Boolean implication analysis unveils candidate universal relationships in microbiome data.

Affiliation

Vo D(#)(1), Singh SC(#)(2), Safa S(#)(3), Sahoo D(4)(5)(6).
Author information:
(1)Department of Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, 92093-083, USA.
(2)Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, 92093-083, USA.
(3)Department of Computer Science and Engineering, Jacob's School of Engineering, University of California San Diego, La Jolla, CA, 92093-083, USA.
(4)Department of Computer Science and Engineering, Jacob's School of Engineering, University of California San Diego, La Jolla, CA, 92093-083, USA. [Email]
(5)Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0730, Leichtag Building 132, La Jolla, CA, 92093-083, USA. [Email]
(6)Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093-083, USA. [Email]
(#)Contributed equally

Abstract

BACKGROUND: Microbiomes consist of bacteria, viruses, and other microorganisms, and are responsible for many different functions in both organisms and the environment. Past analyses of microbiomes focused on using correlation to determine linear relationships between microbes and diseases. Weak correlations due to nonlinearity between microbe pairs may cause researchers to overlook critical components of the data. With the abundance of available microbiome, we need a method that comprehensively studies microbiomes and how they are related to each other. RESULTS: We collected publicly available datasets from human, environment, and animal samples to determine both symmetric and asymmetric Boolean implication relationships between a pair of microbes. We then found 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 contexts. We discovered that around 330,000 pairs of microbes universally exhibit the same relationship in almost all the datasets we studied, thus making them good candidates for invariants. Our results also confirm known biological properties and seem promising in terms of disease diagnosis. CONCLUSIONS: Since the relationships are likely universal, we expect them to hold in clinical settings, as well as general populations. If these strong invariants are present in disease settings, it may provide insight into prognostic, predictive, or therapeutic properties of clinically relevant diseases. For example, our results indicate that there is a difference in the microbe distributions between patients who have or do not have IBD, eczema and psoriasis. These new analyses may improve disease diagnosis and drug development in terms of accuracy and efficiency.