Table 1.

Statistical Approaches to Impute Missing Intensity Values

method	description	R package	method reference	R parameters
BPCA	the posterior distribution of the model parameters and the missing values are estimated using a variational Bayes algorithm	pcaMethods24 (Bioconductor)	Oba et al.25	nPcs = 3 method = “bpca”
EM	expectation maximization: the observed data are used to estimate missing data via penalized likelihood expectation maximization	PEMM26 v 1.0 (CRAN)	Chen et al.27	phi = 0
IRMI	iterative robust model-based imputation: each peptide with missing values is iteratively used as a response variable in linear regression while the remaining peptides are used as explanatory variables	VIM28 v.5.1.0 (CRAN)	Templ et al.29
kNN	k-nearest neighbors: values are imputed using a weighted average intensity of k most similar peptides	VIM28 v.5.1.0 (CRAN)	Kowarik et al.28	k = 5
LLS	local least-squares: the missing values are imputed based on linear locally weighted least-squares regression	imputation30 v 2.0.1 leveraging locfit31 v 1.5–9.1 (Github)	Loader32
MEAN	mean replacement: missing values are filled in with the mean observed value for the respective peptide
MICE	multivariate imputation by chained equations: multiple imputation method that replaces missing values by predictive mean matching	mice33 v 3.8.0 (CRAN)	Little34	m = 5
PCA	principal component analysis: runs PCA, imputes the missing values with the regularized reconstruction formulas and repeats until convergence	missMDA35 v 1.16.0 (CRAN)	Josse et al.36	ncp = 3
RF	random forest: nonparametric method to impute missing values using a random forest trained on the observed parts of the data set, repeated iteratively until convergence	MissForest37 v 1.4 (CRAN)	Stekhoven et al.38	ntree = 100