A Graphical Model for Fusing Diverse Microbiome Data
- URL: http://arxiv.org/abs/2208.09934v1
- Date: Sun, 21 Aug 2022 17:54:39 GMT
- Title: A Graphical Model for Fusing Diverse Microbiome Data
- Authors: Mehmet Aktukmak, Haonan Zhu, Marc G. Chevrette, Julia Nepper, Jo
Handelsman, Alfred Hero
- Abstract summary: We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data.
We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model.
- Score: 2.385985842958366
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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