TCM visualizes trajectories and cell populations from single cell data
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 16, 2018
- Source ID
- 10.1038/s41467-018-05112-9
Entities
People
- Daniel J Garry
- Il-youp Kwak
- Naoko Koyano-nakagawa
- Wei Pan
- Wuming Gong
Organizations
- National Institutes of Health
- United States Department of Defense