TCM visualizes trajectories and cell populations from single cell data

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

doi:10.1038/s41467-018-05112-9

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 journal › Article › peer-review

11 Scopus citations

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.

Original language	English (US)
Article number	2749
Journal	Nature communications
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41467-018-05112-9
State	Published - 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.

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© 2018 The Author(s).

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10.1038/s41467-018-05112-9

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048168

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AU - Koyano-Nakagawa, Naoko

AU - Pan, Wei

AU - Garry, Daniel J.

N1 - 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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N2 - 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.

AB - 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.

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TCM visualizes trajectories and cell populations from single cell data

Abstract

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