imp4p-package: Introduction to the IMP4P package
In imp4p: Imputation for Proteomics
Description
Author(s)
References
Description
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. An example for using this package from MaxQuant outputs is provided in Giai Gianetto Q. (2021).
More explanations and details on the functions of this package are available in Giai Gianetto Q. et al. (2020) (doi: doi: 10.1101/2020.05.29.122770).
Author(s)
Maintainer: Quentin Giai Gianetto <quentin2g@yahoo.fr>
References
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: doi: 10.1101/2020.05.29.122770
Giai Gianetto, Q. (2021) Statistical analysis of post-translational modifications quantified by label-free proteomics across multiple biological conditions with R: illustration from SARS-CoV-2 infected cells. (pasteur-03243433)
imp4p documentation built on Sept. 5, 2021, 5:38 p.m.
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Description Author(s) References
Description
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. An example for using this package from MaxQuant outputs is provided in Giai Gianetto Q. (2021).
More explanations and details on the functions of this package are available in Giai Gianetto Q. et al. (2020) (doi: doi: 10.1101/2020.05.29.122770).
Author(s)
Maintainer: Quentin Giai Gianetto <quentin2g@yahoo.fr>
References
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: doi: 10.1101/2020.05.29.122770
Giai Gianetto, Q. (2021) Statistical analysis of post-translational modifications quantified by label-free proteomics across multiple biological conditions with R: illustration from SARS-CoV-2 infected cells. (pasteur-03243433)
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