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Updated: Sep 17, 2025
Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
Published on: December 1, 2023
ShinySC: An R/Shiny-based desktop application for seamless analysis of scRNA-Seq data
Po-Jung Huang1, Fang-Yu Tsai2, Yi-Ju Wu3
1Department 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.
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.
