A Bayesian factor analysis model for high-dimensional microbiome count data

TL;DR

This paper introduces a Bayesian zero-inflated probabilistic PCA model tailored for high-dimensional microbiome count data, effectively handling excess zeros and improving latent factor analysis compared to existing methods.

Contribution

The paper presents a novel Bayesian zero-inflated PCA model with a variational inference algorithm specifically designed for microbiome count data analysis.

Findings

Outperforms competing methods in simulation experiments

Demonstrates superior prediction accuracy on real datasets

Efficient variational inference algorithm developed

Abstract

Dimension reduction techniques are among the most essential analytical tools in the analysis of high-dimensional data. Generalized principal component analysis (PCA) is an extension to standard PCA that has been widely used to identify low-dimensional features in high-dimensional discrete data, such as binary, multi-category and count data. For microbiome count data in particular, the multinomial PCA is a natural counterpart of the standard PCA. However, this technique fails to account for the excessive number of zero values, which is frequently observed in microbiome count data. To allow for sparsity, zero-inflated multivariate distributions can be used. We propose a zero-inflated probabilistic PCA model for latent factor analysis. The proposed model is a fully Bayesian factor analysis technique that is appropriate for microbiome count data analysis. In addition, we use the mean-field-type variational family to approximate the marginal likelihood and develop a classification variational approximation algorithm to fit the model. We demonstrate the efficiency of our procedure for predictions based on the latent factors and the model parameters through simulation experiments, showcasing its superiority over competing methods. This efficiency is further illustrated with two real microbiome count datasets. The method is implemented in R.