New Method for Normalizing Metabolomics Data
Author Information
Author(s): Marko Sysi-Aho, Mikko Katajamaa, Laxman Yetukuri, Matej Orešič
Primary Institution: VTT Technical Research Centre of Finland
Hypothesis
Can a new normalization method using multiple internal standards improve metabolomics data analysis?
Conclusion
The NOMIS method is superior in reducing variability across metabolite measurements compared to traditional normalization methods.
Supporting Evidence
- The NOMIS method significantly reduced the coefficient of variance in metabolite measurements.
- It was demonstrated to be more effective than normalization by l2 norm and retention time region specific standard compounds.
- The method can help select the best combinations of internal standards for different biological matrices.
Takeaway
This study introduces a new way to make metabolomics data more accurate by using several internal standards to help balance the measurements.
Methodology
The NOMIS method was tested on mouse liver lipid profiles using UPLC/MS and compared to other normalization methods.
Potential Biases
Potential bias from the variability of internal standards and their correlation with metabolites.
Limitations
The method's performance may vary with different biological matrices and analytical platforms.
Participant Demographics
Mouse liver samples were used in the study.
Digital Object Identifier (DOI)
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