Statistical data analysis software tools | Mass spectrometry-based untargeted metabolomics
After pre-processing, the LC-MS raw data are summarized by a peak list. The statistical analysis aims to detect those peaks whose intensity levels are significantly altered between distinct biological groups. The specific choice of statistical methods often depends on the particular study design, while some methods can be applied to multiple types of studies.
Provides a web-based analytical pipeline for high-throughput metabolomics studies. MetaboAnalyst aims to offer a variety of commonly used procedures for metabolomic data processing, normalization, multivariate statistical analysis, as well as data annotation. The current implementation focuses on exploratory statistical analysis, functional interpretation, and advanced statistics for translational metabolomics studies. This tool is also available as desktop version.
A total solution to deal with not only data dependent MS/MS but also data independent MS/MS experiments for metabolomics and lipidomics. Its feature is 1) implementing de-convolution method for data independent MS/MS 2) using unified criteria for peak identification 3) supporting all data processing step from raw data import to statistical analysis 4) user-friendly graphic user interface. MS-DIAL deals with data independent acquisition MS/MS data (ex. SWATH) by means of two step algorithms (peak spotting and MS2Dec) for spectral deconvolution. Also, it supports compound identification, peak alignment, and principal component analysis on the graphical user interface. The spectrum information is outputted by MassBank, NIST, and Mascot formats. And the organized data matrix (sample vs metabolite) is exported as tab delimited text file.
Allows users to analyze and visualize liquid chromatography – mass spectrometry (LC-MS) data. PiMP is a comprehensive and integrated web enabled pipeline that consists of five tasks: (1) project administration, (2) data upload, (3) quality control, (4) analysis parameters and (5) data interpretation. Users can define the experimental design, specify metadata and share the project with collaborators with a chosen level of permission. It aims at automatization and standardization of metabolomics analysis.
Permits comprehensive metabolomics data pre-processing, statistical analysis and interpretation. W4M includes computational modules for data normalization, multivariate analysis and annotation. It can create interactive web-based documents showing the results of the analyses, and users can share them with collaborators directly on the platform. This tool enables multi-omics analyses in a global systems-biology approach.
Aims to integrate and analyze metabolomics experiment data. MeltDB is a program that can be applied for the description and analysis of metabolomic experiments. This program hosts over 30 experiments predominantly from gas chromatography-mass spectrometry (GC/MS) measurements. Moreover, this tool includes an API allowing users to evaluate novel methods and algorithms for the preprocessing of metabolomic datasets.
Allows peak identification, prediction, and data integration of MetAlign results. AIoutput is a non-targeted and targeted analysis software developed for Gas chromatography coupled to mass spectrometry (GC/MS) based metabolomics
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 high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities.
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.
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.
A software tool for the efficient and automatic analysis of GC/MS-based metabolomics data. Starting with raw MS data, MetaboliteDetector detects and subsequently identifies potential metabolites. Moreover, a comparative analysis of a large number of chromatograms can be performed in either a targeted or nontargeted approach. It automatically determines appropriate quantification ions and performs an integration of single ion peaks. The analysis results can directly be visualized with a principal component analysis. Since the manual input is limited to absolutely necessary parameters, the program is also usable for the analysis of high-throughput data. However, the intuitive graphical user interface of MetaboliteDetector additionally allows for a detailed examination of a single GC/MS chromatogram including single ion chromatograms, recorded mass spectra, and identified metabolite spectra in combination with the corresponding reference spectra obtained from a reference library. MetaboliteDetector is able to import GC/MS data in NetCDF and FastFlight format.
Allows analysis of metabolomics data. rMANOVA can be employed for analysis of any high-dimensional data set with an underlying experimental design. It can take the correlation between variables into account and is also applicable when the number of variables vastly exceeds the sample size. This tool offers a realistic view of the data.
Includes widely used statistical methods to process and identify keys entities of input experiments, offers different integrative analysis methodologies and provides interactive visualization to facilitate biological interpretations. Metabox is a bioinformatics toolbox for deep phenotyping analytics that combines data processing, statistical analysis, functional analysis and integrative exploration of metabolomic data within proteomic and transcriptomic contexts. It supports in-depth analysis of metabolomic data by including four analysis modules: data normalization, statistical analysis, network construction and functional analysis.
Can end-to-end metabolomics data analysis through a set of interchangeable modules. metaX automates analysis of untargeted metabolomics data acquired from Liquide chromatography coupled mass spectrometry (LC/MS) or gas chromatography coupled mass spectrometry (GC/MS). It is based upon the fast process and the optimized workflow to improve the interpretation of metabolomics data. The tool can deal with large-scale metabolomics datasets.
Assist users in processing, visualization and re-analysis of publicly-submitted raw and processed Gas Chromatography-Mass Spectrometry (GC-MS) metabolomics datasets. MetabolomeExpress performs three main functions: (i) store complete GC/MS metabolomics datasets in a way that makes them highly accessible, (ii) provide researchers with cost-free online access to a powerful raw data processing pipeline and (iii) store metabolite response statistics in a central database.
Offers an assortment of features for metabolomics data processing. SECIMTools is available both as a standalone software or as a set of methods that can be run through the Galaxy platform. The software provides: (i) quality control metrics; (ii), visualization techniques including principal component analysis; (iii) basic statistical analysis methods; (iv) advanced classification methods such as random forest; and finally (v) variable selection tools.
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.