Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.

ABSTRACT: In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the "best" ones. However, if only the best results are selectively reported, this may cause over-optimism: the "best" method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome a

SUBMITTER: Ullmann T 

PROVIDER: S-EPMC9873197 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature