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Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets
Harmen H M Draisma1, Theo H Reijmers, Frans van der Kloet
1Leiden/Amsterdam Center for Drug Research (LACDR), Leiden University, P.O. Box 9502, NL-2300 RA Leiden, The Netherlands.
Analytical Chemistry
|January 8, 2010
Summary
Combining omics data improves statistical power but requires correcting for nonbiological differences. Quantile equating is a new method that successfully adjusts semiquantitative data blocks for these systematic variations.
Area of Science:
- Biochemistry
- Bioinformatics
- Analytical Chemistry
Background:
- Combining omics data from different sources increases statistical power for analysis.
- Nonbiological systematic differences between data sets, or blocks, prevent direct data combination.
- These differences can arise from analytical variations accumulating over time.
Purpose of the Study:
- To present a data transformation method called quantile equating.
- To correct for linear and nonlinear distributional differences among semiquantitative data blocks.
- To enable the combination of omics data sets for enhanced statistical analysis.
Main Methods:
- Quantile equating method applied per variable.
- Correction for linear and nonlinear differences in data distributions.
- Evaluation of data blocks using uni- and multivariate methods before and after equating.
Main Results:
- Successful application of quantile equating demonstrated on metabolomics data.
- Data obtained from liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy were used.
- The method effectively corrects for nonbiological systematic differences among data blocks.
Conclusions:
- Quantile equating is a validated method for correcting systematic differences in semiquantitative omics data.
- The method facilitates the combination of data from different blocks, enhancing statistical power.
- This approach is applicable to various omics platforms, including metabolomics.
