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VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories.
Lyla Atta1,2,3, Arpan Sahoo1,4, Jean Fan1,2,4
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Bioinformatics (Oxford, England)
|September 9, 2021
Summary
VeloViz visualizes cell state changes from single-cell transcriptomics data. This tool provides reliable RNA velocity-informed 2D and 3D embeddings for clearer cellular trajectory interpretation.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Single-cell transcriptomics offers static snapshots of cellular states.
- RNA velocity analysis infers dynamic cell state changes.
- Current visualization methods for RNA velocity data can be misleading.
Purpose of the Study:
- To develop VeloViz, a tool for RNA velocity-informed 2D and 3D embeddings.
- To improve the visualization and interpretation of cellular trajectories.
- To provide a more reliable representation of cell state dynamics.
Main Methods:
- VeloViz generates RNA velocity-informed 2D and 3D embeddings.
- The tool utilizes single-cell transcriptomics data.
- Analysis includes real and simulated datasets.
Main Results:
- VeloViz embeddings accurately capture cellular trajectories across various topologies.
- The tool effectively visualizes trajectories even with missing intermediate cell states.
- RNA velocity predictions enhance the reliability of visualized cellular trajectories.
Conclusions:
- VeloViz offers a more robust method for visualizing cellular trajectories.
- The tool aids in interpreting cell state dynamics from single-cell data.
- Improved visualization facilitates a deeper understanding of biological processes.
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