multi.impute performs multiple imputation on a
given quantitative proteomics dataset.
multi.impute(data, conditions, nb.imp = NULL, method, parallel = FALSE)A numeric array of dimension c(dim(data),nb.imp).
A quantitative matrix to be imputed, with proteins/peptides in rows and samples in columns.
A vector of length the number of samples where each element corresponds to the condition the sample belongs to.
The number of imputation to perform.
A single character string describing the imputation method to be used. See details.
Logical, whether or not use parallel computing
(with foreach).
Multiple imputation consists in imputing several times a given
dataset using a given method. Here, imputation methods can be chosen either
from mice, imp4p-package or
impute.knn:
"pmm", "midastouch", "sample", "cart", "rf","mean", "norm",
"norm.nob", "norm.boot", "norm.predict": imputation methods as described
in mice.
"RF" imputes missing values using random forests algorithm as
described in impute.RF.
"MLE" imputes missing values using maximum likelihood estimation
as described in impute.mle.
"PCA" imputes missing values using principal component analysis as
described in impute.PCA.
"SLSA" imputes missing values using structured least squares
algorithm as described in impute.slsa.
"kNN" imputes missing values using k nearest neighbors as
described in impute.knn.
M. Chion, Ch. Carapito and F. Bertrand (2021). Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics. arxiv:2108.07086. https://arxiv.org/abs/2108.07086.
library(mi4p)
data(datasim)
multi.impute(data = datasim[,-1], conditions = attr(datasim,"metadata")$Condition, method = "MLE")
Run the code above in your browser using DataLab