# EXPLANA: a user-friendly workflow for EXPLoratory ANAlysis and feature selection in cross-sectional and longitudinal microbiome studies

## TL;DR

EXPLANA is a user-friendly tool for analyzing microbiome data over time, helping identify important features related to outcomes in both cross-sectional and longitudinal studies.

## Contribution

EXPLANA introduces a novel workflow combining machine learning and change analysis for feature selection in longitudinal microbiome studies.

## Key findings

- EXPLANA outperformed existing tools like QIIME 2 feature-volatility in simulated data with a balanced accuracy of 0.91.
- The tool identified novel order-dependent categorical feature changes, such as differences between A_B and B_A transitions.
- EXPLANA generates interactive reports summarizing methods and results for both numerical and categorical data.

## Abstract

Longitudinal microbiome studies (LMS) are increasingly common but have analytic challenges including nonindependent data requiring mixed-effects models. Furthermore, large amounts of data motivate exploratory analysis to identify factors related to outcome variables. Although change analysis (i.e. calculating feature changes between timepoints) can be powerful, how to best conduct these analyses is often unclear. For example, observational LMS measurements show natural fluctuations, so baseline might not be a reference of primary interest, whereas for interventional LMS, baseline is typically a key reference point, often indicating the start of treatment.

To address these challenges, a feature selection workflow, called EXPLANA (EXPLoratory ANAlysis), was developed for LMS that supports numerical and categorical data, and also accommodates cross-sectional studies. Machine learning methods were combined with different types of change calculations and downstream interpretation methods to identify statistically meaningful variables and explain their relationship to outcomes. EXPLANA generates an interactive report that textually and graphically summarizes methods and results. EXPLANA had good performance on simulated longitudinal data, with a balanced accuracy score of 0.91 (range: 0.79–1.00, SD = 0.05), outperformed an existing tool, QIIME 2 feature-volatility (balanced accuracy: 0.95 versus 0.56) and identified novel order-dependent categorical feature changes (e.g. different effect for A_B versus B_A). EXPLANA is broadly applicable and simplifies analytics for identifying features related to outcomes of interest.

Software is available at https://github.com/JTFouquier/explana and https://zenodo.org/records/17478745 (10.5281/zenodo.17478744). Documentation and demos are available at www.explana.io.