recipe bioconductor-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.

package bioconductor-mai

(downloads) docker_bioconductor-mai



depends bioconductor-pcamethods:


depends bioconductor-s4vectors:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-caret:

depends r-doparallel:

depends r-e1071:

depends r-foreach:

depends r-future:

depends r-future.apply:

depends r-missforest:

depends r-tidyverse:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-mai

and update with::

   mamba update bioconductor-mai

To create a new environment, run:

mamba create --name myenvname bioconductor-mai

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-mai/tags`_ for valid values for ``<tag>``)

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