Dataset Information

MetaboDiff: an R package for differential metabolomic analysis.

ABSTRACT: Summary:Comparative metabolomics comes of age through commercial vendors offering metabolomics for translational researchers outside the mass spectrometry field. The MetaboDiff packages aims to provide a low-level entry to differential metabolomic analysis with R by starting off with the table of metabolite measurements. As a key functionality, MetaboDiffs offers the exploration of sample traits in a data-derived metabolic correlation network. Availability and implementation:The MetaboDiff R package is platform-independent, available at http://github.com/andreasmock/MetaboDiff/ and released under the MIT licence. The package documentation comprises a step-by-step markdown tutorial. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Mock A

PROVIDER: S-EPMC6157071 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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MetaboDiff: an R package for differential metabolomic analysis.

Mock Andreas A Warta Rolf R Dettling Steffen S Brors Benedikt B Jäger Dirk D Herold-Mende Christel C

Bioinformatics (Oxford, England) 20181001 19

<h4>Summary</h4>Comparative metabolomics comes of age through commercial vendors offering metabolomics for translational researchers outside the mass spectrometry field. The MetaboDiff packages aims to provide a low-level entry to differential metabolomic analysis with R by starting off with the table of metabolite measurements. As a key functionality, MetaboDiffs offers the exploration of sample traits in a data-derived metabolic correlation network.<h4>Availability and implementation</h4>The M ...[more]

PMID: 29718102

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Project description:BACKGROUND: Large-scale molecular interaction networks are dynamic in nature and are of special interest in the analysis of complex diseases, which are characterized by network-level perturbations rather than changes in individual genes/proteins. The methods developed for the identification of differentially expressed genes or gene sets are not suitable for network-level analyses. Consequently, bioinformatics approaches that enable a joint analysis of high-throughput transcriptomics datasets and large-scale molecular interaction networks for identifying perturbed networks are gaining popularity. Typically, these approaches require the sequential application of multiple bioinformatics techniques - ID mapping, network analysis, and network visualization. Here, we present the Variability Analysis in Networks (VAN) software package: a collection of R functions to streamline this bioinformatics analysis. FINDINGS: VAN determines whether there are network-level perturbations across biological states of interest. It first identifies hubs (densely connected proteins/microRNAs) in a network and then uses them to extract network modules (comprising of a hub and all its interaction partners). The function identifySignificantHubs identifies dysregulated modules (i.e. modules with changes in expression correlation between a hub and its interaction partners) using a single expression and network dataset. The function summarizeHubData identifies dysregulated modules based on a meta-analysis of multiple expression and/or network datasets. VAN also converts protein identifiers present in a MITAB-formatted interaction network to gene identifiers (UniProt identifier to Entrez identifier or gene symbol using the function generatePpiMap) and generates microRNA-gene interaction networks using TargetScan and Microcosm databases (generateMicroRnaMap). The function obtainCancerInfo is used to identify hubs (corresponding to significantly perturbed modules) that are already causally associated with cancer(s) in the Cancer Gene Census database. Additionally, VAN supports the visualization of changes to network modules in R and Cytoscape (visualizeNetwork and obtainPairSubset, respectively). We demonstrate the utility of VAN using a gene expression data from metastatic melanoma and a protein-protein interaction network from the Human Protein Reference Database. CONCLUSIONS: Our package provides a comprehensive and user-friendly platform for the integrative analysis of -omics data to identify disease-associated network modules. This bioinformatics approach, which is essentially focused on the question of explaining phenotype with a 'network type' and in particular, how regulation is changing among different states of interest, is relevant to many questions including those related to network perturbations across developmental timelines.

Dataset Information

MetaboDiff: an R package for differential metabolomic analysis.

Publications

MetaboDiff: an R package for differential metabolomic analysis.

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