# Phoenics: a novel statistical approach for longitudinal metabolomic pathway analysis

## TL;DR

Phoenics is a new statistical method that improves the analysis of longitudinal metabolomic data by focusing on biological pathways, making results more interpretable.

## Contribution

Phoenics introduces a pathway-based differential analysis method for longitudinal metabolomic data with improved detection of biological functions.

## Key findings

- Phoenics properly controls the Type I error rate in pathway analysis.
- It outperforms alternative methods in detecting differential metabolic pathways.
- The method successfully identifies new impacted biological functions from metabolomic data.

## Abstract

Metabolomics describes the metabolic profile of an organism at a given time by the concentrations of its constituent metabolites. When studied over time, metabolite concentrations can help understand the dynamical evolution of a biological process. However, metabolites are involved into sequences of chemical reactions, called metabolic pathways, related to a given biological function. Accounting for these pathways into statistical methods for metabolomic data is thus a relevant way to directly express results in terms of biological functions and to increase their interpretability.

We propose a new method, phoenics, to perform differential analysis for longitudinal metabolomic data at the pathway level. In short, phoenics proceeds in two steps: First, the matrix of metabolite quantifications is transformed by a dimension reduction approach accounting for pathway information. Then, a mixed linear model is fitted on the transformed data.

This method was applied to semi-synthetic NMR data and two real NMR datasets assessing the effects of antibiotics and irritable bowel syndrome on feces. Results showed that phoenics properly controls the Type I error rate and has a better ability to detect differential metabolic pathways and to extract new impacted biological functions than alternative methods. The method is implemented in the R package phoenics available on CRAN