Metaproteomics is an effective tool for characterizing the functional profiles of microbial communities by directly measuring protein expression. However, prospective power analysis and sample-size estimation are often overlooked at the study design stage in metaproteomics, which can result in underpowered experiments and reduced ability to detect biologically meaningful effects. In this study, we present a practical, end-to-end workflow for conducting power analysis prior to data collection. We focus on three common experimental designs: between-group comparisons, parallelized perturbation experiments, and beta diversity analyses. To tailored these experimental designs, we consider three major statistical approaches for power estimation: parametric tests (e.g., t-test, ANOVA), non-parametric tests (e.g., Wilcoxon rank-sum, Kruskal-Wallis), and distance-based multivariate methods (e.g., PERMANOVA using Bray-Curtis). By presenting detailed case studies, we provide practical guidance on how to calculate effect sizes, generate simulated datasets, and estimate statistical power across varying sample sizes. We also supply corresponding visualizations for each scenario to support sample-size determination and power assessment. This framework is intended to help researchers optimize sample size, improve experimental efficiency, and reduce costs, thereby enabling more reliable and interpretable biological insights from metaproteomic studies.
Wang et al. (Tue,) studied this question.