| imp4p-package {imp4p} | R Documentation |
This package provides functions to analyse missing value mechanisms in the context of bottom-up MS-based quantitative proteomics.
It allows estimating a mixture model of missing completely-at-random (MCAR) values and missing not-at-random (MNAR) values.
It also contains functions allowing the imputation of missing values under hypotheses of MCAR and/or MNAR values.
The main functions of this package are the estim.mix (estimation of a model of MCAR and MNAR (left-censored) values), impute.mi (multiple imputation) and impute.mix (imputation based on a decision rule). It provides also several imputation algorithms for MS-based data. They can be used to impute matrices containing peptide intensities (as Maxquant outputs for instance).
Missing values has to be indicated with NA and a log-2 transformation of the intensities has to be applied before using these functions.
More explanations and details on the functions of this package are available in Giai Gianetto et al.(2020).
Maintainer: Quentin Giai Gianetto <quentin2g@yahoo.fr>
Giai Gianetto, Q., Wieczorek S., Couté Y., Burger, T. (2020). A peptide-level multiple imputation strategy accounting for the different natures of missing values in proteomics data. bioRxiv 2020.05.29.122770; doi: 10.1101/2020.05.29.122770