Stochastic variational variable selection for high-dimensional microbiome data.

ABSTRACT:

Background

The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the approach of the existing methods, the computational burden of identifying a small number of representative species from a large number of observed species remains a challenge.

Results

We propose a novel approach to improve the performance of the widely used DMM approach by combining three ideas: (i) we propose an indicator variable to identify representative operational taxonomic units

SUBMITTER: Dang T 

PROVIDER: S-EPMC9789572 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature