DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data
Abstract Background Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics...
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2024-12-01
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| Online Access: | https://doi.org/10.1186/s12859-024-05994-1 |
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| author | Christopher Patsalis Gayatri Iyer Marci Brandenburg Alla Karnovsky George Michailidis |
| author_facet | Christopher Patsalis Gayatri Iyer Marci Brandenburg Alla Karnovsky George Michailidis |
| author_sort | Christopher Patsalis |
| collection | DOAJ |
| description | Abstract Background Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data. This approach is limited to endogenous metabolites that can be readily mapped to metabolic pathways. An alternative to pathway analysis that can be used for any classes of metabolites, including unknown compounds that are ubiquitous in untargeted metabolomics data, involves defining metabolite-metabolite interactions using experimental data. Our group has developed several network-based methods that use partial correlations of experimentally determined metabolite measurements. These were implemented in CorrelationCalculator and Filigree, two software tools for the analysis of metabolomics data we developed previously. The latter tool implements the Differential Network Enrichment Analysis (DNEA) algorithm. This analysis is useful for building differential networks from metabolomics data containing two experimental groups and identifying differentially enriched metabolic modules. While Filigree is a user-friendly tool, it has certain limitations when used for the analysis of large-scale metabolomics datasets. Results We developed the DNEA R package for the data-driven network analysis of metabolomics data. We present the DNEA workflow and functionality, algorithm enhancements implemented with respect to the package’s predecessor, Filigree, and discuss best practices for analyses. We tested the performance of the DNEA R package and illustrated its features using publicly available metabolomics data from the environmental determinants of diabetes in the young. To our knowledge, this package is the only publicly available tool designed for the construction of biological networks and subsequent enrichment testing for datasets containing exogenous, secondary, and unknown compounds. This greatly expands the scope of traditional enrichment analysis tools that can be used to analyze a relatively small set of well-annotated metabolites. Conclusions The DNEA R package is a more flexible and powerful implementation of our previously published software tool, Filigree. The modular structure of the package, along with the parallel processing framework built into the most computationally extensive steps of the algorithm, make it a powerful tool for the analysis of large and complex metabolomics datasets. |
| format | Article |
| id | doaj-art-c46d5f12270a4f8cbce0356028992676 |
| institution | DOAJ |
| issn | 1471-2105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-c46d5f12270a4f8cbce03560289926762025-08-20T02:40:17ZengBMCBMC Bioinformatics1471-21052024-12-0125111910.1186/s12859-024-05994-1DNEA: an R package for fast and versatile data-driven network analysis of metabolomics dataChristopher Patsalis0Gayatri Iyer1Marci Brandenburg2Alla Karnovsky3George Michailidis4Department of Computational Medicine and Bioinformatics, University of MichiganDepartment of Computational Medicine and Bioinformatics, University of MichiganDepartment of Computational Medicine and Bioinformatics, University of MichiganDepartment of Computational Medicine and Bioinformatics, University of MichiganDepartment of Statistics, University of FloridaAbstract Background Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data. This approach is limited to endogenous metabolites that can be readily mapped to metabolic pathways. An alternative to pathway analysis that can be used for any classes of metabolites, including unknown compounds that are ubiquitous in untargeted metabolomics data, involves defining metabolite-metabolite interactions using experimental data. Our group has developed several network-based methods that use partial correlations of experimentally determined metabolite measurements. These were implemented in CorrelationCalculator and Filigree, two software tools for the analysis of metabolomics data we developed previously. The latter tool implements the Differential Network Enrichment Analysis (DNEA) algorithm. This analysis is useful for building differential networks from metabolomics data containing two experimental groups and identifying differentially enriched metabolic modules. While Filigree is a user-friendly tool, it has certain limitations when used for the analysis of large-scale metabolomics datasets. Results We developed the DNEA R package for the data-driven network analysis of metabolomics data. We present the DNEA workflow and functionality, algorithm enhancements implemented with respect to the package’s predecessor, Filigree, and discuss best practices for analyses. We tested the performance of the DNEA R package and illustrated its features using publicly available metabolomics data from the environmental determinants of diabetes in the young. To our knowledge, this package is the only publicly available tool designed for the construction of biological networks and subsequent enrichment testing for datasets containing exogenous, secondary, and unknown compounds. This greatly expands the scope of traditional enrichment analysis tools that can be used to analyze a relatively small set of well-annotated metabolites. Conclusions The DNEA R package is a more flexible and powerful implementation of our previously published software tool, Filigree. The modular structure of the package, along with the parallel processing framework built into the most computationally extensive steps of the algorithm, make it a powerful tool for the analysis of large and complex metabolomics datasets.https://doi.org/10.1186/s12859-024-05994-1Network analysisPathway analysisPartial correlationMetabolomicsEnrichment analysisNetwork visualization |
| spellingShingle | Christopher Patsalis Gayatri Iyer Marci Brandenburg Alla Karnovsky George Michailidis DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data BMC Bioinformatics Network analysis Pathway analysis Partial correlation Metabolomics Enrichment analysis Network visualization |
| title | DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data |
| title_full | DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data |
| title_fullStr | DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data |
| title_full_unstemmed | DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data |
| title_short | DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data |
| title_sort | dnea an r package for fast and versatile data driven network analysis of metabolomics data |
| topic | Network analysis Pathway analysis Partial correlation Metabolomics Enrichment analysis Network visualization |
| url | https://doi.org/10.1186/s12859-024-05994-1 |
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