TCM visualizes trajectories and cell populations from single cell data

Wuming Gong, Il Youp Kwak, Naoko Koyano-Nakagawa, Wei Pan, Daniel J. Garry

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
Article number2749
JournalNature communications
Issue number1
StatePublished - Dec 1 2018

Bibliographical note

Funding 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.

Publisher Copyright:
© 2018 The Author(s).


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