DOI: 10.18129/B9.bioc.MAI    

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

Mechanism-Aware Imputation

Bioconductor version: Development (3.15)

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>

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biocViews Classification, Metabolomics, Software, StatisticalMethod
Version 1.1.0
In Bioconductor since BioC 3.14 (R-4.1) (< 6 months)
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
Suggests knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0)
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