A Graphical Model for Fusing Diverse Microbiome Data

TL;DR

This paper introduces a Bayesian graphical model that fuses diverse high-dimensional microbiome count data by modeling them through a shared latent Gaussian space, enabling integrated analysis and visualization.

Contribution

The paper presents a novel multinomial-Gaussian generative model and a scalable variational EM algorithm for joint modeling of disparate microbiome datasets.

Findings

Effective data fusion demonstrated on microbiome dataset

Model provides a common latent space for visualization

Scalable inference method suitable for high-dimensional data

Abstract

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high dimensional features, in this case transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each correspond to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. The inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.