A New Phylogeny-Driven Random Forest-Based Classification Approach for Functional Metagenomics

TL;DR: In this paper , a new classification method based on RF as guided by the evolutionary ancestry of microbial phylogeny, i.e. Phylogeny-RF, has been developed in order to capture the effects of phylogenetic relatedness in a ML classifier itself.

Abstract: Classifying microbial genes into their functional repertoire is an important task for metagenomic studies, where the research community is trying to develop Machine Learning (ML) based methods to achieve good classification performance. Random Forest (RF) has been proposed as one of the most favorable methods for such supervised analysis when applied over the abundance profiles of microbial genes mapping them to functional phenotypes. To further explore and make optimization in the existing RF model (based on the biological relationships between microbial features), a new classification method based on RF as guided by the evolutionary ancestry of microbial phylogeny, i.e. Phylogeny-RF, has been developed in this paper. This method facilitates to capture the effects of phylogenetic relatedness in a ML classifier itself. Closely related microbes by phylogeny are highly correlated and tend to have similar genetic and phenotypic traits. Such microbes behave similarly; and hence tend to be selected together or one of these could be dropped from the analysis, to make the ML process better. The proposed Phylogeny-RF algorithm has been compared with state-of-the-art classification methods including RF and the phylogeny-aware method of MetaPhyl, using 2 real-world 16S rRNA metagenomic data sets. It is observed that the proposed method performed better than the other phylogeny-driven benchmarks. For example, Phylogeny-RF attained a high AUC of 0.949 over soil microbiomes in comparison to other benchmarks.