Towards a More Accurate Differential Analysis of Multiple Imputed Proteomics Data with mi4limma.
Fiche publication
Date publication
janvier 2023
Journal
Methods in molecular biology (Clifton, N.J.)
Auteurs
Membres identifiés du Cancéropôle Est :
Dr CARAPITO Christine
Tous les auteurs :
Chion M, Carapito C, Bertrand F
Lien Pubmed
Résumé
Imputing missing values is a common practice in label-free quantitative proteomics. Imputation replaces a missing value by a user-defined one. However, the imputation itself is not optimally considered downstream of the imputation process. In particular, imputed datasets are considered as if they had always been complete. The uncertainty due to the imputation is not properly taken into account. Hence, the mi4p package provides a more accurate statistical analysis of multiple-imputed datasets. A rigorous multiple imputation methodology is implemented, leading to a less biased estimation of parameters and their 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.
Mots clés
Differential analysis, Label-free quantitative proteomics, Missing values, Moderated t-testing, Multiple imputation
Référence
Methods Mol Biol. 2023 ;2426:131-140