MAI

DOI: 10.18129/B9.bioc.MAI    

This is the development version of MAI; to use it, please install the devel version of Bioconductor.

Mechanism-Aware Imputation

Bioconductor version: Development (3.14)

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 "4.1") and enter:

if (!requireNamespace("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")

 

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

Details

biocViews Classification, Metabolomics, Software, StatisticalMethod
Version 0.99.7
In Bioconductor since BioC 3.14 (R-4.1)
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_0.99.7.tar.gz
Windows Binary MAI_0.99.7.zip
macOS 10.13 (High Sierra) MAI_0.99.7.tgz
Source Repository git clone https://git.bioconductor.org/packages/MAI
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/MAI
Package Short Url https://bioconductor.org/packages/MAI/
Package Downloads Report Download Stats

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