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MAI

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

Mechanism-Aware Imputation


Bioconductor version: Development (3.19)

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")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

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

Details

biocViews Classification, Metabolomics, Software, StatisticalMethod
Version 1.9.0
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
System Requirements
URL https://github.com/KechrisLab/MAI
Bug Reports https://github.com/KechrisLab/MAI/issues
See More
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_1.9.0.tar.gz
Windows Binary MAI_1.9.0.zip (64-bit only)
macOS Binary (x86_64) MAI_1.9.0.tgz
macOS Binary (arm64) MAI_1.9.0.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