Single-cell platforms provide statistically large samples of snapshot observations capable of resolving intrercellular heterogeneity. Currently, there is a growing literature on algorithms that exploit this attribute in order to infer the trajectory of biological mechanisms, such as cell proliferation and differentiation. Despite the efforts, the trajectory inference methodology has not yet been used for addressing the challenging problem of learning the dynamics of protein signaling systems. In this work, we assess this prospect by testing the performance of this class of algorithms on four proteomic temporal datasets. To evaluate the learning quality, we design new general-purpose evaluation metrics that are able to quantify performance on (i) the biological meaning of the output, (ii) the consistency of the inferred trajectory, (iii) the algorithm robustness, (iv) the correlation of the learning output with the initial dataset, and (v) the roughness of the cell parameter levels though the inferred trajectory. We show that experimental time alone is insufficient to provide knowledge about the order of proteins during signal transduction. Accordingly, we show that the inferred trajectories provide richer information about the underlying dynamics. We learn that established methods tested on high-dimensional data with small sample size, slow dynamics, and complex structures (e.g. bifurcations) cannot always work in the signaling setting. Among the methods we evaluate, Scorpius and a newly introduced approach that combines Diffusion Maps and Principal Curves were found to perform adequately in recovering the progression of signal transduction although their performance on some metrics varies from one dataset to another. The novel metrics we devise highlight that it is difficult to conclude, which one method is universally applicable for the task. Arguably, there are still many challenges and open problems to resolve. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

中文翻译：

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从单细胞蛋白质组数据学习途径动力学：一项比较研究。

单细胞平台提供统计上大的快照观察样本，能够解决细胞内异质性。目前，越来越多的文献利用这一属性来推断生物机制的轨迹，例如细胞增殖和分化。尽管付出了努力，轨迹推断方法尚未用于解决学习蛋白质信号系统动力学的挑战性问题。在这项工作中，我们通过在四个蛋白质组时间数据集上测试此类算法的性能来评估这一前景。为了评估学习质量，我们设计了新的通用评估指标，能够量化以下方面的表现：（i）输出的生物学意义，（ii）推断轨迹的一致性，（iii）算法的鲁棒性，（iv） ）学习输出与初始数据集的相关性，以及（v）通过推断轨迹的细胞参数水平的粗糙度。我们表明，仅实验时间不足以提供有关信号转导过程中蛋白质顺序的知识。因此，我们表明推断的轨迹提供了有关潜在动态的更丰富的信息。我们了解到，在样本量小、动态缓慢和结构复杂（例如分叉）的高维数据上测试的既定方法并不总是在信号设置中起作用。在我们评估的方法中，Scorpius 和一种新引入的结合扩散图和主曲线的方法被发现在恢复信号转导的进展方面表现良好，尽管它们在某些指标上的表现因数据集而异。我们设计的新颖指标强调，很难得出哪一种方法普遍适用于该任务的结论。可以说，仍然有许多挑战和悬而未决的问题需要解决。© 2020 作者。细胞计数法 A 部分由 Wiley periodicals, Inc. 代表国际细胞计数法促进会出版。