Abstract
Scope:
Combining different “omics” data types in a single, integrated analysis may better characterize the effects of diet on human health.
Methods and results:
The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for “Omics” (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non-obese subjects (n = 13; study 2); and an 8-week parallel intervention study that assessed three diets with variable lipid content on fasting parameters (n = 39; study 3). In study 1, combining datasets using SNF or DIABLO significantly improve sample classification. For studies 2 and 3, the value of SNF integration depends on the dietary groups being compared, while DIABLO discriminates samples well but does not perform better than transcriptomic data alone.
Conclusion:
The integration of associated “omics” datasets can help clarify the subtle signals observed in nutritional interventions. The performance of each integration tool is differently influenced by study design, size of the datasets, and sample size.
Combining different “omics” data types in a single, integrated analysis may better characterize the effects of diet on human health.
Methods and results:
The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for “Omics” (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non-obese subjects (n = 13; study 2); and an 8-week parallel intervention study that assessed three diets with variable lipid content on fasting parameters (n = 39; study 3). In study 1, combining datasets using SNF or DIABLO significantly improve sample classification. For studies 2 and 3, the value of SNF integration depends on the dietary groups being compared, while DIABLO discriminates samples well but does not perform better than transcriptomic data alone.
Conclusion:
The integration of associated “omics” datasets can help clarify the subtle signals observed in nutritional interventions. The performance of each integration tool is differently influenced by study design, size of the datasets, and sample size.
| Original language | English |
|---|---|
| Article number | 2000647 |
| Journal | Molecular Nutrition & Food Research |
| Volume | 65 |
| Issue number | 4 |
| Early online date | 29 Jan 2021 |
| DOIs | |
| Publication status | Published - Feb 2021 |
Keywords
- Biotechnology
- Food Science
Access to Document
- Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies
Copyright 2021 the authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.