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) |
|---|---|
| Pages (from-to) | 1139-1155 |
| Number of pages | 17 |
| Journal | Bioanalysis |
| Volume | 11 |
| Issue number | 12 |
| DOIs | |
| State | Published - Jan 1 2019 |
| Externally published | Yes |
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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
Cite this
Ontology-based metabolomics data integration with quality control. / Buendia, Patricia; Bradley, Ray M.; Taylor, Thomas J.; Schymanski, Emma L.; Patti, Gary J.; Kabuka, Mansur R.
In: Bioanalysis, Vol. 11, No. 12, 01.01.2019, p. 1139-1155.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Ontology-based metabolomics data integration with quality control
AU - Buendia, Patricia
AU - Bradley, Ray M.
AU - Taylor, Thomas J.
AU - Schymanski, Emma L.
AU - Patti, Gary J.
AU - Kabuka, Mansur R.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - data integration
KW - diabetes use case
KW - meta-analysis
KW - metabolomics
KW - ontology-based expert system
KW - quality control
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UR - http://www.scopus.com/inward/citedby.url?scp=85069896082&partnerID=8YFLogxK
U2 - 10.4155/bio-2018-0303
DO - 10.4155/bio-2018-0303
M3 - Article
C2 - 31179719
AN - SCOPUS:85069896082
VL - 11
SP - 1139
EP - 1155
JO - Bioanalysis
JF - Bioanalysis
SN - 1757-6180
IS - 12
ER -