# Latent Variable Modeling for the Microbiome

## TL;DR

This paper explores the application of probabilistic latent variable models like LDA and NMF to microbiome data, providing guidelines and comparisons through simulations and a case study on antibiotics' effects.

## Contribution

It introduces the use of latent variable models in microbiome analysis and offers practical guidelines for their application based on simulation results.

## Key findings

- Different models are suitable for various microbiome data structures.
- Probabilistic models can effectively capture microbiome community patterns.
- Case study demonstrates model applicability to real microbiome data.

## Abstract

The human microbiome is a complex ecological system, and describing its structure and function under different environmental conditions is important from both basic scientific and medical perspectives. Viewed through a biostatistical lens, many microbiome analysis goals can be formulated as latent variable modeling problems. However, although probabilistic latent variable models are a cornerstone of modern unsupervised learning, they are rarely applied in the context of microbiome data analysis, in spite of the evolutionary, temporal, and count structure that could be directly incorporated through such models. We explore the application of probabilistic latent variable models to microbiome data, with a focus on Latent Dirichlet Allocation, Nonnegative Matrix Factorization, and Dynamic Unigram models. To develop guidelines for when different methods are appropriate, we perform a simulation study. We further illustrate and compare these techniques using the data of [10], a study on the effects of antibiotics on bacterial community composition. Code and data for all simulations and case studies are available publicly.