Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0 Article Swipe
Kwanjeera Wanichthanarak
,
Ammarin In-on
,
Sili Fan
,
Oliver Fiehn
,
Arporn Wangwiwatsin
,
Sakda Khoomrung
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/gigascience/giae005
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/gigascience/giae005
In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.
Related Topics
Concepts
Metabolomics
Normalization (sociology)
Database normalization
Computer science
Data processing
Context (archaeology)
Data mining
Data quality
Bioinformatics
Artificial intelligence
Pattern recognition (psychology)
Biology
Operating system
Sociology
Economics
Metric (unit)
Operations management
Anthropology
Paleontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/gigascience/giae005
- https://academic.oup.com/gigascience/article-pdf/doi/10.1093/gigascience/giae005/56985710/giae005.pdf
- OA Status
- gold
- Cited By
- 9
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392848448
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392848448Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/gigascience/giae005Digital Object Identifier
- Title
-
Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Kwanjeera Wanichthanarak, Ammarin In-on, Sili Fan, Oliver Fiehn, Arporn Wangwiwatsin, Sakda KhoomrungList of authors in order
- Landing page
-
https://doi.org/10.1093/gigascience/giae005Publisher landing page
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https://academic.oup.com/gigascience/article-pdf/doi/10.1093/gigascience/giae005/56985710/giae005.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://academic.oup.com/gigascience/article-pdf/doi/10.1093/gigascience/giae005/56985710/giae005.pdfDirect OA link when available
- Concepts
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Metabolomics, Normalization (sociology), Database normalization, Computer science, Data processing, Context (archaeology), Data mining, Data quality, Bioinformatics, Artificial intelligence, Pattern recognition (psychology), Biology, Operating system, Sociology, Economics, Metric (unit), Operations management, Anthropology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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56Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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