## 8 Homo- and Heteronuclear Two-Dimensional (2D) NMR Analysis of Human Saliva
### Data Normalization, Transformation, Scaling, and Dimensionality Reduction
The bottom line is that most fully established metabolomics researchers prefer to perform a combination of both UV and MV analyses on datasets acquired, largely because selected metabolite variables may be univariately significant but not multivariately so (a common observation that may arise from it acting as a singular, orthogonal biomarker that remains unrelated to other possible ones), or vice-versa, which may occur in situations where, although a metabolite may not prove to serve as a statistically significant biomarker in a UV context, it remains strongly correlated to other biomolecules that together form a strong MV pattern or orthogonal component that is of value for distinguishing between disease and healthy control patient cohorts, for example. Although of much interest, these arguments lie beyond the scope of this paper, and therefore readers are referred to [135; 136] for further information. Furthermore, the use of evolutionary algorithms, for example genetic algorithms/genetic programming, is also a valuable approach. Indeed, such evolutionary algorithms are generally performed in combination with a second MV analysis algorithmic technique (e.g., PLS-DA) that seeks and explores composites of metabolite variables that reveal the highest level of efficacy achieved with the secondary algorithm and that are arbitrated by the principles of evolution and species selection exercises [137]. With regard to the above approaches for the preliminary processing of complex datasets prior to the application of MV statistical analysis techniques, Figure 7 shows the effects of the sequential row-wise normalization, glog-transformation, and column-wise Pareto scaling processes on the means, variances, and kernel densities of a two-classification human WMS supernatant \({}^{1}\)H NMR dataset; also shown are variable box-plots for 50 of the most important ISB variables. The WMS samples for this analysis were donated by n = 48 participants with an acute sore-throat condition, and an equivalent number of those without this disorder as healthy controls. Moreover, five daily samples were sequentially collected from each subject over a period of five consecutive days. Full details and examples of corresponding 600 MHz WMS supernatant \({}^{1}\)H NMR profiles for this investigation are presented as a case study in Part II of this report. From these processing experiments, it can be observed that glog-transformation serves to homogenize variable variances somewhat, although the overall kernel density plot revealed a trimodal distribution of variables remaining. Moreover, subsequent application of the Pareto-scaling approach not only further markedly homogenizes previously heteroscedastic variances but also renders the kernel density plot virtually unimodal and symmetrical. Therefore, we thoroughly recommend that all these preprocessing methods are applied prior to the performance of MV statistical analysis, although it should be noted that alternative normalization (e.g., PQN, median-, or range-normalization, etc. ), transformation (e.g., cubed root, inverse, or arcsine transformations, the latter being suitable for proportion or percentage data, etc.) or scaling (e.g., autoscaling, etc.) methods may also be employed where considered relevant or where fully justified. Figure 7: Cont. Figure 7: _Cont_. The influence of different of row-wise normalization approaches was also explored as part of a preliminary investigation for this study. Table 3 shows the results arising  
Figure 7: Box and kernel density plots shown for the 50 most important \({}^{1}\)H NMR ISB intensity discriminatory variables before and after row-wise normalization, transformation, and column-wise scaling for the acute sore-throat study dataset. (**a**) With CSN only; (**b**) as (**a**), but also with glog-transformation applied; (**c**) as (**b**), but with Pareto-scaling applied.