Data imputation software tools | Mass spectrometry-based untargeted metabolomics

An R package for post-processing of metabolomic data. The primary functions of the MSPrep package are summarization of replicates, filtering, imputation of missing data, normalization and/or batch effect adjustment and dataset diagnostics.

Implements a missing value imputation algorithm for liquid chromatography-mass spectrometry (LC MS) metabolomics. MINMA is an R package whose algorithm combines the afore-mentioned information and traditional approaches by applying the support vector regression (SVR) algorithm to a predictor network newly constructed among the features. The software provides a function to match feature m/z values to about 30 positive adduct ions, or over 10 negative adduct ions.

An R package of a set of tools and functions to perform an automatic end-to-end analysis of LC/MS metabolomic data, putting special emphasis on peak annotation and metabolite identification. The goal of the MAIT package is to provide an array of tools that makes programmable metabolomic end-to-end statistical analysis possible. MAIT includes functions to improve peak annotation through the process called biotransformations and to assess the predictive power of statistically significant metabolites that quantify class separability.

Assists users for metabolomics data analysis. Specmine includes a workflow that can be adapted for specific case studies, addressing tasks as data loading, pre-processing, normalization, metabolite identification, univariate and multivariate statistical analysis, clustering, machine learning and feature selection. It also offers modules for the visualization of data including box plots, volcano plots and spectra.

Allows users to analyze unknown signals. PAIRUP-MS is a program useful for processing, matching, and annotating metabolite signals in MS-based profiling datasets. This tool performs meta and pathway analyses for unknown signals. It can be applied to diverse untargeted profiling datasets and is helpful to advance metabolomics as a powerful approach for elucidating biology underlying human traits and diseases.