Zero-inflated Poisson Factor Model with Application to Microbiome Absolute Abundance Data

TL;DR

This paper introduces a zero-inflated Poisson factor analysis model tailored for high-dimensional microbiome count data, effectively capturing excess zeros and enabling meaningful dimension reduction for downstream analysis.

Contribution

The paper proposes a novel ZIPFA model with an EM algorithm, specifically designed for microbiome data, addressing limitations of existing methods in handling zero inflation and count data.

Findings

The ZIPFA model accurately captures zero inflation in simulated data.

Application to ORIGINS data reveals microbiome associations with periodontal disease.

The method improves dimension reduction for microbiome analysis.

Abstract

Dimension reduction of high-dimensional microbiome data facilitates subsequent analysis such as regression and clustering. Most existing reduction methods cannot fully accommodate the special features of the data such as count-valued and excessive zero reads. We propose a zero-inflated Poisson factor analysis (ZIPFA) model in this article. The model assumes that microbiome absolute abundance data follow zero-inflated Poisson distributions with library size as offset and Poisson rates negatively related to the inflated zero occurrences. The latent parameters of the model form a low-rank matrix consisting of interpretable loadings and low-dimensional scores which can be used for further analyses. We develop an efficient and robust expectation-maximization (EM) algorithm for parameter estimation. We demonstrate the efficacy of the proposed method using comprehensive simulation studies. The application to the Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) provides valuable insights into the relation between subgingival microbiome and periodontal disease.