Package: MAI 1.11.0

Jonathan Dekermanjian

MAI: Mechanism-Aware Imputation

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.

Authors:Jonathan Dekermanjian [aut, cre], Elin Shaddox [aut], Debmalya Nandy [aut], Debashis Ghosh [aut], Katerina Kechris [aut]

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# Install 'MAI' in R:

install.packages('MAI', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kechrislab/mai/issues

Datasets:

untargeted_LCMS_data - Example data set containing missing values

On BioConductor:MAI-1.11.0(bioc 3.20)MAI-1.10.0(bioc 3.19)

bioconductor-package

1 exports 1.69 score 164 dependencies 5 mentions

Last updated 2 months agofrom:8bb058f9e0

Exports:MAI

Dependencies:abind askpass backports base64enc Biobase BiocGenerics bit bit64 blob broom bslib cachem callr caret cellranger class cli clipr clock codetools colorspace conflicted cpp11 crayon curl data.table DBI dbplyr DelayedArray diagram digest doParallel doRNG dplyr dtplyr e1071 evaluate fansi farver fastmap fontawesome forcats foreach fs future future.apply gargle generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 globals glue googledrive googlesheets4 gower gtable hardhat haven highr hms htmltools httr ids ipred IRanges isoband iterators itertools jquerylib jsonlite KernSmooth knitr labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix MatrixGenerics matrixStats memoise mgcv mime missForest ModelMetrics modelr munsell nlme nnet numDeriv openssl parallelly pcaMethods pillar pkgconfig plyr prettyunits pROC processx prodlim progress progressr proxy ps purrr R6 ragg randomForest rappdirs RColorBrewer Rcpp readr readxl recipes rematch rematch2 reprex reshape2 rlang rmarkdown rngtools rpart rstudioapi rvest S4Arrays S4Vectors sass scales selectr shape SparseArray SQUAREM stringi stringr SummarizedExperiment survival sys systemfonts textshaping tibble tidyr tidyselect tidyverse timechange timeDate tinytex tzdb UCSC.utils utf8 uuid vctrs viridisLite vroom withr xfun xml2 XVector yaml zlibbioc

Utilizing Mechanism-Aware Imputation (MAI)

Jonathan Dekermanjian, Elin Shaddox, Debmalya Nandy, Debashis Ghosh, Katerina Kechris

Rendered fromUsingMAI.Rmdusingknitr::rmarkdownon Jul 07 2024.

Last update: 2022-08-10
Started: 2021-07-20

Help page	Topics
Mechanism-Aware Imputation	MAI
Example data set containing missing values	untargeted_LCMS_data

Package: MAI 1.11.0

MAI: Mechanism-Aware Imputation

Utilizing Mechanism-Aware Imputation (MAI)

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