<i>ShinyCellPlus: an improved ShinyCell application for the interactive visualization of single cell dat</i>

Drew Moore; Bryan Granger; Stefano Berto

doi:10.12688/f1000research.161076.1

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Software Tool Article

ShinyCellPlus: an improved ShinyCell application for the interactive visualization of single cell dat

[version 1; peer review: 2 approved with reservations]

Drew Moore^1,2, Bryan Granger^1,2, Stefano Berto ^1,2

PUBLISHED 27 Mar 2025

Author details Author details

¹ Neuroscience, MUSC Health, Charleston, South Carolina, 29403, USA
² Bioinformatic Core, MUSC Health, Charleston, South Carolina, 29403, USA

Drew Moore
Roles: Conceptualization, Data Curation, Methodology

Bryan Granger
Roles: Conceptualization, Data Curation, Methodology, Supervision

Stefano Berto
Roles: Supervision, Writing – Original Draft Preparation

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Bioinformatics gateway.

Abstract

Motivation

With the growing complexity of single-cell data, there is a need for more sophisticated sharing tools. To address this, we developed ShinyCellPlus, an enhanced version of ShinyCell, that offers advanced, interactive visualizations and functionalities for single-cell RNA-seq data

Results

The library facilitates single-cell data visualization, shares tab-separated tables from marker detection or differential expression analyses, implements functional enrichment analysis, and provides easy access to gene set enrichments.

Availability and implementation

Code and documentation of ShinyCellPlus are currently available at https://github.com/BioinformaticsMUSC/ShinyCellPlus

Keywords

ShinyApp, Single cell, scRNA-seq

Corresponding author: Stefano Berto

Competing interests: No competing interests were disclosed.

Grant information: The study was supported by the CNDD Genomics and Bioinformatics Core (P20 GM148302)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2025 Moore D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Moore D, Granger B and Berto S. ShinyCellPlus: an improved ShinyCell application for the interactive visualization of single cell dat [version 1; peer review: 2 approved with reservations]. F1000Research 2025, 14:338 (https://doi.org/10.12688/f1000research.161076.1) First published: 27 Mar 2025, 14:338 (https://doi.org/10.12688/f1000research.161076.1) Latest published: 27 Mar 2025, 14:338 (https://doi.org/10.12688/f1000research.161076.1)

1 Introduction

Single-cell RNA sequencing (scRNA-seq) technologies have revolutionized molecular biology by enabling transcriptome profiling at single-cell resolution.^1–4 Additionally, substantial efforts have been made to develop computational pipelines and tools to improve the analysis and visualization of large scRNA-seq data sets. These advances have driven significant innovation in computational biology of over 1500 tools and various frameworks and software repositories, such as Bioconductor, Seurat, and Scanpy.^5–9

As the community continues to publish and deposit single cell datasets, the need to facilitate their sharing among collaborators has become increasingly important. Web-based and open-source frameworks have emerged as valuable tools for sharing results of single cell experiments with both intramural and extramural collaborators. For instance, stand-alone software such as iS-CellR, ASAP, SingleCAnalyzer, ShIVA, singleCellTK provide graphical interfaces for single cell RNA-seq datasets.^10–14 Other open-source methods based on the R shiny framework such as ShinyCell, SCHNAPPs, SeuratV3Wizard, and iSEE have become a free-to-use solution to share results for computational biologists.^15–18 With the increasing adoption of single-cell experimental approaches generating more complex data, it is necessary to improve methods of data sharing to accommodate the growing complexity of single-cell datasets.

Taking advantage of the Shiny framework (https://shiny.rstudio.com/), here we present ShinyCellPlus, an improved Shiny/R application based on ShinyCell¹⁶ and R/Bioconductor packages as ggvolc, ggplot2, and scToppR,¹⁹ to provide interactive visualizations of single cell RNA-seq results with specific and upgraded functionalities. ShinyCellPlus is an exploratory tool that allows scientists to share single cell data with collaborators or the scientific community. The R package and code are available at https://github.com/BioinformaticsMUSC/ShinyCellPlus.

2 Implementation

2.1 ShinyCellPlus overview

ShinyCellPlus was developed to expand the capabilities of the ShinyCell package, enabling more streamlined visualization of single-cell RNA-seq data. It achieves this by deploying a Shiny web app that can be used either on specific websites or locally. ShinyCellPlus relies on a Seurat object containing relevant single-cell genomic data and metadata files with all the necessary variables for visualizations. To maximize the app's functionality, we recommend converting SingleCellExperiment objects into Seurat objects before deployment. The user interface (UI) is organized with tabs and modules at the top, while specifications, thresholds, and other visualization options are located on the left-hand sidebar, preserving the original ShinyCell UI structure. To enhance the ShinyCell package, we incorporated additional miscellaneous data into the Seurat object based on single-cell analysis. This allows users to quickly identify cell-type markers, examine genes differentially expressed by cell type, and generate intuitive visualizations (Fig. 1) that can be downloaded in different formats. The Shiny app and visualization methods are compatible with modern browsers and have undergone testing on Google Chrome (v98.0.4758.80) and Firefox (v96.0).

Figure 1. The ShinyCellPlus upgrades. Screenshots of the new and upgraded functionalities.

(A) Example of the “split-by” new sections.

(B) Visualization of the markers table new sections. Similarly, two additional sections report the differential expression tables based on the analysis performed.

(C) Visualization section for the genes differentially expressed. Volcano plot can be modified using the left section with visualizations and thresholds parameters.

(D) Visualization section for the gene enrichment based on AUCell analysis.

2.2 Split by conditions and visualizations

To facilitate comparative visualizations, ShinyCellPlus includes specific functionalities to split the Seurat data by sample or conditions.

• Gene Dual Coexpression - Added a second interactive dimensional reduction plot to express the relationship between two genes along with the first plot.
• Split Dataset: Cell Info - Allows for comparison of conditions and other categorial data stored in the Seurat metadata.
• Split Dataset: Gene Expression - Allows for comparison of conditions stored in the Seurat metadata with selection of gene expression on both graphs.
• Split Dataset: Gene Coexpression - Allows for comparison of conditions stored in the Seurat metadata with selection of two genes for coexpression on both graphs.

2.3 Marker genes and differential expression tables

ShinyCellPlus enables the interactive visualization of marker genes and differential expression (DEG) statistics. To achieve this, ShinyCellPlus requires a tab-separated input for both markers and DEGs. To calculate markers and differential expression, we provided two examples based on the R packages Presto (https://github.com/immunogenomics/presto), Libra (https://github.com/neurorestore/Libra), or FindMarkers() function in Seurat. Presto uses an optimized Wilcoxon test to compute cell-type or cluster markers, and the resulting tables can be stored in the designated section called “misc” within a new Seurat object. Differential expression (DEG) statistics can be computed using the run_de() function from the R package Libra or the FindMarkers() function from the R package Seurat. To streamline the incorporation of the resulting tables into the Seurat object, we have developed a function called add_libra_DE_table_to_seurat(), which can be found in our GitHub repository (https://github.com/BioinformaticsMUSC/ShinyCellPlus). The Marker table and DEG table are presented interactively, with a drop-down menu that allows users to select cell types and different DEG tables from various comparisons. For the display of interactive tables, we utilize the R package DT (https://rstudio.github.io/DT/). Additionally, we offer an interactive volcano plot for a visual exploration of DEG statistics as well as interactive balloon and cluster dot plots visualizing gene ontology queries from ToppGene (https://toppgene.cchmc.org/). The volcano plot is created using the R package ggvolc (https://github.com/loukesio/ggvolc), and the gene ontology balloon and cluster dot plots (Fig. 2) are made by using R package scToppR (https://github.com/BioinformaticsMUSC/scToppR/), an API for ToppGene.

Figure 2. ToppGene gene ontology section.

(A) Cluster ontology dotplots; to allow for a large number of clusters, the canvas’ height and width are adjustable, along with image exports height and width.

(B) Ontology balloon plot showing most significant ontological category for cluster.

2.4 Gene set enrichment

For gene set signatures, ShinyCellPlus employs the R package AUCell (https://github.com/aertslab/AUCell).²⁰ AUCell enables the identification of cells displaying active gene sets, such as signatures or gene modules, within single-cell RNA-seq data. AUCell statistics can be stored in a designated section called "aucell" within a new Seurat object. In ShinyCellPlus, users can input lists of genes to be plotted interactively, allowing them to quickly visualize relevant gene modules.

3 Operation

An RStudio environment is recommended for ease of running the Shiny output files locally. The package’s DESCRIPTION file contains ‘Depends’ and ‘Imports’ lists for all external packages used within the creation of the Shiny app files as well as what is required when running said files through Shiny. Runtime can be memory intensive depending upon the data contained within the initial input Seurat object. Often there is lag from when the app’s HTML initially loads to when the first graphs appear, depending upon the size of the associated data files, but subsequent graph updates are much quicker and responsive. Instructions for general operation of the package can be found within the GitHub’s README.

4 Conclusions

ShinyCellPlus stands out as an intuitive and graphical tool designed for single-cell RNA-seq, enhancing the already excellent ShinyCell R package. This advanced, user-friendly app, built on the widely adopted Seurat pipeline, provides a more sophisticated platform for single-cell analyses. Our team is committed to improving and expanding the capabilities of ShinyCellPlus in response of the growing interest in single-cell omics and friendly user's apps. It not only generates publication-ready plots and tables but also allows users to interactively explore single-cell data and intuitively examine individual genes of interest.

Software availability

Source code available from: https://github.com/BioinformaticsMUSC/ShinyCellPlus

License: GPL-3.0 License

Archived source code at time of publication: https://doi.org/10.5281/zenodo.14713623

License: GNU General Public License v3.0

Ethics and consent

Ethical approval and consent were not required.

Data availability

All data underlying the results are available as part of the article and no additional source data are required.

Acknowledgments

D.M. developed, implemented, and improved the code with help from B.G. D.M., B.G., and S.B. wrote the manuscript. Special thanks to John F. Ouyang for the pioneer work on the ShinyCell and kind help for ShinyCellPlus.

References

1. Gulati GS, D'Silva JP, Liu Y, et al.: Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat. Rev. Mol. Cell Biol. 2025; 26: 11–31. PubMed Abstract | Publisher Full Text
2. Baysoy A, Bai Z, Satija R, et al.: The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 2023; 24: 695–713. PubMed Abstract | Publisher Full Text | Free Full Text
3. Aldridge S, Teichmann SA: Single cell transcriptomics comes of age. Nat. Commun. 2020; 11: 4307. PubMed Abstract | Publisher Full Text | Free Full Text
4. Stegle O, Teichmann SA, Marioni JC: Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 2015; 16: 133–145. PubMed Abstract | Publisher Full Text
5. Stuart T, Srivastava A, Madad S, et al.: Single-cell chromatin state analysis with Signac. Nat. Methods. 2021; 18: 1333–1341. PubMed Abstract | Publisher Full Text
6. Zappia L, Phipson B, Oshlack A: Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database. PLoS Comput. Biol. 2018; 14: e1006245. PubMed Abstract | Publisher Full Text | Free Full Text
7. Wolf FA, Angerer P, Theis FJ: SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018; 19: 15. PubMed Abstract | Publisher Full Text | Free Full Text
8. Lun AT, McCarthy DJ, Marioni JC: A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 2016; 5: 2122. PubMed Abstract | Publisher Full Text
9. Satija R, Farrell JA, Gennert D, et al.: Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 2015; 33: 495–502. PubMed Abstract | Publisher Full Text
10. Aussel R, Asif M, Chenag S, et al.: ShIVA: a user-friendly and interactive interface giving biologists control over their single-cell RNA-seq data. Sci. Rep. 2023; 13: 14377. PubMed Abstract | Publisher Full Text | Free Full Text
11. David FPA, Litovchenko M, Deplancke B, et al.: ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses. Nucleic Acids Res. 2020; 48: W403–W414. PubMed Abstract | Publisher Full Text | Free Full Text
12. Patel MV: iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data. Bioinformatics. 2018; 34: 4305–4306. PubMed Abstract | Publisher Full Text | Free Full Text
13. Prieto C, Barrios D, Villaverde A: SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud. Front Bioinform. 2022; 2: 793309. PubMed Abstract | Publisher Full Text | Free Full Text
14. Wang Y, Sarfraz I, Pervaiz N, et al.: Interactive analysis of single-cell data using flexible workflows with SCTK2. Patterns (N Y). 2023; 4: 100814. PubMed Abstract | Publisher Full Text | Free Full Text
15. Jagla B, Libri V, Chica C, et al.: SCHNAPPs - Single Cell sHiNy APPlication(s). J. Immunol. Methods. 2021; 499: 113176. PubMed Abstract | Publisher Full Text
16. Ouyang JF, Kamaraj US, Cao EY, et al.: ShinyCell: simple and sharable visualization of single-cell gene expression data. Bioinformatics. 2021; 37: 3374–3376. PubMed Abstract | Publisher Full Text
17. Rue-Albrecht K, Marini F, Soneson C: Lun ATL: iSEE: Interactive SummarizedExperiment Explorer. F1000Res. 2018; 7: 741. PubMed Abstract | Publisher Full Text | Free Full Text
18. Yousif A, Drou N, Rowe J, et al.: NASQAR: a web-based platform for high-throughput sequencing data analysis and visualization. BMC Bioinformatics. 2020; 21: 267. Publisher Full Text
19. Granger B, Berto S: scToppR: a coding-friendly R interface to ToppGene. Bioinformatics. 2024; 40. Publisher Full Text
20. Aibar S, Gonzalez-Blas CB, Moerman T, et al.: SCENIC: single-cell regulatory network inference and clustering. Nat. Methods. 2017; 14: 1083–1086. PubMed Abstract | Publisher Full Text

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Version 1

VERSION 1 PUBLISHED 27 Mar 2025

Author details Author details

¹ Neuroscience, MUSC Health, Charleston, South Carolina, 29403, USA
² Bioinformatic Core, MUSC Health, Charleston, South Carolina, 29403, USA

Drew Moore
Roles: Conceptualization, Data Curation, Methodology

Bryan Granger
Roles: Conceptualization, Data Curation, Methodology, Supervision

Stefano Berto
Roles: Supervision, Writing – Original Draft Preparation

Competing interests

No competing interests were disclosed.

Grant information

The study was supported by the CNDD Genomics and Bioinformatics Core (P20 GM148302)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (1)

version 1

Published: 27 Mar 2025, 14:338

https://doi.org/10.12688/f1000research.161076.1

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© 2025 Moore D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Moore D, Granger B and Berto S. ShinyCellPlus: an improved ShinyCell application for the interactive visualization of single cell dat [version 1; peer review: 2 approved with reservations]. F1000Research 2025, 14:338 (https://doi.org/10.12688/f1000research.161076.1)

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PUBLISHED 27 Mar 2025

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Reviewer Report 26 May 2025

Pierre-Luc Germain, ETH Zurich, Zurich, Switzerland

Approved with Reservations

https://doi.org/10.5256/f1000research.177060.r384434

When presenting such a tool, it should be a must to include a live demo, i.e. a link to a fully-featured set-up of the app so that readers can get a feel for it before deciding when they want to ... Continue reading

When presenting such a tool, it should be a must to include a live demo, i.e. a link to a fully-featured set-up of the app so that readers can get a feel for it before deciding when they want to use it. I couldn't find any such link, neither in the paper nor in the repository. This should be amended.
Similarly, it would be good to provide users trying to set up the app an example dataset that's appropriately prepared (could be the same as the one used in the example above, or a subset thereof).

At the moment, installation is a pain. The package depends on lots of packages that are neither in cran nor in bioconductor, and one has to figure out where they are. Specifically, rbokeh, ggvolc and scToppR need to be installed manually. When going to the scToppR github page, the installation command doesn't work because scToppR is not in bioconductor. Similarly, the package depends on rbokeh, whose main information in the repository is that it is not actively maintained.
In the end I had to do this to install:
```
BiocManager::install(c("BioinformaticsMUSC/scToppR", "bokeh/rbokeh", "loukesio/ggvolc", "BioinformaticsMUSC/ShinyCellPlus"))
```
Users abandon early, and this lack of user-friendliness already at the installation stage makes one wary of the rest of the package: the feeling it gives is that the authors haven't really asked external users to try it.

It would be important to include some idea of the scalability. I appreciate the way the dataset is prepared and stored as h5 to ease memory requirements, this seems nicely done. Are the plots simply vector ggplots, which would become unmanageable with large datasets, or do they implement some rasterization or are they based on something more scalable? It's not a tragedy if the package is only applicable to small/medium-sized datasets, but users need to know before trying it.

The dataset and app preparation happens smoothly from a user perspective. At the same time, it seems like this could be achieved from other formats as well, e.g. an AnnData object. In general, it feels like many of the extra functionalities added in ShinyCellPlus are dependent on specific ways of doing the analysis (e.g. doing GO enrichment using ToppGene). This isn't optimal, but passable, I guess.

A key question I was left with is how this compares with other available tools, in particular iSEE. What was the rationale for a new tool? Are there things that ShinyCellPlus can do, and which iSEE can't? or, since this seems unlikely given iSEE's flexibility and custom plugins, are there things that are much simpler to do? This might be worth some discussion.

MINOR COMMENTS:
- There are a lot of little things in the functions that make user experience unnecessarily complicated. For example, I needed to specify the assay (which isn't done in the docs, presumably because the assay is named something recognized), but the error message wasn't very instructive. One could either fetch the default assay (and mention this) or at least outputs a useful error message saying that this was specified. Similarly, if scConf is left null, it could simply be computed on the fly to use defaults.
- The documentation says, "The Shiny App should run upon completion of this script", but it doesn't: the script simply creates a shinyApp folder, in which one has to go and manually launch the app.
- As one interacts with the app, the console reports many errors, and while these do not impair the usage of the app, it does suggest that the inputs to reactive functions are not appropriately checked, and this litters the logs with unnecessary warnings that can make debugging more difficult.
- The readme discusses the fact that it can take some time for the app to load. The authors could consider a splash page showing that something is happening (and at the same time allowing for some name/icon of the dataset/instance to appear), which can be easily implemented for instance with the waiter package.
- It might be useful to split the documentation, for example a user trying to test the app for the first time doesn't necessarily need to read through the whole Libra part...

Is the rationale for developing the new software tool clearly explained?

Partly
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Single-cell sequencing analysis and method development, also including shiny apps

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Reviewer Report 29 Apr 2025

Valdemaras Petrošius, Technical University of Denmark, Copenhagen, Denmark

Approved with Reservations

https://doi.org/10.5256/f1000research.177060.r377204

The authors here present an upgraded single-cell transcriptomics data visualization tool that has the functionality carry out basic visualization of datasets. The presented package supplements original ShinyCell package by allowing differential expression and pathway enrichment analysis. This package should allow ... Continue reading

The authors here present an upgraded single-cell transcriptomics data visualization tool that has the functionality carry out basic visualization of datasets. The presented package supplements original ShinyCell package by allowing differential expression and pathway enrichment analysis. This package should allow bioinformaticians to share results with less computationally inclined collaborator, where they could browse their genes and pathways of interest at their own pace with the shared Shiny application. I have listed some recommendations how to increase the adaptability of the package, however if the authors deems they do not align with their vision for the tool, they should not prohibit acceptance.

The dependence on Seurat only output is rather limiting, and the package should allow direct import from the other major data analysis packages like scanpy (h5ad format).
Trajectory inference in the form of pseudotime is a pivotal part of single-cell dataset analysis, hence adding appropriate visualization features might be warranted.
Bioinformaticians tend to generate their own data visualization styles/approaches, including a short framework in the package where would could insert additional visualization could promote a more widespread use of the package.

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Single-cell technologies

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Valdemaras Petrošius, Technical University of Denmark, Copenhagen, Denmark
Pierre-Luc Germain, ETH Zurich, Zurich, Switzerland

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26 May 2025 | for Version 1

Pierre-Luc Germain, ETH Zurich, Zurich, Switzerland

7 Views Cite this report Responses(0)

Approved With Reservations

When presenting such a tool, it should be a must to include a live demo, i.e. a link to a fully-featured set-up of the app so that readers can get a feel for it before deciding when they want to use it. I couldn't find any such link, neither in the paper nor in the repository. This should be amended.
Similarly, it would be good to provide users trying to set up the app an example dataset that's appropriately prepared (could be the same as the one used in the example above, or a subset thereof).

At the moment, installation is a pain. The package depends on lots of packages that are neither in cran nor in bioconductor, and one has to figure out where they are. Specifically, rbokeh, ggvolc and scToppR need to be installed manually. When going to the scToppR github page, the installation command doesn't work because scToppR is not in bioconductor. Similarly, the package depends on rbokeh, whose main information in the repository is that it is not actively maintained.
In the end I had to do this to install:
```
BiocManager::install(c("BioinformaticsMUSC/scToppR", "bokeh/rbokeh", "loukesio/ggvolc", "BioinformaticsMUSC/ShinyCellPlus"))
```
Users abandon early, and this lack of user-friendliness already at the installation stage makes one wary of the rest of the package: the feeling it gives is that the authors haven't really asked external users to try it.

It would be important to include some idea of the scalability. I appreciate the way the dataset is prepared and stored as h5 to ease memory requirements, this seems nicely done. Are the plots simply vector ggplots, which would become unmanageable with large datasets, or do they implement some rasterization or are they based on something more scalable? It's not a tragedy if the package is only applicable to small/medium-sized datasets, but users need to know before trying it.

The dataset and app preparation happens smoothly from a user perspective. At the same time, it seems like this could be achieved from other formats as well, e.g. an AnnData object. In general, it feels like many of the extra functionalities added in ShinyCellPlus are dependent on specific ways of doing the analysis (e.g. doing GO enrichment using ToppGene). This isn't optimal, but passable, I guess.

A key question I was left with is how this compares with other available tools, in particular iSEE. What was the rationale for a new tool? Are there things that ShinyCellPlus can do, and which iSEE can't? or, since this seems unlikely given iSEE's flexibility and custom plugins, are there things that are much simpler to do? This might be worth some discussion.

MINOR COMMENTS:
- There are a lot of little things in the functions that make user experience unnecessarily complicated. For example, I needed to specify the assay (which isn't done in the docs, presumably because the assay is named something recognized), but the error message wasn't very instructive. One could either fetch the default assay (and mention this) or at least outputs a useful error message saying that this was specified. Similarly, if scConf is left null, it could simply be computed on the fly to use defaults.
- The documentation says, "The Shiny App should run upon completion of this script", but it doesn't: the script simply creates a shinyApp folder, in which one has to go and manually launch the app.
- As one interacts with the app, the console reports many errors, and while these do not impair the usage of the app, it does suggest that the inputs to reactive functions are not appropriately checked, and this litters the logs with unnecessary warnings that can make debugging more difficult.
- The readme discusses the fact that it can take some time for the app to load. The authors could consider a splash page showing that something is happening (and at the same time allowing for some name/icon of the dataset/instance to appear), which can be easily implemented for instance with the waiter package.
- It might be useful to split the documentation, for example a user trying to test the app for the first time doesn't necessarily need to read through the whole Libra part...

Is the rationale for developing the new software tool clearly explained?

Partly
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Single-cell sequencing analysis and method development, also including shiny apps

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

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Reviewer Report

11 Views

29 Apr 2025 | for Version 1

Valdemaras Petrošius, Technical University of Denmark, Copenhagen, Denmark

11 Views Cite this report Responses(0)

Approved With Reservations

The authors here present an upgraded single-cell transcriptomics data visualization tool that has the functionality carry out basic visualization of datasets. The presented package supplements original ShinyCell package by allowing differential expression and pathway enrichment analysis. This package should allow bioinformaticians to share results with less computationally inclined collaborator, where they could browse their genes and pathways of interest at their own pace with the shared Shiny application. I have listed some recommendations how to increase the adaptability of the package, however if the authors deems they do not align with their vision for the tool, they should not prohibit acceptance.

The dependence on Seurat only output is rather limiting, and the package should allow direct import from the other major data analysis packages like scanpy (h5ad format).
Trajectory inference in the form of pseudotime is a pivotal part of single-cell dataset analysis, hence adding appropriate visualization features might be warranted.
Bioinformaticians tend to generate their own data visualization styles/approaches, including a short framework in the package where would could insert additional visualization could promote a more widespread use of the package.

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Single-cell technologies

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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[1] 1. Gulati GS, D'Silva JP, Liu Y, et al.: Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat. Rev. Mol. Cell Biol. 2025; 26: 11–31. PubMed Abstract | Publisher Full Text

[2] 2. Baysoy A, Bai Z, Satija R, et al.: The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 2023; 24: 695–713. PubMed Abstract | Publisher Full Text | Free Full Text

[3] 3. Aldridge S, Teichmann SA: Single cell transcriptomics comes of age. Nat. Commun. 2020; 11: 4307. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Stegle O, Teichmann SA, Marioni JC: Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 2015; 16: 133–145. PubMed Abstract | Publisher Full Text

[5] 5. Stuart T, Srivastava A, Madad S, et al.: Single-cell chromatin state analysis with Signac. Nat. Methods. 2021; 18: 1333–1341. PubMed Abstract | Publisher Full Text

[6] 6. Zappia L, Phipson B, Oshlack A: Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database. PLoS Comput. Biol. 2018; 14: e1006245. PubMed Abstract | Publisher Full Text | Free Full Text

[7] 7. Wolf FA, Angerer P, Theis FJ: SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018; 19: 15. PubMed Abstract | Publisher Full Text | Free Full Text

[8] 8. Lun AT, McCarthy DJ, Marioni JC: A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 2016; 5: 2122. PubMed Abstract | Publisher Full Text

[9] 9. Satija R, Farrell JA, Gennert D, et al.: Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 2015; 33: 495–502. PubMed Abstract | Publisher Full Text

[10] 10. Aussel R, Asif M, Chenag S, et al.: ShIVA: a user-friendly and interactive interface giving biologists control over their single-cell RNA-seq data. Sci. Rep. 2023; 13: 14377. PubMed Abstract | Publisher Full Text | Free Full Text

[11] 11. David FPA, Litovchenko M, Deplancke B, et al.: ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses. Nucleic Acids Res. 2020; 48: W403–W414. PubMed Abstract | Publisher Full Text | Free Full Text

[12] 12. Patel MV: iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data. Bioinformatics. 2018; 34: 4305–4306. PubMed Abstract | Publisher Full Text | Free Full Text

[13] 13. Prieto C, Barrios D, Villaverde A: SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud. Front Bioinform. 2022; 2: 793309. PubMed Abstract | Publisher Full Text | Free Full Text

[14] 14. Wang Y, Sarfraz I, Pervaiz N, et al.: Interactive analysis of single-cell data using flexible workflows with SCTK2. Patterns (N Y). 2023; 4: 100814. PubMed Abstract | Publisher Full Text | Free Full Text

[15] 15. Jagla B, Libri V, Chica C, et al.: SCHNAPPs - Single Cell sHiNy APPlication(s). J. Immunol. Methods. 2021; 499: 113176. PubMed Abstract | Publisher Full Text

[16] 16. Ouyang JF, Kamaraj US, Cao EY, et al.: ShinyCell: simple and sharable visualization of single-cell gene expression data. Bioinformatics. 2021; 37: 3374–3376. PubMed Abstract | Publisher Full Text

[17] 17. Rue-Albrecht K, Marini F, Soneson C: Lun ATL: iSEE: Interactive SummarizedExperiment Explorer. F1000Res. 2018; 7: 741. PubMed Abstract | Publisher Full Text | Free Full Text

[18] 18. Yousif A, Drou N, Rowe J, et al.: NASQAR: a web-based platform for high-throughput sequencing data analysis and visualization. BMC Bioinformatics. 2020; 21: 267. Publisher Full Text

[19] 19. Granger B, Berto S: scToppR: a coding-friendly R interface to ToppGene. Bioinformatics. 2024; 40. Publisher Full Text

[20] 20. Aibar S, Gonzalez-Blas CB, Moerman T, et al.: SCENIC: single-cell regulatory network inference and clustering. Nat. Methods. 2017; 14: 1083–1086. PubMed Abstract | Publisher Full Text

ShinyCellPlus: an improved ShinyCell application for the interactive visualization of single cell dat

Abstract

Motivation

Results

Availability and implementation

Keywords

1 Introduction

2 Implementation

2.1 ShinyCellPlus overview

Figure 1. The ShinyCellPlus upgrades. Screenshots of the new and upgraded functionalities.

2.2 Split by conditions and visualizations

2.3 Marker genes and differential expression tables

Figure 2. ToppGene gene ontology section.

2.4 Gene set enrichment

3 Operation

4 Conclusions

Software availability

Ethics and consent

Data availability

Acknowledgments

References

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