Package: mi4p 1.3

Frederic Bertrand

mi4p: Multiple Imputation for Proteomics

A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) <doi:10.1371/journal.pcbi.1010420>. It is dedicated to dealing with multiple imputation for proteomics.

Authors:Marie Chion [aut], Christine Carapito [aut], Frederic Bertrand [cre, aut], Gordon Smyth [ctb], Davis McCarthy [ctb], Hélène Borges [ctb], Thomas Burger [ctb], Quentin Giai-Gianetto [ctb], Samuel Wieczorek [ctb]

mi4p_1.3.tar.gz
mi4p_1.3.zip(r-4.7)mi4p_1.3.zip(r-4.6)mi4p_1.3.zip(r-4.5)
mi4p_1.3.tgz(r-4.6-any)mi4p_1.3.tgz(r-4.5-any)
mi4p_1.3.tar.gz(r-4.7-any)mi4p_1.3.tar.gz(r-4.6-any)
mi4p_1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mi4p/json (API)
NEWS

# Install 'mi4p' in R:
install.packages('mi4p', repos = c('https://mariechion.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mariechion/mi4p/issues

Pkgdown/docs site:https://mariechion.github.io

Datasets:

On CRAN:

Conda:

5.09 score 7 stars 35 scripts 249 downloads 26 exports 147 dependencies

Last updated from:d8ed5ecc6f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK246
source / vignettesOK306
linux-release-x86_64OK241
macos-release-arm64OK187
macos-oldrel-arm64OK213
windows-develOK174
windows-releaseOK145
windows-oldrelOK153
wasm-releaseOK227

Exports:check.conditionscheck.designeBayes.modformatLimmaResulthid.ebayeslimmaCompleteTest.modmake.contrastmake.designmake.design.1make.design.2make.design.3meanImp_emmeansmi4limmamulti.imputeMVgenproj_matrixprotdatasimrubin1.allrubin1.onerubin2.allrubin2bt.allrubin2bt.onerubin2wt.allrubin2wt.onetest.designwithin_variance_comp_emmeans

Dependencies:abindbackportsbase64encbitbit64bootbroombslibcachemcarcarDataclicliprclustercodetoolscolorspacecowplotcpp11crayoncrosstalkDerivdigestdoBydoParalleldoRNGdplyrDTellipseemmeansestimabilityevaluateFactoMineRfarverfastmapflashClustfontawesomeforcatsforeachforecastFormulafracdifffsgenericsggplot2ggrepelglmnetgluegtablehavenhighrhmshtmltoolshtmlwidgetsimp4pimputeIsoisobanditeratorsitertoolsjomojquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelimmalme4lmtestmagrittrMASSMatrixMatrixModelsmemoisemgcvmicemicrobenchmarkmimeminqamissForestmissMDAmitmlmodelrmultcompViewmvtnormnlmenloptrnnetnormnumDerivordinalotelpanpbkrtestpillarpkgconfigprettyunitsprogresspromisespurrrquantregR6randomForestrangerrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreformulasrlangrmarkdownrngtoolsrpartS7sassscalesscatterplot3dshapeSparseMstatmodstringistringrsurvivaltibbletidyrtidyselecttimeDatetinytextruncnormtzdbucminfurcautf8vctrsviridisLitevroomwithrxfunyamlzoo

Multiple imputation for proteomics

Marie Chion, Université de Strasbourg et CNRS, IRMA, labex IRMIA, LSMBO, IPHC, [email protected], Christine Carapito, Université de Strasbourg et CNRS, LSMBO, IPHC, [email protected], Frédéric Bertrand, Université de Strasbourg et CNRS, IRMA, labex IRMIA, Université de technologie de Troyes, LIST3N, [email protected]

Rendered fromIntromi4p.Rmdusingknitr::rmarkdownon May 17 2026.

Last update: 2025-09-19
Started: 2021-08-11

Readme and manuals

Help Manual

Help pageTopics
Check if the design is validcheck.conditions
Check if the design is validcheck.design
A single simulated datasetdatasim
MI-aware Modifed eBayes FunctioneBayes.mod
Format a Result from LimmaformatLimmaResult
MI-aware Modifed eBayes Functionhid.ebayes
Computes a hierarchical differential analysislimmaCompleteTest.mod
Builds the contrast matrixmake.contrast
Builds the design matrixmake.design
Builds the design matrix for designs of level 1make.design.1
Builds the design matrix for designs of level 2make.design.2
Builds the design matrix for designs of level 3make.design.3
Multiple Imputation EstimatemeanImp_emmeans
Differential analysis after multiple imputationmi4limma
mm_peptides - peptide-level intensities for mousemm_peptides
Multiple imputation of quantitative proteomics datasetsmulti.impute
Amputation of a datasetMVgen
A list of simulated datasets.norm.200.m100.sd1.vs.m200.sd1.list
Variance-Covariance Matrix Projectionproj_matrix
Data simulation functionprotdatasim
Extract of the abundances of Exp1_R25_pept datasetqData
First Rubin rule (all peptides)rubin1.all
First Rubin rule (a given peptide)rubin1.one
Computes the 2nd Rubin's rule (all peptides)rubin2.all
2nd Rubin's rule Between-Imputation component (all peptides)rubin2bt.all
2nd Rubin's rule Between-Imputation Component (a given peptide)rubin2bt.one
2nd Rubin's rule Within-Variance Component (all peptides)rubin2wt.all
2nd Rubin's rule Within-Variance Component (a given peptide)rubin2wt.one
Experimental design for the Exp1_R25_pept datasetsTab
Check if xxxxxxtest.design
Multiple Imputation Within Variance Componentwithin_variance_comp_emmeans