TCM visualizes trajectories and cell populations from single cell data

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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 languageEnglish (US)
Article number2749
JournalNature communications
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2018

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