Abstract
Aim: The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. Results & methodology: This paper presents an integrated system of quality control (QC) methods to assess metabolomics results by evaluating the data acquisition strategies and metabolite identification process when integrating datasets for meta-analysis. An ontology knowledge base and a rule-based system representing the experiment and chemical background information direct the processes involved in data integration and QC verification. A diabetes meta-analysis study using these QC methods finds putative biomarkers that differ between cohorts. Conclusion: The methods presented here ensure the validity of meta-analysis when integrating data from different metabolic profiling studies.
Original language | English (US) |
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Pages (from-to) | 1139-1155 |
Number of pages | 17 |
Journal | Bioanalysis |
Volume | 11 |
Issue number | 12 |
DOIs | |
State | Published - 2019 |
Keywords
- data integration
- diabetes use case
- meta-analysis
- metabolomics
- ontology-based expert system
- quality control
ASJC Scopus subject areas
- Analytical Chemistry
- Pharmacology, Toxicology and Pharmaceutics(all)
- Clinical Biochemistry
- Medical Laboratory Technology