Chemometrics models for overcoming high between subject variability: applications in clinical metabolic profiling studies
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In human metabolic profiling studies, between-subject variability is often the dominant feature and can mask the potential classifications of clinical interest. Conventional models such as principal component analysis (PCA) are usually not effective in such situations and it is therefore highly desirable to find a suitable model which is able to discover the underlying pattern hidden behind the high between-subject variability. In this study we employed two clinical metabolomics data sets as the testing grounds, in which such variability had been observed, and we demonstrate that a proper choice of chemometrics model can help to overcome this issue of high between-subject variability. Two data sets were used to represent two different types of experiment designs. The first data set was obtained from a small-scale study investigating volatile organic compounds (VOCs) collected from chronic wounds using a skin patch device and analysed by thermal desorption-gas chromatography-mass spectrometry. Five patients were recruited and for each patient three sites sampled in triplicate: healthy skin, boundary of the lesion and top of the lesion, the aim was to discriminate these three types of samples based on their VOC profile. The second data set was from a much larger study involving 35 healthy subjects, 47 patients with chronic obstructive pulmonary disease and 33 with asthma. The VOCs in the breath of each subject were collected using a mask device and analysed again by GC–MS with the aim of discriminating the three types of subjects based on breath VOC profiles. Multilevel simultaneous component analysis, multilevel partial least squares for discriminant analysis, ANOVA-PCA, and a novel simplified ANOVA-PCA model—which we have named ANOVA-Mean Centre (ANOVA-MC)—were applied on these two data sets. Significantly improved results were obtained by using these models. We also present a novel validation procedure to verify statistically the results obtained from those models.
KeywordsMetabolic profiling Between-subject variability Multilevel simultaneous component analysis Multilevel partial least squares for discriminant analysis ANOVA PCA ANOVA simultaneous component analysis ANOVA mean-centre Breath analysis
We thank Dr. Maria Basanta and Dr. Baharudin Ibrahim for providing the breath VOCs data; Dr. Alexi Thomas, Dr. Svetlana Riazanskaia and Dr. William Cheung for providing the skin VOCs data.
- Fens, N., Zwinderman, A. H., van der Schee, M. P. C., de Nijs, S. B., Dijkers, E., Roldaan, A. C., et al. (2009). Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma. American Journal of Respiratory and Critical Care Medicine, 180, 1076–1082.CrossRefPubMedGoogle Scholar
- Ferreira, D. L. S., Kittiwachana, S., Fido, L. A., Thompson, D. R., Escott, R. E. A., & Brereton, R. G. (2009). Multilevel simultaneous component analysis for fault detection in multicampaign process monitoring: Application to on-line high performance liquid chromatography of a continuous process. Analyst, 137, 1571–1585.CrossRefGoogle Scholar
- Hartigan, J. A., & Wong, M. A. (1979). A K-means Clustering Algorithm. Journal of the Royal Statistical Society Series C (Applied Statistics), 28, 100–108.Google Scholar