MAI

DOI: 10.18129/B9.bioc.MAI  

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see MAI.

Mechanism-Aware Imputation

Bioconductor version: 3.17

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>

Installation

To install this package, start R (version "4.3") 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")

 

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

Details

biocViews	Classification, Metabolomics, Software, StatisticalMethod
Version	1.6.0
In Bioconductor since	BioC 3.14 (R-4.1) (2 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
LinkingTo
Suggests	knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0)
SystemRequirements
Enhances
URL	https://github.com/KechrisLab/MAI
BugReports	https://github.com/KechrisLab/MAI/issues
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

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

Source Package	MAI_1.6.0.tar.gz
Windows Binary	MAI_1.6.0.zip
macOS Binary (x86_64)	MAI_1.6.0.tgz
macOS Binary (arm64)	MAI_1.6.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/MAI
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/MAI
Bioc Package Browser	https://code.bioconductor.org/browse/MAI/
Package Short Url	https://bioconductor.org/packages/MAI/
Package Downloads Report	Download Stats