Matrix Linear Models for connecting metabolite composition to individual characteristics

Music City Center 

We examine the problem of assessing how the association of metabolite levels with individual characteristics (such as sex or treatment) depends on metabolite traits (e.g., pathways). A standard approach involves two steps: testing each metabolite's association, followed by enrichment analysis. We combine both steps using a bilinear model based on the matrix linear model (MLM) framework. Our method estimates relationships among metabolites that share known characteristics, whether categorical (such as lipid type or pathway) or numerical (such as the number of double bonds in triglycerides). We demonstrate the
flexibility and interpretability of MLMs across various metabolomic studies. We illustrate how our method can distinguish the contributions of two correlated features of triglycerides: the number of carbon atoms and the number of double bonds, which would be overlooked if we analyzed lipids individually. Our method has been implemented using the open-source Julia package, MatrixLM, and can be explored using interactive notebooks.

high-throughput data

metabolomics

Julia package 

Section on Statistics in Genomics and Genetics