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 (3.24)
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")):
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M (2015). "Orchestrating high-throughput genomic analysis with Bioconductor." Nature Methods, 12(2), 115–121. doi:10.1038/nmeth.3252.
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J (2004). "Bioconductor: open software development for computational biology and bioinformatics." Genome Biology, 5(10), R80. doi:10.1186/gb-2004-5-10-r80.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## The following initializes the development version of Bioconductor
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 | 1.19.0 |
| In Bioconductor since | BioC 3.14 (R-4.1) (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_1.19.0.tar.gz |
| Windows Binary (x86_64) | MAI_1.19.0.zip |
| macOS Binary (big-sur-x86_64) | MAI_1.19.0.tgz |
| macOS Binary (sonoma-arm64) | MAI_1.19.0.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 |