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Bioconductor - MAI

MAI

This is the released version of MAI; for the devel version, see MAI.

Mechanism-Aware Imputation

Bioconductor version: Release (3.21)

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 CUAnschutz.edu>

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

To install this package, start R (version "4.5") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("MAI")

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

Documentation

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

browseVignettes("MAI")

Details biocViews Classification, Metabolomics, Software, StatisticalMethod Version 1.14.0 In Bioconductor since BioC 3.14 (R-4.1) (3.5 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 System Requirements URL https://github.com/KechrisLab/MAI Bug Reports https://github.com/KechrisLab/MAI/issues See More Package Archives

Follow Installation instructions to use this package in your R session.

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