Data imputation software tools | Mass spectrometry-based untargeted metabolomics
Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR).
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.
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.
Allows analysis of direct infusion and liquid chromatography mass spectrometry-based metabolomics data. Galaxy-M consists of a metabolomics tool for Galaxy, developed for both direct infusion mass spectrometry (DIMS) and liquid chromatography mass spectrometry (LC-MS) metabolomics. This tool aims to enable biologists without programming skills to construct and execute next generation sequencing (NGS) data analyses.
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.
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