Cong Feng* and Ming Chen
College of Life Sciences, Zhejiang University, Hangzhou, China
ventson [at] zju.edu.cn
Abstract
The complexity of single-cell transcriptomic data traditionally required advanced programming skills, which limited independent data exploration by experimental biologists. This barrier motivated the authors to develop CellAnalyst, a software tool that explicitly separated routine bioinformatics analysis from subsequent data interpretation. The primary objective was to empower non-computational scientists to explore and visualize single-cell transcriptomes independently. To achieve this, the authors structured the workflow to require only a simple initial setup executed via a command in the R language. Following this brief setup, the platform provided multiple visualization and analytical functions within an intuitive graphical user interface. This approach allowed users to interactively explore dimensionality reduction, cellular clustering, differential gene expression, and functional enrichment without writing additional code. Furthermore, the developers incorporated an optional split view feature that segregated data by metadata categories, which facilitated side-by-side comparative insights across different experimental conditions or time points. The software successfully enabled experimental biologists to navigate pre-processed datasets, accurately identify cellular subpopulations, and generate customizable, high-quality visualizations. This strategic separation of computational processing and data exploration significantly reduced the time and effort required for data interpretation. To maximize accessibility, the developers designed the tool to support cross-platform use, functioning seamlessly across Windows, Mac, and Linux web servers. Ultimately, CellAnalyst provided an accessible and effective solution that democratized complex bioinformatics workflows and accelerated cellular research by bridging the gap between sophisticated analytical algorithms and the broader biological community. The software was made freely accessible at https://bis.zju.edu.cn/cellanalyst.
Keywords: single cell, visualization, bioinformatics, software