GraphKKE：用于人体微生物组分析的图核Koopman嵌入
GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
摘要：
已经发现越来越多的疾病与微生物组构成的紊乱密切相关，例如，肥胖、糖尿病或某些癌症类型。由于现代高通量组学技术，可以直接分析人体微生物组及其对健康状况的影响。长期监测微生物群落，并探索其成员之间的关联。这些关系可以用一个时间演变图来描述。为了了解微生物群落成员对不同范围的扰动（如抗生素暴露或疾病）和一般动力学特性的反应，必须分析人类微生物群落的时间演变图。由于微生物和亚稳态动力学之间的数十种复杂相互作用，这变得特别具有挑战性。解决这个问题的关键是将时间演化图表示为保持原始动态的固定长度特征向量。提出了一种基于迁移算子和图核的谱分析的时间演化图嵌入学习方法。我们证明了我们的方法可以在创建的合成数据和真实数据上捕获时间演化图中的临时变化。我们的实验证明了该方法的有效性。此外，我们表明，我们的方法可以应用于人体微生物组数据来研究动态过程。
英文摘要：
More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored over long periods of time and the associations between their members are explored. These relationships can be described by a time-evolving graph. In order to understand responses of the microbial community members to a distinct range of perturbations such as antibiotics exposure or diseases and general dynamical properties, the time-evolving graph of the human microbial communities has to be analyzed. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics. The key to solving this problem is the representation of the time-evolving graphs as fixed-length feature vectors preserving the original dynamics. We propose a method for learning the embedding of the time-evolving graph that is based on the spectral analysis of transfer operators and graph kernels. We demonstrate that our method can capture temporary changes in the time-evolving graph on both created synthetic data and real-world data. Our experiments demonstrate the efficacy of the method. Furthermore, we show that our method can be applied to human microbiome data to study dynamic processes.
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