Evaluation of statistical approaches for differential metaproteomics.

ABSTRACT: Metaproteomics characterizes and compares molecular phenotypes of organisms in communities by comprehensively analyzing their protein expression profiles using statistical methods. However, not all statistical methods are suitable for determining differentially abundant protein groups in metaproteomic analyses. Statistical challenges in metaproteomics include: data sparsity, non-normality, compositionality, and large between-sample variability. These challenges can potentially be addressed with several data processing steps, including imputation, normalization, transformation, and selection of the appropriate statistical tests. The potential combinations of different processing methods create a complex matrix of analysis options and it is currently unclear how these combinations impact the

SUBMITTER: Hinzke T 

PROVIDER: S-EPMC12709467 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature