Imputomics is a software package and a web server designed to simulate and impute missing values in omics datasets. It is a comprehensive package that offers a range of methods for simulating and imputing missing values in different types of omics data such as genomics, transcriptomics, proteomics, and metabolomics. Imputomics provides a user-friendly interface that allows users to simulate missing values based on different distributions and impute missing values using state-of-the-art methods.
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Simulation of missing values: Imputomics provides a variety of options for simulating missing values, including missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) mechanisms. Users can specify the percentage of missing values and the distribution from which the missing values are generated.
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Imputation methods: Imputomics offers the biggest collection of imputation methods for different types of omics data, including k-nearest neighbors (KNN), random forests, expectation-maximization (EM) algorithm, and principal components analysis (PCA) and many others.
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Performance evaluation: Imputomics facilitates evaluating the performance of imputation methods. Users can evaluate imputation accuracy and compare different methods using metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared).
This repository contains the data and code necessary to reproduce the results from the paper Imputomics: comprehensive missing data imputation for metabolomics data. It uses renv package to assure the reproducibility. As imputomics implements lots of missing value imputations methods from other R packages.
The imputomics can be accessed through our web server.
imputomics is available on GitHub
devtools::install_github("michbur/imputomics")
renv::restore()To run imputomics type the following command into an R console.
imputomics::imputomics_gui()Jarosław Chilimoniuk, Krystyna Grzesiak, Jakub Kała, Dominik Nowakowski, Małgorzata Bogdan, Michał Ciborowski, Adam Krętowski, Michał Burdukiewicz (2023). Imputomics: comprehensive missing data imputation for metabolomics data (submitted).
If you have any questions, suggestions or comments, contact Michal Burdukiewicz.
We want to thank the Clinical Research Centre (Medical University of Białystok) members for fruitful discussions. K.G. wants to acknowledge grant no. 2021/43/O/ST6/02805 (National Science Centre). M.C. acknowledges grant no. B.SUB.23.533 (Medical University of Białystok). The study was supported by the Ministry of Education and Science funds within the project ‘Excellence Initiative - Research University’. We also acknowledge the Center for Artificial Intelligence at the Medical University of Białystok (funded by the Ministry of Health of the Republic of Poland).
