Sparse Treatment-Effect Model for Taxon Identification with High-Dimensional Metagenomic Data

To identify disease-associated taxa is an important task in metagenomics. To date, many methods have been proposed for feature selection and prediction. However, those proposed methods are either using univariate (generalized) regression approaches to get the corresponding P-values without considering the interactions among taxa, or using lasso or L₀ type sparse modeling approaches to identify taxa with best predictions without providing P-values. To the best of our knowledge, there are no available methods that consider taxon interactions and also generate P-values. In this paper, we propose a treatment-effect model for identifying taxa (STEMIT) and performing statistical inference with high-dimensional metagenomic data. STEMIT will provide a P-value for a taxon through a two-step treatment-effect maximization. It will provide causal inference if the study is a clinical trial. We first identify taxa associated with the treatment-effect variable and the targeting feature with sparse modeling, and then estimate the P-value of the targeting gene with ordinary least square (OLS) regression. We demonstrate that the proposed method is efficient and can identify biologically important taxa with a real metagenomic data set. The software for L₀ sparse modeling can be downloaded at https://cran.r-project.org/web/packages/l0ara/.