BeanLabASU/metabimpute: Missingness Evaluation, Simulation and Imputation in Metabolomics

A package to evaluate missing data, simulate data matrices and missingness, evaluate multiple imputation methods and return statistics on these and finally methods to impute utilizing multiple standard imputation approaches. Novel imputation methodologies which utilize an imputation approach with data that uses biological or technical replication are also included (as described in ***paper***). ICC evaluation methods are included specifically included to suit researchers working with data with biological or technical replicates. Source code was written by the authors with code copied and modified from the following github packages: https://github.com/Tirgit/missCompare, https://github.com/WandeRum/GSimp (Wei, R., Wang, J., Jia, E., Chen, T., Ni, Y., & Jia, W. (2017). GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies. PLOS Computational Biology) https://github.com/juuussi/impute-metabo Kokla, M., Virtanen, J., Kolehmainen, M. et al. Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study. BMC Bioinformatics 20, 492 (2019). https://doi.org/10.1186/s12859-019-3110-0

install.packages("remotes")
remotes::install_github("BeanLabASU/metabimpute")