KechrisLab/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.

Getting started

Package details

Bioconductor views Classification Metabolomics Software StatisticalMethod
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
KechrisLab/MAI documentation built on Sept. 14, 2022, 4:09 p.m.