mi4p-package: mi4p: Multiple imputation for proteomics

In mi4p: Multiple Imputation for Proteomics

mi4p: Multiple imputation for proteomics

Description

Imputing missing values is 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.

Author(s)

This package has been written by Marie Chion, Christine Carapito and Frederic Bertrand. Maintainer: <[e-mail]>

References

M. Chion, Ch. Carapito and F. Bertrand (2021). Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics. arxiv:2108.07086. https://arxiv.org/abs/2108.07086.

M. Chion, Ch. Carapito, F. Bertrand. Towards a more accurate differential analysis of multiple imputed proteomics data with mi4limma. Statistical Analysis of Proteomic Data: Methods and Tools, 2022. hal-03442944 https://hal.archives-ouvertes.fr/hal-03442944

mi4p documentation built on March 31, 2023, 6:23 p.m.