SeuratExplorer
An '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.
README
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
<!-- To generate README.md for GitHub homepage: copy this file to the package root directory, set for_github parameter to TRUE, then knit. Delete the README.Rmd file from the root directory after completion -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
# specify the directory where figures generated by code chunks in an R Markdown document should be saved
fig.path = "man/figures/README-",
out.width = "100%"
)
```
```{r echo=FALSE, results="hide", message=FALSE}
# Set to TRUE when generating README.md for GitHub (uses relative image paths); otherwise FALSE (uses absolute paths)
for_github <- FALSE
```
# SeuratExplorer
```{r eval=FALSE, echo=FALSE, results="hide", message=FALSE}
library(badger)
# backup some codes or badges
`r badge_lifecycle("stable")`
`r badge_cran_release("SeuratExplorer")`
`r badge_devel("fentouxungui/SeuratExplorer", "rossellhayes")`
`r badge_cran_download("SeuratExplorer", "grand-total")`
`r badge_code_size("fentouxungui/SeuratExplorer")`
```
<!-- badges: start -->
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://cran.r-project.org/package=SeuratExplorer)
[](https://github.com/fentouxungui/SeuratExplorer)
[](https://cran.r-project.org/package=SeuratExplorer)
[](https://github.com/fentouxungui/SeuratExplorer)
[](https://deepwiki.com/fentouxungui/SeuratExplorer)
[](https://zread.ai/fentouxungui/SeuratExplorer)
<!-- badges: end -->
> 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***:
```{r eval=FALSE}
if(!require(devtools)){install.packages("devtools")}
install_github("fentouxungui/SeuratExplorer", dependencies = TRUE)
```
Or install from CRAN:
```{r eval=FALSE}
# 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
```{r eval=FALSE}
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)
```{r eval=FALSE}
# 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](http://www.nibs.ac.cn:666/SeuratExplorer/). You can download sample demo data from [GitHub](https://github.com/fentouxungui/SeuratExplorerServer/blob/main/inst/extdata/source-data/fly/Rds-file/G101_PC20res04.rds).
```{r eval=FALSE}
# app.R
library(SeuratExplorer)
launchSeuratExplorer()
```
## Assay option
> [Seurat Assay](https://github.com/satijalab/seurat/wiki/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
```{r echo = FALSE, message=FALSE, warning=FALSE}
if(for_github){
knitr::include_graphics('./inst/extdata/www/upload-data.png')
}else{
knitr::include_graphics(system.file("extdata", "www/upload-data.png", package ="SeuratExplorer"), rel_path = FALSE)
}
```
### 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:**
```{r echo = FALSE, out.width="100%"}
if(for_github){
knitr::include_graphics(c('./inst/extdata/www/Dimplot-1.png',
'./inst/extdata/www/Dimplot-2.png',
'./inst/extdata/www/Dimplot-1-highlight.png'))
}else{
knitr::include_graphics(system.file("extdata",
c('www/Dimplot-1.png','www/Dimplot-2.png','www/Dimplot-1-highlight.png'),
package ="SeuratExplorer"), rel_path = FALSE)
}
```
### 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:**
```{r echo = FALSE, out.width="100%"}
if(for_github){
knitr::include_graphics(c('./inst/extdata/www/featureplot-1.png', './inst/extdata/www/featureplot-2.png', './inst/extdata/www/featureplot-3.png'))
}else{
knitr::include_graphics(system.file("extdata", c("www/featureplot-1.png",
"www/featureplot-2.png",
"www/featureplot-3.png"), package ="SeuratExplorer"), rel_path = FALSE)
}
```
### 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:**
```{r echo = FALSE, out.width="100%"}
if (for_github){
knitr::include_graphics(c("./inst/extdata/www/violin-1.png",
"./inst/extdata/www/violin-2.png",
"./inst/extdata/www/violin-3.png",
"./inst/extdata/www/violin-4.png",
"./inst/extdata/www/violin-5.png",
"./inst/extdata/www/violin-6.png",
"./inst/extdata/www/violin-7.png"))
}else{
knitr::include_graphics(system.file("extdata", c("www/violin-1.png",
"www/violin-2.png",
"www/violin-3.png",
"www/violin-4.png",
"www/violin-5.png",
"www/violin-6.png",
"www/violin-7.png"), package ="SeuratExplorer"), rel_path = FALSE)
}
```
### 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:**
```{r echo = FALSE, out.width="100%"}
if (for_github) {
knitr::include_graphics(c("./inst/extdata/www/dotplot.png",
"./inst/extdata/www/dotplot-1.png"))
}else{
knitr::include_graphics(system.file("extdata",
c("www/dotplot.png",
"www/dotplot-1.png")
, package ="SeuratExplorer"), rel_path = FALSE)
}
```
### 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:**
```{r echo = FALSE}
if (for_github) {
knitr::include_graphics("./inst/extdata/www/heatmap-cell-level.png")
}else{
knitr::include_graphics(system.file("extdata", "www/heatmap-cell-level.png", package ="SeuratExplorer"), rel_path = FALSE)
}
```
### 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:**
```{r echo = FALSE, out.width="100%"}
if (for_github) {
knitr::include_graphics("./inst/extdata/www/heatmap-group-level.png")
}else{
knitr::include_graphics(system.file("extdata", "www/heatmap-group-level.png", package ="SeuratExplorer"), rel_path = FALSE)
}
```
### 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:**
```{r echo = FALSE, out.width="100%"}
if (for_github) {
knitr::include_graphics(c("./inst/extdata/www/ridge-plot.png", "./inst/extdata/www/ridge-plot-2.png"))
}else{
knitr::include_graphics(system.file("extdata", c("www/ridge-plot.png",
"www/ridge-plot-2.png"),
package ="SeuratExplorer"), rel_path = FALSE)
}
```
### Plot Cell Percentage
- Supports faceting
- Supports adjusting the height/width ratio of the plot
- Supports downloading plots in PDF format (WYSIWYG)
**Example plots:**
```{r echo = FALSE, out.width="100%"}
if (for_github) {
knitr::include_graphics(c("./inst/extdata/www/cell-ratio.png", "./inst/extdata/www/cell-ratio-2.png"))
}else{
knitr::include_graphics(system.file("extdata", c("www/cell-ratio.png",
"www/cell-ratio-2.png"),
package ="SeuratExplorer"), rel_path = FALSE)
}
```
### 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:**
```{r echo = FALSE, out.width="50%"}
if (for_github) {
knitr::include_graphics("./inst/extdata/www/DEGs-1.png")
knitr::include_graphics("./inst/extdata/www/DEGs-2.png")
}else{
knitr::include_graphics(system.file("extdata", "www/DEGs-1.png", package ="SeuratExplorer"), rel_path = FALSE)
knitr::include_graphics(system.file("extdata", "www/DEGs-2.png", package ="SeuratExplorer"), rel_path = FALSE)
}
```
#### Output description
```{r echo = FALSE}
if (for_github) {
knitr::include_graphics("./inst/extdata/www/DEGs-4.jpg")
}else{
knitr::include_graphics(system.file("extdata", "www/DEGs-4.jpg", package ="SeuratExplorer"), rel_path = FALSE)
}
```
> [FindMarkers(object, ...)](https://satijalab.org/seurat/reference/findmarkers)
>
> 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.
```{r echo = FALSE, out.width="80%"}
if (for_github) {
knitr::include_graphics("./inst/extdata/www/Find-Top-Genes-by-Cell.jpg")
}else{
knitr::include_graphics(system.file("extdata", "www/Find-Top-Genes-by-Cell.jpg", package ="SeuratExplorer"), rel_path = FALSE)
}
```
#### 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
```{r echo = FALSE, out.width="80%"}
if (for_github) {
knitr::include_graphics("./inst/extdata/www/Find-Top-Genes-by-Mean-UMI-counts.jpg")
}else{
knitr::include_graphics(system.file("extdata", "www/Find-Top-Genes-by-Mean-UMI-counts.jpg", package ="SeuratExplorer"), rel_path = FALSE)
}
```
#### 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
```{r echo = FALSE, out.width="80%"}
if (for_github) {
knitr::include_graphics("./inst/extdata/www/gene-short-summary.jpg")
}else{
knitr::include_graphics(system.file("extdata", "www/gene-short-summary.jpg", package ="SeuratExplorer"), rel_path = FALSE)
}
```
#### 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
```{r echo = FALSE}
if (for_github) {
knitr::include_graphics("./inst/extdata/www/featurecorrelation.jpg")
}else{
knitr::include_graphics(system.file("extdata", "www/featurecorrelation.jpg", package ="SeuratExplorer"), rel_path = FALSE)
}
```
#### Output description
```{r echo = FALSE, out.width="40%"}
if (for_github) {
knitr::include_graphics("./inst/extdata/www/feature-correlation-output.jpg")
}else{
knitr::include_graphics(system.file("extdata", "www/feature-correlation-output.jpg", package ="SeuratExplorer"), rel_path = FALSE)
}
```
- ``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
```{r echo = FALSE, message=FALSE, warning=FALSE}
if(for_github){
knitr::include_graphics('./inst/extdata/www/cell-metadata.jpg')
} else{
knitr::include_graphics(system.file("extdata", "www/cell-metadata.jpg", package ="SeuratExplorer"), rel_path = FALSE)
}
```
### 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:
1. **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
2. **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.)
3. **File Formats:**
- `.rds`: Standard R data format (smaller files, faster I/O)
- `.qs2`: QSZ compression format (better compression for large objects)
4. **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
```{r eval=FALSE}
# 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](https://github.com/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](https://shiny.rstudio.com/): 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](https://ggplot2.tidyverse.org/): A system for declaratively creating graphics, based on "The Grammar of Graphics". SeuratExplorer uses ggplot2 for all its visualizations.
- [ComplexHeatmap](https://jokergoo.github.io/ComplexHeatmap/reference/Heatmap.html): Bioconductor package for making complex heatmaps with annotations.
- [shinydashboard](https://rstudio.github.io/shinydashboard/): Create dashboards with Shiny. SeuratExplorer uses this for its admin-style interface.
### Related Projects
- [Hla-Lab/SeuratExplorer](https://github.com/rwcrocker/SeuratExplorer/): An interactive R shiny application for exploring scRNAseq data processed in Seurat (another implementation with similar goals).
- [junjunlab/scRNAtoolVis](https://github.com/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](https://github.com/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](https://github.com/fentouxungui/SeuratExplorer/issues)
- **WeChat**: Follow [微信公众号：分析力工厂](https://mp.weixin.qq.com/s/lpvI9OnyN95amOeVGmeyMQ) for tutorials and updates in Chinese
### Citation
If you use SeuratExplorer in your research, please cite:
```bibtex
@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](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](https://mp.weixin.qq.com/s/lpvI9OnyN95amOeVGmeyMQ)
## Session Info
```{r echo = FALSE}
sessionInfo()
```
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.4 |
rolling linux/jammy R-4.5 | SeuratExplorer_0.1.4.tar.gz |
1.7 MiB |
0.1.4 |
rolling linux/noble R-4.5 | SeuratExplorer_0.1.4.tar.gz |
1.7 MiB |
0.1.4 |
rolling source/ R- | SeuratExplorer_0.1.4.tar.gz |
1.2 MiB |
0.1.4 |
latest linux/jammy R-4.5 | SeuratExplorer_0.1.4.tar.gz |
1.7 MiB |
0.1.4 |
latest linux/noble R-4.5 | SeuratExplorer_0.1.4.tar.gz |
1.7 MiB |
0.1.4 |
latest source/ R- | SeuratExplorer_0.1.4.tar.gz |
1.2 MiB |
0.1.4 |
2026-04-26 source/ R- | SeuratExplorer_0.1.4.tar.gz |
1.2 MiB |
0.1.4 |
2026-04-23 source/ R- | SeuratExplorer_0.1.4.tar.gz |
1.2 MiB |
0.1.4 |
2026-04-09 windows/windows R-4.5 | SeuratExplorer_0.1.4.zip |
1.7 MiB |
0.1.0 |
2025-04-20 source/ R- | SeuratExplorer_0.1.0.tar.gz |
1.2 MiB |