
Multiple imputation for proteomics
Marie Chion, Université de Strasbourg et CNRS, IRMA, labex IRMIA, LSMBO, IPHC, marie.chion@protonmail.fr
Christine Carapito, Université de Strasbourg et CNRS, LSMBO, IPHC, ccarapito@unistra.fr
Frédéric Bertrand, Université de Strasbourg et CNRS, IRMA, labex IRMIA, Université de technologie de Troyes, LIST3N, frederic.bertrand@lecnam.net
2025-09-19
Source:vignettes/Intromi4p.Rmd
Intromi4p.Rmd
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mi4p: multiple imputation for proteomics
This repository contains the R code and package for the mi4p methodology (Multiple Imputation for Proteomics), proposed by Marie Chion, Christine Carapito and Frédéric Bertrand (2021) in Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics, https://arxiv.org/abs/2108.07086.
The following material is available on the Github repository of the package https://github.com/mariechion/mi4p/.
The
Functions
folder contains all the functions used for the workflow.The
Simulation-1
,Simulation-2
andSimulation-3
folders contain all the R scripts and data used to conduct simulated experiments and evaluate our methodology.The
Arabidopsis_UPS
andYeast_UPS
folders contain all the R scripts and data used to challenge our methodology on real proteomics datasets. Raw experimental data were deposited with the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD003841 and PXD027800.
This website and these examples were created by M. Chion, C. Carapito and F. Bertrand.
Installation
You can install the released version of mi4p from CRAN with:
install.packages("mi4p")
You can install the development version of mi4p from github with:
devtools::install_github("mariechion/mi4p")
Examples
First section
set.seed(4619)
datasim <- protdatasim()
str(datasim)
#> 'data.frame': 200 obs. of 11 variables:
#> $ id.obs: int 1 2 3 4 5 6 7 8 9 10 ...
#> $ X1 : num 99.6 99.9 100.2 99.8 100.4 ...
#> $ X2 : num 97.4 101.3 100.3 100.2 101.7 ...
#> $ X3 : num 100.3 100.9 99.1 101.2 100.6 ...
#> $ X4 : num 99.4 99.2 98.5 99.1 99.5 ...
#> $ X5 : num 98.5 99.7 100 100.2 100.7 ...
#> $ X6 : num 200 199 199 200 199 ...
#> $ X7 : num 200 200 202 199 199 ...
#> $ X8 : num 202 199 200 199 201 ...
#> $ X9 : num 200 200 199 201 200 ...
#> $ X10 : num 200 198 200 201 199 ...
#> - attr(*, "metadata")='data.frame': 10 obs. of 3 variables:
#> ..$ Sample.name: chr [1:10] "X1" "X2" "X3" "X4" ...
#> ..$ Condition : Factor w/ 2 levels "A","B": 1 1 1 1 1 2 2 2 2 2
#> ..$ Bio.Rep : int [1:10] 1 2 3 4 5 6 7 8 9 10
attr(datasim, "metadata")
#> Sample.name Condition Bio.Rep
#> 1 X1 A 1
#> 2 X2 A 2
#> 3 X3 A 3
#> 4 X4 A 4
#> 5 X5 A 5
#> 6 X6 B 6
#> 7 X7 B 7
#> 8 X8 B 8
#> 9 X9 B 9
#> 10 X10 B 10
AMPUTATION
MV1pct.NA.data <- MVgen(dataset = datasim[,-1], prop_NA = 0.01)
MV1pct.NA.data
IMPUTATION
MV1pct.impMLE <- multi.impute(data = MV1pct.NA.data, conditions = attr(datasim,"metadata")$Condition, method = "MLE", parallel = FALSE)
ESTIMATION
print(paste(Sys.time(), "Dataset", 1, "out of", 1))
MV1pct.impMLE.VarRubin.Mat <- rubin2.all(data = MV1pct.impMLE, metacond = attr(datasim, "metadata")$Condition)
MODERATED T-TEST
MV1pct.impMLE.mi4limma.res <- mi4limma(qData = apply(MV1pct.impMLE,1:2,mean),
sTab = attr(datasim, "metadata"),
VarRubin = sqrt(MV1pct.impMLE.VarRubin.S2))
MV1pct.impMLE.mi4limma.res
(simplify2array(MV1pct.impMLE.mi4limma.res)$P_Value.A_vs_B_pval)[1:10]
(simplify2array(MV1pct.impMLE.mi4limma.res)$P_Value.A_vs_B_pval)[11:200]<=0.05
True positive rate
sum((simplify2array(MV1pct.impMLE.mi4limma.res)$P_Value.A_vs_B_pval)[1:10]<=0.05)/10
False positive rate
sum((simplify2array(MV1pct.impMLE.mi4limma.res)$P_Value.A_vs_B_pval)[11:200]<=0.05)/190
MV1pct.impMLE.dapar.res <-limmaCompleteTest.mod(qData = apply(MV1pct.impMLE,1:2,mean), sTab = attr(datasim, "metadata"))
MV1pct.impMLE.dapar.res
Simulate a list of 100 datasets.
set.seed(4619)
norm.200.m100.sd1.vs.m200.sd1.list <- lapply(1:100, protdatasim)
metadata <- attr(norm.200.m100.sd1.vs.m200.sd1.list[[1]],"metadata")
100 datasets with parallel comuting support. Quite long to run even with parallel computing support.
library(foreach)
doParallel::registerDoParallel(cores=NULL)
requireNamespace("foreach",quietly = TRUE)
IMPUTATION
MV1pct.impMLE <- foreach::foreach(iforeach = MV1pct.NA.data,
.errorhandling = 'stop', .verbose = F) %dopar%
multi.impute(data = iforeach, conditions = metadata$Condition,
method = "MLE", parallel = F)
PROJECTION
MV1pct.impMLE.VarRubin.S2 <- lapply(1:length(MV1pct.impMLE.VarRubin.Mat), function(id.dataset){
print(paste("Dataset", id.dataset, "out of",length(MV1pct.impMLE.VarRubin.Mat), Sys.time()))
as.numeric(lapply(MV1pct.impMLE.VarRubin.Mat[[id.dataset]], function(aaa){
DesMat = mi4p::make.design(metadata)
return(max(diag(aaa)%*%t(DesMat)%*%DesMat))
}))
})
MODERATED T-TEST
MV1pct.impMLE.mi4limma.res <- foreach(iforeach = 1:100, .errorhandling = 'stop', .verbose = T) %dopar%
mi4limma(qData = apply(MV1pct.impMLE[[iforeach]],1:2,mean),
sTab = metadata,
VarRubin = sqrt(MV1pct.impMLE.VarRubin.S2[[iforeach]]))
MV1pct.impMLE.dapar.res <- foreach(iforeach = 1:100, .errorhandling = 'stop', .verbose = T) %dopar%
limmaCompleteTest.mod(qData = apply(MV1pct.impMLE[[iforeach]],1:2,mean),
sTab = metadata)