Abstract
Background: Microbiomes consist of bacteria, viruses, and other microorganisms, and are responsible for many different functions in both organisms and the environment. Some previous analyses of microbiomes focus on the relationships between specific microbiomes and a particular disease. These typically use correlation which is fundamentally symmetric with respect to pairs of microbes. Correlation focuses on the symmetry of the data distribution, and asymmetric data is often discarded as having a weak correlation. With all the data available on the microbiome, there is a need for a method that comprehensively studies microbiomes and how they are related to each other.Results: We collect publicly available datasets from human, environment, and animal samples to determine both symmetric and asymmetric Boolean relationships between a pair of microbes. We then find relationships that are potentially invariants, meaning they will hold in any microbe community. In other words, if we determine there is a relationship between two microbes, we expect the relationship to hold in almost all context. We discovered that certain pairs of microbes always exhibit the same relationship in almost all the datasets we studied, thus making them good candidates for universal relationships. Our results confirm known biological properties and seem promising in terms of disease diagnosis.Conclusions: Since the relationships are likely universal, we expect that they will hold in a clinical setting as well as in the general population. Strong universal relationships may provide insight on prognostic, predictive, or therapeutic properties of a clinically relevant disease. These new analyses may improve disease diagnosis and drug development in terms of accuracy and efficiency.
Symmetric and Asymmetric Data in Solution Models
