Huang Po-Jung, Tsai Fang-Yu, Wu Yi-Ju, Weng Yi-Chen, Lee Chi-Ching, Shih Sin-Hong, Jiang Shih Sheng
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.
National Institute of Cancer Research, National Health Research Institute, Zhunan, Miaoli, Taiwan.
Biomed J. 2025 Jul 1:100885. doi: 10.1016/j.bj.2025.100885.
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.
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.
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.
单细胞RNA测序（scRNA-seq）能够详细分析细胞异质性，但复杂的工作流程和多样的数据格式限制了没有编程专业知识的临床医生和研究人员对其的使用。
我们推出了ShinySC，这是一个基于R/Shiny的桌面应用程序，旨在通过直观的图形界面简化全面的scRNA-seq分析。ShinySC支持多种输入格式，包括10x Genomics、Seurat、Scanpy、BD Rhapsody和CellView。该工具集成了诸如质量控制、标准化、降维、聚类、标记基因识别、批次校正、差异表达分析和轨迹推断等基本分析程序。值得注意的是，ShinySC实现了多种自动细胞类型注释方法——基于参考的（SingleR）、基于标记的（ScType、scCATCH）和基于GPT的（GPTCelltype）——具有并排比较和手动标签细化功能。基准测试表明，在配备64GB内存的标准桌面系统上，对于包含多达200,000个细胞的数据集，该软件具有强大的性能，分析持续时间取决于特定任务和注释方法。对PBMC和干扰素刺激数据集的演示分析证实了ShinySC在准确注释细胞类型和捕捉特定条件下转录动态方面的有效性。
ShinySC为scRNA-seq分析提供了一个统一、用户友好且可扩展的数据平台，特别为非编程用户量身定制。它通过兼容多种数据格式、采用通用注释策略以及生成高质量、可用于发表的图形，克服了现有局限性。ShinySC可在Windows、macOS和Linux平台上免费获取，提高了单细胞转录组学研究的可及性和可重复性。
