adapted in SeuratExplorer.
rstudio/shiny-server: Shiny Server is a server program that makes Shiny applications available over the web. Use this to deploy SeuratExplorer on a server for multi-user access.

Contributing and Support

Getting Help

Documentation: See function documentation with ?function_name in R
Issues: Report bugs or request features at GitHub Issues
WeChat: Follow 微信公众号：分析力工厂 for tutorials and updates in Chinese

Citation

If you use SeuratExplorer in your research, please cite:

@software{seuratexplorer2025,
  title = {SeuratExplorer: An Shiny App for Exploring scRNA-seq Data Processed in Seurat},
  author = {Zhang, Yongchao},
  year = {2025},
  url = {https://github.com/fentouxungui/SeuratExplorer},
  note = {R package version 0.1.3}
}

License

This package is licensed under GPL (>= 3). See LICENSE.md for details.

Acknowledgments

SeuratExplorer is built upon excellent work by:

The Seurat development team (Satija Lab)
The RStudio/Shiny team
The Bioconductor community
All contributors and users who provide feedback and suggestions

中文介绍

微信公众号：分析力工厂

Session Info

#> R version 4.4.3 (2025-02-28 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26200)
#> 
#> Matrix products: default
#> 
#> 
#> locale:
#> [1] LC_COLLATE=Chinese (Simplified)_China.utf8 
#> [2] LC_CTYPE=Chinese (Simplified)_China.utf8   
#> [3] LC_MONETARY=Chinese (Simplified)_China.utf8
#> [4] LC_NUMERIC=C                               
#> [5] LC_TIME=Chinese (Simplified)_China.utf8    
#> 
#> time zone: Asia/Shanghai
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] compiler_4.4.3    fastmap_1.2.0     cli_3.6.5         tools_4.4.3      
#>  [5] htmltools_0.5.9   otel_0.2.0        rstudioapi_0.17.1 yaml_2.3.12      
#>  [9] rmarkdown_2.30    knitr_1.51        xfun_0.55         digest_0.6.39    
#> [13] rlang_1.1.6       evaluate_1.0.5

Name		Name	Last commit message	Last commit date
Latest commit History 171 Commits
R		R
data		data
inst/extdata		inst/extdata
man		man
tutorials		tutorials
.Rbuildignore		.Rbuildignore
.gitattributes		.gitattributes
.gitignore		.gitignore
CITATION.cff		CITATION.cff
DESCRIPTION		DESCRIPTION
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
README.md		README.md
SeuratExplorer.Rproj		SeuratExplorer.Rproj
before-submit-to-CRAN.md		before-submit-to-CRAN.md

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SeuratExplorer

Why build this R package

Key Features

Installation

Run app on local

Deploy on server

Assay option

Assay Slot Support by Feature

Introduction

Load data

Dimensional Reduction Plot

Feature Plot

Violin Plot

Dot Plot

Heatmap for cell level expression

Heatmap for group averaged expression

Ridge Plot

Plot Cell Percentage

Find Cluster Markers and DEGs Analysis

Two analysis modes supported

Output description

Top Expressed Features

1. Find Top Genes by Cell

How it works

Output description

2. Find Top Genes by Mean UMI counts

Output description

Feature Summary

Output description

Feature Correlation Analysis

Three analysis modes

Output description

Rename cluster names

Search Features

Cell Metadata

Structure of Seurat Object

About

Data Preparation Tips

Preparing Your Seurat Object

Example Preprocessing Code

FAQ

Key related packages

Core Dependencies

Visualization Dependencies

Related Projects

Contributing and Support

Getting Help

Citation

License

Acknowledgments

中文介绍

Session Info

About

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