GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis

TL;DR

GraphKKE introduces a novel spectral graph kernel embedding method that effectively captures dynamic changes in time-evolving microbial community graphs, aiding microbiome analysis and disease association studies.

Contribution

The paper presents a new embedding technique based on transfer operators and graph kernels for analyzing time-evolving graphs in microbiome research.

Findings

Successfully captures temporary changes in synthetic data

Effective in real-world microbiome datasets

Facilitates understanding of microbial dynamics

Abstract

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.