A new method for mining information of gut microbiome with probabilistic topic models

Abstract

Microbiome is closely related to many major human diseases, but it is generally analyzed by the traditional statistical methods such as principal component analysis, principal coordinate analysis, etc. These methods have shortcomings and do not consider the characteristics of the microbiome data itself (i.e., the “probability distribution” of microbiome). A new method based on probabilistic topic model was proposed to mine the information of gut microbiome in this paper, taking gut microbiome of type 2 diabetes patients and healthy subjects as an example. Firstly, different weights were assigned to different microbiome according to the degree of correlation between different microbiome and subjects. Then a probabilistic topic model was employed to obtain the probabilistic distribution of gut microbiome (i.e., per-topic OTU (operational taxonomic units, OTU) distribution and per-patient topic distribution). Experimental results showed that the output topics can be used as the characteristics of gut microbiome, and can describe the differences of gut microbiome over different groups. Furthermore, in order to verify the ability of this method to characterize gut microbiome, clustering and classification operations on the distributions over topics for gut microbiome in each subject were performed, and the experimental results showed that the clustering and classification performance has been improved, and the recognition rate of three groups reached 100%. The proposed method could mine the information hidden in gut microbiome data, and the output topics could describe the characteristics of gut microbiome, which provides a new perspective for the study of gut microbiome.