MAI

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

Mechanism-Aware Imputation

Bioconductor version: Release (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 CUAnschutz.edu>

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

Installation

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

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

Need some help? Ask on the Bioconductor Support site!

Details

biocViews	Classification, Metabolomics, Software, StatisticalMethod
Version	1.12.0
In Bioconductor since	BioC 3.14 (R-4.1) (3 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

Suggests	knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0)
Linking To
Enhances
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report	Build Report

Package Archives

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

Source Package	MAI_1.12.0.tar.gz
Windows Binary (x86_64)	MAI_1.12.0.zip
macOS Binary (x86_64)	MAI_1.12.0.tgz
macOS Binary (arm64)	MAI_1.12.0.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