Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics.

ABSTRACT: Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if they had always been complete. Hence, the uncertainty due to the imputation is not adequately taken into account. We provide a rigorous multiple imputation strategy, leading to a less biased estimation of the parameters' variability thanks to Rubin's rules. The imputation-based peptide's intensities' variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results. This workflow can

SUBMITTER: Chion M 

PROVIDER: S-EPMC9462777 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature