Performance determinants of unsupervised clustering methods for microbiome data
Year: 2022
Type: article
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. In contrast, the unweighted UniFrac (UU) metric clustered poorly on dataset with a high prevalence of low-abundance OTUs. To explore these hypotheses about BC and UU, we systematically modified the properties of the poorly performing datasets and found that this approach resulted in improved BC and UU performance. Based on these observations, we rationally combined BC and UU to generate a novel metric. We tested its performance while varying the relative contributions of each metric and also compared it with another combined metric, the generalized UniFrac distance. The proposed metric showed high performance across all datasets. CONCLUSIONS: Our systematic evaluation of clustering performance in these five datasets demonstrates that there is no existing clustering method that universally performs best across all datasets. We propose a combined metric of BC and UU that capitalizes on the complementary strengths of the two metrics. Video abstract.
Source: Microbiome
Language: English
Topic: Gut microbiota and health
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,390
Funders: Cancer Prevention and Research Institute of Texas, National Institutes of Health, University of Texas MD Anderson Cancer Center, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of General Medical Sciences, National Center for Advancing Translational Sciences
Awards: Center for Clinical and Translational Sciences (CCTS), MD Anderson Cancer Center Prostate Cancer SPORE, G293715275, Recruitment of Rising Stars, G3929693838, G4276892241, Viral miRs and cellular miRs in sepsis, Acute Myeloid Leukemia in the Immunosuppressed Microenvironment, Protecting colonic mucus to mitigate acute intestinal graft-versus-host disease, G5059082296, G5520316217, G6368863098, G6906829653, G9000348896, Cancer Center Support Grant, New data science approaches to visualize and understand the impact of the microbiome on risk of graft-versus-host disease
OpenAlex ID: W4221116063