MAI
This is the development version of MAI; for the stable release version, see MAI.
Mechanism-Aware Imputation
DOI: 10.18129/B9.bioc.MAI
Bioconductor version: Development (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("MAI")):
Installation
To install this package, start R (version "4.5") 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 | ||
| NEWS | Text | |
| LICENSE | Text |
Details
| biocViews | Classification, Metabolomics, Software, StatisticalMethod |
| Version | 1.13.0 |
| 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 |
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_1.13.0.tar.gz |
| Windows Binary (x86_64) | MAI_1.13.0.zip |
| macOS Binary (x86_64) | MAI_1.13.0.tgz |
| macOS Binary (arm64) | MAI_1.13.0.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 |