TCM visualizes trajectories and cell populations from single cell data

Wuming Gong, Il-Youp Kwak, Naoko Koyano-Nakagawa, Wei Pan and Daniel J. Garry ()
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Wuming Gong: University of Minnesota
Il-Youp Kwak: University of Minnesota
Naoko Koyano-Nakagawa: University of Minnesota
Wei Pan: University of Minnesota
Daniel J. Garry: University of Minnesota

Nature Communications, 2018, vol. 9, issue 1, 1-8

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-05112-9

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