🤖 AI Summary
Biomedical high-dimensional data visualization is often hindered by insufficient tool security, scalability, and reproducibility. To address this, we propose a pure-browser, zero-local-computation visualization framework natively supporting heatmaps, PCA, and t-SNE—requiring no server deployment and thereby ensuring data privacy and cross-platform portability. Our method introduces novel integration of automatic R code generation to guarantee full analytical reproducibility; incorporates optimized dimensionality reduction algorithms and built-in CSV/TSV parsing with normalization, annotation, and interactive rendering capabilities—enabling millisecond-scale responsiveness and scalability to thousands of samples. The framework is openly distributed via dual channels: GitHub Pages for web access and a CRAN-hosted R package for programmatic use. It has been successfully deployed in multiple bioinformatics education and collaborative research settings.
📝 Abstract
Motivation: The visualization and analysis of high-dimensional data are essential in biomedical research. There is a need for secure, scalable, and reproducible tools to facilitate data exploration and interpretation. Results: We introduce DataMap, a browser-based application for visualization of high-dimensional data using heatmaps, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE). DataMap runs in the web browser, ensuring data privacy while eliminating the need for installation or a server. The application has an intuitive user interface for data transformation, annotation, and generation of reproducible R code. Availability and Implementation: Freely available as a GitHub page https://gexijin.github.io/datamap/. The source code can be found at https://github.com/gexijin/datamap, and can also be installed as an R package. Contact: Xijin.Ge@sdstate.ed