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.
Bibliographical noteFunding Information:
Funding support was obtained from the National Institutes of Health (R01HL122576 and U01HL100407 to D.J.G.) and the Department of Defense (GRANT11763537). We acknowledge the support from the University of Minnesota Supercomputing Institute.