This is the development version of MAI; for the stable release version, see MAI.
Mechanism-Aware ImputationBioconductor version: Development (3.22)
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, entercitation("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.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("MAI")Follow Installation instructions to use this package in your R session.
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