TCM visualizes trajectories and cell populations from single cell data

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.Time series single cell expression data has large variance between time points and is challenging for analysis. Here, the authors develop a new dimension reduction and data visualization tool for large scale temporal scRNA-seq data which identifies trajectories and subpopulations.

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