Evaluation of imputation and imputation-free strategies for differential abundance analysis in metaproteomics data.

ABSTRACT: 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

SUBMITTER: Mou X 

PROVIDER: S-EPMC12009712 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature