Package: mi4p 1.1

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) <arxiv:2108.07086>. 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_1.1.tar.gz
mi4p_1.1.zip(r-4.5)mi4p_1.1.zip(r-4.4)mi4p_1.1.zip(r-4.3)
mi4p_1.1.tgz(r-4.4-any)mi4p_1.1.tgz(r-4.3-any)
mi4p_1.1.tar.gz(r-4.5-noble)mi4p_1.1.tar.gz(r-4.4-noble)
mi4p_1.1.tgz(r-4.4-emscripten)mi4p_1.1.tgz(r-4.3-emscripten)
mi4p.pdf |mi4p.html✨
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

Datasets:

datasim - A single simulated dataset
mm_peptides - Mm_peptides - peptide-level intensities for mouse
norm.200.m100.sd1.vs.m200.sd1.list - A list of simulated datasets.
qData - Extract of the abundances of Exp1_R25_pept dataset
sTab - Experimental design for the Exp1_R25_pept dataset

On CRAN:

26 exports 6 stars 1.27 score 136 dependencies 33 scripts 342 downloads

Last updated 1 years agofrom:af0b351500. Checks:OK: 3 ERROR: 4. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 01 2024
R-4.5-win	ERROR	Sep 01 2024
R-4.5-linux	ERROR	Sep 01 2024
R-4.4-win	ERROR	Sep 01 2024
R-4.4-mac	ERROR	Sep 01 2024
R-4.3-win	OK	Sep 01 2024
R-4.3-mac	OK	Sep 01 2024

Exports:check.conditions check.design eBayes.mod formatLimmaResult hid.ebayes limmaCompleteTest.mod make.contrast make.design make.design.1 make.design.2 make.design.3 meanImp_emmeans mi4limma multi.impute MVgen proj_matrix protdatasim rubin1.all rubin1.one rubin2.all rubin2bt.all rubin2bt.one rubin2wt.all rubin2wt.one test.design within_variance_comp_emmeans

Dependencies:abind backports base64enc bit bit64 boot broom bslib cachem car carData cli clipr cluster codetools colorspace cowplot cpp11 crayon crosstalk Deriv digest doBy doParallel doRNG dplyr DT ellipse emmeans estimability evaluate FactoMineR fansi farver fastmap flashClust fontawesome forcats foreach fs generics ggplot2 ggrepel glmnet glue gtable haven highr hms htmltools htmlwidgets httpuv imp4p impute Iso isoband iterators itertools jomo jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle limma lme4 magrittr MASS Matrix MatrixModels memoise mgcv mice microbenchmark mime minqa missForest missMDA mitml modelr multcompView munsell mvtnorm nlme nloptr nnet norm numDeriv ordinal pan pbkrtest pillar pkgconfig prettyunits progress promises purrr quantreg R6 randomForest rappdirs RColorBrewer Rcpp RcppEigen readr rlang rmarkdown rngtools rpart sass scales scatterplot3d shape SparseM statmod stringi stringr survival tibble tidyr tidyselect tinytex truncnorm tzdb ucminf utf8 vctrs viridisLite vroom withr xfun yaml

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 Sep 01 2024.

Last update: 2022-03-15
Started: 2021-08-11

Help page	Topics
mi4p: Multiple imputation for proteomics	mi4p-package mi4p
Check if the design is valid	check.conditions
Check if the design is valid	check.design
A single simulated dataset	datasim
MI-aware Modifed eBayes Function	eBayes.mod
Format a Result from Limma	formatLimmaResult
MI-aware Modifed eBayes Function	hid.ebayes
Computes a hierarchical differential analysis	limmaCompleteTest.mod
Builds the contrast matrix	make.contrast
Builds the design matrix	make.design
Builds the design matrix for designs of level 1	make.design.1
Builds the design matrix for designs of level 2	make.design.2
Builds the design matrix for designs of level 3	make.design.3
Multiple Imputation Estimate	meanImp_emmeans
Differential analysis after multiple imputation	mi4limma
mm_peptides - peptide-level intensities for mouse	mm_peptides
Multiple imputation of quantitative proteomics datasets	multi.impute
Amputation of a dataset	MVgen
A list of simulated datasets.	norm.200.m100.sd1.vs.m200.sd1.list
Variance-Covariance Matrix Projection	proj_matrix
Data simulation function	protdatasim
Extract of the abundances of Exp1_R25_pept dataset	qData
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 dataset	sTab
Check if xxxxxx	test.design
Multiple Imputation Within Variance Component	within_variance_comp_emmeans

Package: mi4p 1.1

mi4p: Multiple Imputation for Proteomics

Multiple imputation for proteomics

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)