Inference of multiple trajectories in single cell RNA-seq data from RNA velocity
Inference of multiple trajectories in single cell RNA-seq data from RNA velocity
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DOI：
10.1101/2020.09.30.321125
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Ziqi Zhang;Xiuwei Zhang
中科院分区：
文献类型：
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作者：
Ziqi Zhang;Xiuwei Zhang
轨迹推断方法用于推断连续生物过程的发育动力学，例如干细胞分化和癌细胞发育。当前大多数轨迹推断方法基于细胞之间的转录组相似性，使用单细胞RNA测序（scRNA-Seq）数据来推断细胞发育轨迹。这些方法通常仅限于某些轨迹结构，例如树或循环，并且当提供根单元时只能部分推断轨迹的方向。我们提出了 CellPaths，一种单细胞轨迹推断方法，通过整合 RNA 速度信息来推断发育轨迹。 CellPaths 能够找到多个高分辨率轨迹，而不是传统轨迹推理方法中的一条轨迹，并且轨迹结构不再受限于任何特定拓扑。 RNA-velocity 提供的方向信息还允许 CellPaths 自动检测根细胞和分化方向。我们在真实数据集和合成数据集上评估 CellPaths。结果表明，与当前最先进的轨迹推断方法相比，CellPaths 可以找到更准确、更详细的轨迹。
Trajectory inference methods are used to infer the developmental dynamics of a continuous biological process such as stem cell differentiation and cancer cell development. Most of the current trajectory inference methods infer cell developmental trajectories based on the transcriptome similarity between cells, using single cell RNA-Sequencing (scRNA-Seq) data. These methods are often restricted to certain trajectory structures like trees or cycles, and the directions of the trajectory can only be partly inferred when the root cell is provided. We present CellPaths, a single cell trajectory inference method that infers developmental trajectories by integrating RNA velocity information. CellPaths is able to find multiple high-resolution trajectories instead of one single trajectory from traditional trajectory inference methods, and the trajectory structure is no longer constrained to be of any specific topology. The direction information provided by RNA-velocity also allows CellPaths to automatically detect root cell and differentiation direction. We evaluate CellPaths on both real and synthetic datasets. The result shows that CellPaths finds more accurate and detailed trajectories compared to current state-of-the-art trajectory inference methods.