Profiling single cell gene expression data over specified time periods are increasingly applied to the study of complex developmental processes. Here, we describe a novel prototype-based dimension reduction method to visualize high throughput temporal expression data for single cell analyses. Our software preserves the global developmental trajectories over a specified time course, and it also identifies subpopulations of cells within each time point demonstrating superior visualization performance over six commonly used methods.
Package details |
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Author | Wuming Gong <gongx030@umn.edu> |
Maintainer | Wuming Gong <gongx030@umn.edu> |
License | MIT License |
Version | 2.0.2 |
Package repository | View on GitHub |
Installation |
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