Abstract
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.
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.
The methods presented here ensure the validity of meta-analysis when integrating data from different metabolic profiling studies.
