  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 be used both at peptide and protein-level
in quantification datasets. For protein-level results based on peptide-level
quantification data, an aggregation step is also included. Our methodology,
named mi4p, was compared to the state-of-the-art limma workflow implemented in
the DAPAR R package, both on simulated and real datasets. We observed a
trade-off between sensitivity and specificity, while the overall performance of
mi4p outperforms DAPAR in terms of F-Score.
