Topological data analysis of zebrafish patterns
Topological data analysis of zebrafish patterns
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DOI：
10.1073/pnas.1917763117
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发表时间：
2020-03-10
影响因子：
11.1
通讯作者：
Sandstede, Bjorn
中科院分区：
文献类型：
--
作者：
McGuirl, Melissa R.;Volkening, Alexandria;Sandstede, Bjorn
自组织模式行为在自然界中无处不在，从鱼群到有机体发育过程中的集体细胞动力学。从质量上讲，这些模式显示出令人印象深刻的一致性，但在微观和宏观尺度上，模式形成系统中不可避免地存在可变性。量化变异性和测量模式特征可以告知潜在的代理交互并允许进行预测分析。然而，当前用于分析集体行为产生的模式的方法仅捕获宏观特征，或者依赖于手动检查或平滑算法，而这些算法失去了数据的基于代理的基础性质。在这里，我们介绍基于拓扑数据分析和可解释机器学习的方法，用于大规模量化代理级特征和全局模式属性。由于斑马鱼是皮肤图案形成的模型生物，因此我们特别关注分析其皮肤图案，作为说明我们方法的一种手段。使用最近的基于代理的模型，我们模拟了数千种野生型和突变斑马鱼模式，并应用我们的方法来更好地了解斑马鱼的模式变异性。我们的方法能够量化细胞相互作用的随机性对野生型和突变型模式的差异影响，并且我们使用我们的方法来预测条纹和斑点统计数据作为不同细胞通信的函数。我们的工作提供了一种自动量化生物模式和分析基于主体的动态的方法，以便我们现在可以在更大范围内回答模式形成中的关键问题。
Self-organized pattern behavior is ubiquitous throughout nature, from fish schooling to collective cell dynamics during organism development. Qualitatively these patterns display impressive consistency, yet variability inevitably exists within pattern-forming systems on both microscopic and macroscopic scales. Quantifying variability and measuring pattern features can inform the underlying agent interactions and allow for predictive analyses. Nevertheless, current methods for analyzing patterns that arise from collective behavior capture only macroscopic features or rely on either manual inspection or smoothing algorithms that lose the underlying agent-based nature of the data. Here we introduce methods based on topological data analysis and interpretable machine learning for quantifying both agent-level features and global pattern attributes on a large scale. Because the zebrafish is a model organism for skin pattern formation, we focus specifically on analyzing its skin patterns as a means of illustrating our approach. Using a recent agent-based model, we simulate thousands of wild-type and mutant zebrafish patterns and apply our methodology to better understand pattern variability in zebrafish. Our methodology is able to quantify the differential impact of stochasticity in cell interactions on wild-type and mutant patterns, and we use our methods to predict stripe and spot statistics as a function of varying cellular communication. Our work provides an approach to automatically quantifying biological patterns and analyzing agent-based dynamics so that we can now answer critical questions in pattern formation at a much larger scale.