ShinySC: an R/Shiny-based desktop application for seamless analysis of scRNA-Seq data

Po-Jung Huang; Fang-Yu Tsai; Yi-Ju Wu; Yi-Chen Weng; Chi-Ching Lee; Sin-Hong Shih; Shih Sheng Jiang

doi:10.1016/j.bj.2025.100885

ShinySC: an R/Shiny-based desktop application for seamless analysis of scRNA-Seq data

Biomed J. 2025 Jul 1:100885. doi: 10.1016/j.bj.2025.100885. Online ahead of print.

Authors

Po-Jung Huang¹, Fang-Yu Tsai², Yi-Ju Wu³, Yi-Chen Weng⁴, Chi-Ching Lee⁵, Sin-Hong Shih⁶, Shih Sheng Jiang⁷

Affiliations

¹ Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan; Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan; Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan. Electronic address: pjhuang@gap.cgu.edu.tw.
² National Institute of Cancer Research, National Health Research Institute, Zhunan, Miaoli, Taiwan. Electronic address: tatufish@nhri.edu.tw.
³ Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli, Taiwan. Electronic address: nhriwu@nhri.edu.tw.
⁴ School of Medicine, Chang Gung University, Taoyuan, Taiwan. Electronic address: yichenweng91@gmail.com.
⁵ Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan; Department and Graduate Institute of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan. Electronic address: chichinglee@cgu.edu.tw.
⁶ Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan. Electronic address: Xinn01729@gmail.com.
⁷ National Institute of Cancer Research, National Health Research Institute, Zhunan, Miaoli, Taiwan; Biotechnology Center/Ph.D. Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, Taichung, Taiwan. Electronic address: ssjiang@nhir.edu.tw.

PMID: 40609640
DOI: 10.1016/j.bj.2025.100885

Abstract

Background: Single-cell RNA sequencing (scRNA-seq) enables detailed profiling of cellular heterogeneity, but complex workflows and diverse data formats limit accessibility for clinicians and researchers without programming expertise.

Results: We introduce ShinySC, an R/Shiny-based desktop application designed to streamline comprehensive scRNA-seq analysis through an intuitive graphical interface. ShinySC supports various input formats, including 10x Genomics, Seurat, Scanpy, BD Rhapsody, and CellView. The tool integrates essential analytical procedures such as quality control, normalization, dimensionality reduction, clustering, marker gene identification, batch correction, differential expression analysis, and trajectory inference. Notably, ShinySC implements multiple automatic cell-type annotation methods-reference-based (SingleR), marker-based (ScType, scCATCH), and GPT-based (GPTCelltype)-with features for side-by-side comparison and manual label refinement. Benchmarking indicates robust performance for datasets containing up to 200,000 cells on standard desktop systems with 64 GB RAM, with analysis duration dependent on specific tasks and annotation methods. Demonstrative analyses of PBMC and interferon-stimulated datasets confirm ShinySC's efficacy in accurately annotating cell types and capturing condition-specific transcriptional dynamics.

Conclusions: ShinySC provides a unified, user-friendly, and scalable platform for scRNA-seq analysis explicitly tailored for non-programming users. It surpasses existing limitations by accommodating multiple data formats, employing versatile annotation strategies, and generating high-quality, publication-ready figures. Available freely across Windows, macOS, and Linux platforms, ShinySC enhances the accessibility and reproducibility of single-cell transcriptomic research.

Availability: http://tardis.cgu.edu.tw/ShinySC.

Keywords: Automatic cell-type annotation; ChatGPT-4; Single-cell analysis; scRNA-seq.