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 | <span style="color:red;">limited</span> | <span style="color:green;">yes</span> | <span style="color:green;">yes</span> | <span style="color:green;">yes</span> | | Multi-omics integration | <span style="color:red;">limited</span> | <span style="color:green;">yes</span> | <span style="color:green;">yes</span> | <span style="color:green;">yes</span> | | Cross-model queries | <span style="color:red;">limited</span> | <span style="color:green;">yes</span> | <span style="color:green;">yes</span> | <span style="color:green;">yes</span> | | 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 | <span style="color:red;">no</span> | <span style="color:green;">full</span> | | 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]( https://htmlpreview.github.io/?https://github.com/the-ouyang-lab/ShinyCe