Xinyi Mou

For metaproteomics data derived from the collective protein composition of dynamic multi-organism systems, the proportion of missing values and dimensions of data exceeds that observed in single-organism experiments. Consequently, evaluations of differential analysis strategies in other mass spectrometry (MS) data (such as proteomics and metabolomics) may not be directly applicable to metaproteomics data. In this study, we systematically evaluated five imputation methods [sample minimum, quantile regression, k-nearest neighbors (KNN), Bayesian principal component analysis (bPCA), random forest (RF)] and six imputation-free methods (moderated t-test, two-part t-test, two-part Wilcoxon test, semiparametric differential abundance analysis, differential abundance analysis with Bayes shrinkage estimation of variance method, and Mixture) for differential analysis in simulated metaproteomic datasets based on both data-dependent acquisition MS experiments and emerging data-independent acquisition experiments. The simulation datasets comprised 588 scenarios by considering the impacts of sample size, fold change between case and control, and missing value ratio at random and nonrandom. Compared to imputation-free methods, KNN, bPCA, and RF imputation performed poorly in datasets with a high missingness ratio and large sample size and resulted in a high false-positive risk. We made empirical recommendations based on the balance of sensitivity in analysis and control of false positives. The moderated t-test was optimal in scenarios of large sample size with a low missingness ratio. The two-part Wilcoxon test was recommended in scenarios of small sample size with a low missingness ratio or large sample size with a high missingness ratio. The comprehensive evaluations in our study can provide guidance for the differential abundance analysis in metaproteomics.