R/mi4p-package.R

In mi4p: Multiple Imputation for Proteomics

#' mi4p: Multiple imputation for proteomics
#' 
#' 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.
#' 
#' @docType package
#' @name mi4p-package
#' @aliases mi4p-package mi4p
#' @author This package has been written by Marie Chion, Christine Carapito and 
#' Frederic Bertrand.  
#' Maintainer: <frederic.bertrand@@utt.fr>
#' 
#' @references 
#' M. Chion, Ch. Carapito and F. Bertrand (2021). \emph{Accounting for multiple 
#' imputation-induced variability for differential analysis in mass 
#' spectrometry-based label-free quantitative proteomics}. arxiv:2108.07086. 
#' \url{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
#' \url{https://hal.archives-ouvertes.fr/hal-03442944}
#' 
#' @keywords package
#' 
#' @importFrom stats lm median pchisq pf pt vcov
#' @importFrom foreach %dopar%
#' @importFrom stats rnorm
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