ShinyCell2

ShinyCell2 is an enhanced R package for creating interactive, lightweight, and shareable web applications to explore single-cell multi-omics and spatial transcriptomics data. Building on the simplicity of the original ShinyCell, ShinyCell2 introduces powerful new features tailored for modern single-cell modalities, including support for CITE-seq, scATAC-seq, and spatial transcriptomics. It integrates seamlessly with popular analysis tools like Seurat, Scanpy, Signac, and ArchR, and offers advanced visualizations such as zoom-enabled UMAPs, IGV-style peak tracks, and spatial plots—all easily customizable and deployable with minimal dependencies. Designed for both computational and experimental researchers, ShinyCell2 empowers intuitive exploration, cross-modality comparison, and statistical analysis of high-dimensional data without requiring extensive coding or setup. An example web app showcasing the visualization of spatial transcriptomics, scATAC-seq, and CITE-seq datasets can be accessed at https://shinycell.ouyanglab.com/

If you are using ShinyCell2, please cite the biorxiv preprint. The manuscript is currently under review.

Key features of ShinyCell2 include:

• Supports seamless integration and visualization of single-cell multi-omics, peak-based, and spatial data for in-depth exploration.

• Converts data from leading analysis tools—including Scanpy, Seurat, Signac, and ArchR—into a lightweight, portable format suitable for local or web-based deployment, enabling open and accessible data sharing.

• Offers a rich collection of interactive, customizable, and publication-ready plots.

• Easily extendable, with support for user-defined visualizations through R for tailored analysis workflows.

We also compared ShinyCell2 with other popular single cell data visualisation tools, which further highlights the key features of ShinyCell2.

Feature	cellxgene	Vitessce	WebAtlas	ShinyCell2
Framework	JavaScript/Python/R	JavaScript/Python/R	Vitessce-based	R/Shiny
Spatial data support	limited	yes	yes	yes
Multi-omics integration	limited	yes	yes	yes
Cross-model queries	limited	yes	yes	yes
Configuration Complexity	low, via command line	high, via multiple joson files	low, via parameter file	low, via config object
Customisation	limited	extensive but req. expertise	limited	extensive and user-friendly
R/Bioconductor Integration	via cellxgenedp package	via vitessceR package	no	full
Deployment	primarily web-based	primarily web-based	primarily web-based	local and web-based
Local Deployment	yes via R	local http server	local http server	yes, via Rstudio / Rstudio server

Table of Contents and Additional Information / Tutorials

This readme is broken down into the following sections:

• Installation on how to install ShinyCell2

• Quick Start Guide to rapidly deploy a shiny app with a few lines of code

• Frequently Asked Questions

There are also additional information / tutorials as follows:

• Additional information on new visualisations tailored for spatial / scATAC-seq / multiomics

• Additional information on enhanced visualisation features

• Tutorial for creating a ShinyCell2 app for scRNA-seq or CITE-seq data

• Tutorial for creating a ShinyCell2 app for spatial data

• Tutorial for creating a ShinyCell2 app for ArchR-based scATAC-seq data

• Tutorial for creating a ShinyCell2 app for Signac-based scATAC-seq data

• Tutorial for creating a ShinyCell app containing several datasets

• Tutorial for customising ShinyCell aesthetics

• Instructions on how to deploy ShinyCell apps online

Installation

First, users can run the following code to check if the packages required by ShinyCell2 exist and install them if required:

reqPkg = c("data.table", "Matrix", "hdf5r", "reticulate", "R.utils", 
           "ggplot2", "gridExtra", "glue", "readr", "future", "RColorBrewer")
newPkg = reqPkg[!(reqPkg %in% installed.packages()[,"Package"])]
if(length(newPkg)){install.packages(newPkg)}

# If you are using Seurat object as input (for scRNA, multiomics, spatial), 
#   you can install Seurat as follows:
# install.packages("Seurat")

# If you are using Signac object for scATAC-seq, you can install Signac as follows:
# install.packages("Signac")

# If you are using ArchR object as input for scATAC-seq, visit
#   https://github.com/GreenleafLab/ArchR for ArchR's installation instruction

# If you are using h5ad file as input, run code below
# reticulate::py_install("anndata")

Furthermore, on the system where the Shiny app will be deployed, users can run the following code to check if the packages required by the Shiny app exist and install them if required:

reqPkg = c("shiny", "shinyhelper", "data.table", "Matrix", "DT", "magrittr", 
           "ggplot2", "ggrepel", "hdf5r", "ggdendro", "gridExtra", "ggpubr")
newPkg = reqPkg[!(reqPkg %in% installed.packages()[,"Package"])]
if(length(newPkg)){install.packages(newPkg)}

If one is deploying scATAC-seq / peak-based data, bwtools have to be installed for the track plots via the command line:

# Install dependencies for trackplot (on command line)
git clone https://github.com/CRG-Barcelona/libbeato.git
cd ./libbeato
git checkout 0c30432
./configure
make
make install
    
cd ..
git clone https://github.com/CRG-Barcelona/bwtool.git
cd ./bwtool
./configure
make
make check
make install

ShinyCell2 can then be installed from GitHub as follows:

devtools::install_github("the-ouyang-lab/ShinyCell2")

Quick Start Guide

In short, the ShinyCell2 package takes in an input single-cell object and generates a ShinyCell2 config scConf containing labelling and colour palette information for the single-cell metadata. The ShinyCell config and single-cell object are then used to generate the files and code required for the shiny app.

In this example, we will use single-cell CITE-seq data in the form of a Seurat object containing 162,000 PBMC cells measured with 228 antibodies, taken from https://satijalab.org/seurat/articles/multimodal_reference_mapping.html The Seurat object can be downloaded here.

A shiny app can be readily generated using the following code:

library(Seurat)
library(ShinyCell2)

seu <- readRDS("multimodal_pbmc.rds")
scConf <- createConfig(seu)
makeShinyFiles(seu,scConf, shiny.prefix="sc1", shiny.dir="shinyApp/")
makeShinyCodes(shiny.title = "PBMC multiomics", shiny.prefix="sc1",
               shiny.dir="shinyApp/")

The generated shiny app can then be found in the shinyApp/ folder (which is the default output folder). To run the app locally, use RStudio to open either server.R or ui.R in the shiny app folder and click on "Run App" in the top right corner. The shiny app can also be deployed online via online platforms e.g. shinyapps.io and Amazon Web Services (AWS) or be hosted via Shiny Server. For further details, refer to Instructions on how to deploy ShinyCell apps online.

More details on the various visualisations in the ShinyCell2 can be found in Additional information on new visualisations tailored for spatial / scATAC-seq / multiomics and Additional information on enhanced visualisation features

Frequently Asked Questions

• Q: How much memory / storage space does ShinyCell2 and the app consume?

  • A: The ShinyCell2 app consumes very little memory and is meant to be a heavy-duty app where multiple users can access the app simultaneously. Unlike typical R objects, the entire gene expression matrix is stored on disk and not on memory via the hdf5 file system. Also, the hdf5 file system offers superior file compression and takes up less storage space than native R file formats such as rds / Rdata files.
  • A: It should be noted that a large amount of memory is required when building the ShinyCell2 app. This is because the whole single-cell object has to be loaded onto memory and additional memory is required to generate the required files. From experience, a typical laptop with 8GB RAM can handle datasets around 30k cells while 16GB RAM machines can handle around 60k-70k cells for scRNA-seq data. More memory will be required for multiomics and especially scATAC-seq data. As a rule of thumb, if you are able to perform analysis e.g. dimension reduction on the machine, the machine should be able to build the ShinyCell2 app.

• Q: I have both RNA and integrated data in my Seurat object. How do I specify which gene expression assay to plot in the Shiny app?

  • A: Unlike the original ShinyCell, ShinyCell2 now supports multiple assays within a single ShinyCell2 app. Thus, both the RNA and integrated data will be incorporated and users can choose to visualise either assays or even compare their expression in the ShinyCell2 app.

• Q: What types of single-cell data can ShinyCell2 handle?

  • A: ShinyCell2 supports various multi-omics formats including CITE-seq, scATAC-seq, and both standard and spatial scRNA-seq data. It can seamlessly switch between different data modalities such as RNA expression and protein abundance.

• Q: How does ShinyCell2 improve upon its predecessor?

  • A: ShinyCell2 introduces enhanced features for multi-assay visualisation, improved plotting capabilities, and advanced analysis tools. It offers better UMAP visualisation with zooming, flexible data ordering, customisable colour scales, and integrated statistical analysis tools.

• Q: Can I deploy ShinyCell2 apps locally and on the web?

  • A: Yes, ShinyCell2 apps can be run locally using RStudio and can also be deployed online via platforms like shinyapps.io and Amazon Web Services (AWS), or hosted via Shiny Server.

• Q: What are the main visualisation features of ShinyCell2?

  • A: ShinyCell2 offers six common tabs: Zoom-enable DimRed, Side-by-side DimRed, Gene coexpression, Violinplot/Boxplot, Proportional plot, and Bubbleplot/Heatmap. It also has specific tabs for spatial data and scATAC-seq data.

• Q: How does ShinyCell2 handle spatial transcriptomics data?

  • A: ShinyCell2 provides two spatial-specific tabs: "Zoom-enable Spatial" and "Side-by-side Spatial", allowing users to visualise cell information or gene expression overlaid on tissue images with zooming capabilities.

• Q: What special features does ShinyCell2 offer for scATAC-seq data?

  • A: For scATAC-seq data, ShinyCell2 offers a "Track plot" feature to visualise open chromatin regions. It supports custom annotations (in .bed file) and flexible region selection by gene or chromosomal region.

• Q: How does ShinyCell2 compare to other single-cell visualisation tools?

  • A: Compared to tools like cellxgene and Vitessce, ShinyCell2 offers more extensive customisation options, full R/Bioconductor integration, and a user-friendly interface with low configuration complexity.