multi.impute | R Documentation |
multi.impute
performs multiple imputation on a
given quantitative proteomics dataset.
multi.impute(data, conditions, nb.imp = NULL, method, parallel = FALSE)
data |
A quantitative matrix to be imputed, with proteins/peptides in rows and samples in columns. |
conditions |
A vector of length the number of samples where each element corresponds to the condition the sample belongs to. |
nb.imp |
The number of imputation to perform. |
method |
A single character string describing the imputation method to be used. See details. |
parallel |
Logical, whether or not use parallel computing
(with |
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
.
A numeric array of dimension c(dim(data),nb.imp).
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")
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