Performance determinants of unsupervised clustering methods for microbiome data.

ABSTRACT:

Background

In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We applied these to four published datasets where highly distinct microbiome profiles could be seen between sample groups, as well a clinical dataset with less clear separation between groups.

Results

Although no single method outperformed the others consistently, we did identify the key scenarios where certain methods can underperform. Specifically, the Bray Curtis (BC) metric resulted in poor clustering in a dataset where high-abundance OTUs were relatively rare.

SUBMITTER: Shi Y 

PROVIDER: S-EPMC8817542 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature