recipe bioconductor-mai

Mechanism-Aware Imputation

Homepage:

https://bioconductor.org/packages/3.16/bioc/html/MAI.html

License:

GPL-3

Recipe:

/bioconductor-mai/meta.yaml

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

Versions:

1.4.0-01.0.0-0

Depends:
Required By:

Installation

With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-mai

and update with:

conda update bioconductor-mai

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-mai:<tag>

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

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