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This is the development version of MAI; for the stable release version, see MAI.

Mechanism-Aware Imputation

Bioconductor version: Development (3.20)

A two-step approach to imputing missing data in metabolomics. Step 1 uses a random forest classifier to classify missing values as either Missing Completely at Random/Missing At Random (MCAR/MAR) or Missing Not At Random (MNAR). MCAR/MAR are combined because it is often difficult to distinguish these two missing types in metabolomics data. Step 2 imputes the missing values based on the classified missing mechanisms, using the appropriate imputation algorithms. Imputation algorithms tested and available for MCAR/MAR include Bayesian Principal Component Analysis (BPCA), Multiple Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and Random Forest. Imputation algorithms tested and available for MNAR include nsKNN and a single imputation approach for imputation of metabolites where left-censoring is present.

Author: Jonathan Dekermanjian [aut, cre], Elin Shaddox [aut], Debmalya Nandy [aut], Debashis Ghosh [aut], Katerina Kechris [aut]

Maintainer: Jonathan Dekermanjian <Jonathan.Dekermanjian at>

Citation (from within R, enter citation("MAI")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

Utilizing Mechanism-Aware Imputation (MAI) HTML R Script
Reference Manual PDF


biocViews Classification, Metabolomics, Software, StatisticalMethod
Version 1.11.0
In Bioconductor since BioC 3.14 (R-4.1) (2.5 years)
License GPL-3
Depends R (>= 3.5.0)
Imports caret, parallel, doParallel, foreach, e1071, future.apply, future, missForest, pcaMethods, tidyverse, stats, utils, methods, SummarizedExperiment, S4Vectors
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