Multidimensional scaling improves distance-based clustering for microbiome data.

ABSTRACT:

Motivation

Clustering patients into subgroups based on their microbial compositions can greatly enhance our understanding of the role of microbes in human health and disease etiology. Distance-based clustering methods, such as partitioning around medoids (PAM), are popular due to their computational efficiency and absence of distributional assumptions. However, the performance of these methods can be suboptimal when true cluster memberships are driven by differences in the abundance of only a few microbes, a situation known as the sparse signal scenario.

Results

We demonstrate that classical multidimensional scaling (MDS), a widely used dimensionality reduction technique, effectively denoises microbiome data and enhances the clustering performance of distance-based methods.

SUBMITTER: Chen G 

PROVIDER: S-EPMC11814494 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature