Motivation Single-cell transcriptomics profiling technologies enable genome-wide gene expression measurements in individual cells but can currently only provide a static snapshot of cellular transcriptional states. RNA velocity analysis can help infer cell state changes using such single-cell transcriptomics data. To interpret these cell state changes inferred from RNA velocity analysis as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding and other 2D embeddings derived from the observed single-cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. Results We developed VeloViz to create RNA velocity-informed 2D and 3D embeddings from single-cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By considering the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. Availability and implementation Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bioconductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. Datasets used can be found on Zenodo (https://doi.org/10.5281/zenodo.4632471). Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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VeloViz：用于可视化细胞轨迹的 RNA 速度信息嵌入

动机 单细胞转录组学分析技术可以在单个细胞中进行全基因组基因表达测量，但目前只能提供细胞转录状态的静态快照。RNA 速度分析可以帮助使用此类单细胞转录组学数据推断细胞状态变化。为了将这些从 RNA 速度分析推断出的细胞状态变化解释为潜在细胞轨迹的一部分，目前的方法依赖于主成分可视化、t 分布随机邻域嵌入和其他源自观察到的单细胞转录状态的 2D 嵌入。然而，这些 2D 嵌入可以产生底层细胞轨迹的不同表示，从而阻碍了对细胞状态变化的解释。结果 我们开发了 VeloViz，以根据单细胞转录组学数据创建 RNA 速度信息 2D 和 3D 嵌入。使用真实数据和模拟数据，我们证明 VeloViz 嵌入能够跨不同的轨迹拓扑捕获潜在的细胞轨迹，即使中间细胞状态可能缺失。通过考虑 RNA 速度分析预测的未来转录状态，VeloViz 可以帮助可视化更可靠地表示潜在的细胞轨迹。可用性和实施​​源代码可在 GitHub (https://github.com/JEFworks-Lab/veloviz) 和 Bioconductor (https://bioconductor.org/packages/veloviz) 上获得，其他教程位于 https://JEF.works /维洛维兹/。使用的数据集可以在 Zenodo (https://doi.org/10.5281/zenodo.4632471) 上找到。