1 research outputs found
Statistical Total Correlation Spectroscopy Scaling for Enhancement of Metabolic Information Recovery in Biological NMR Spectra
The high level of complexity in nuclear magnetic resonance
(NMR)
metabolic spectroscopic data sets has fueled the development of experimental
and mathematical techniques that enhance latent biomarker recovery
and improve model interpretability. We previously showed that statistical
total correlation spectroscopy (STOCSY) can be used to <i>edit</i> NMR spectra to remove drug metabolite signatures that obscure metabolic
variation of diagnostic interest. Here, we extend this “STOCSY
editing” concept to a generalized scaling procedure for NMR
data that enhances recovery of latent biochemical information and
improves biological classification and interpretation. We call this
new procedure STOCSY-scaling (STOCSY<sup>S</sup>). STOCSY<sup>S</sup> exploits the fixed proportionality in a set of NMR spectra between
resonances from the same molecule to suppress or enhance features
correlated with a resonance of interest. We demonstrate this new approach
using two exemplar data sets: (a) a streptozotocin rat model (<i>n</i> = 30) of type 1 diabetes and (b) a human epidemiological
study utilizing plasma NMR spectra of patients with metabolic syndrome
(<i>n</i> = 67). In both cases significant biomarker discovery
improvement was observed by using STOCSY<sup>S</sup>: the approach
successfully suppressed interfering NMR signals from glucose and lactate
that otherwise dominate the variation in the streptozotocin study,
which then allowed recovery of biomarkers such as glycine, which were
otherwise obscured. In the metabolic syndrome study, we used STOCSY<sup>S</sup> to enhance variation from the high-density lipoprotein cholesterol
peak, improving the prediction of individuals with metabolic syndrome
from controls in orthogonal projections to latent structures discriminant
analysis models and facilitating the biological interpretation of
the results. Thus, STOCSY<sup>S</sup> is a versatile technique that
is applicable in any situation in which variation, either biological
or otherwise, dominates a data set at the expense of more interesting
or important features. This approach is generally appropriate for
many types of NMR-based complex mixture analyses and hence for wider
applications in bioanalytical science