Abstract :
An automated data analysis approach has been developed for processing of 1H NMR from tumour extracts. At present, the only manual interventions involve spectrum phasing (although automation methods are available) and choice of the number of principal components (usually chosen to account for about 99% of data variance). Unsupervised learning was important for identifying errors in the automatic processing scheme and for finding outliers (e.g. due to technical failures during extraction or NMR spectroscopy). it can also reveal underlying structure in the dataset (i.e. which classes of samples may be most easily separated). Factor analysis was useful for reducing data dimensionality (important for subsequent analysis) and, after vector rotation, for identifying important biochemical metabolites. Supervised learning (backpropagation NN) provided a robust classification method and was good for distinguishing between meningiomas and other types of brain tumour. Genetic programming analysis of a subset of these data gave comparable classification to NN but with quite simple `programs´, facilitating biochemical interpretation. In general, classification of astrocytic tumours according to grade was not reliable based on 1 H NMR spectra from chemical extracts. Although in vivo 1H NMR spectra of higher grade brain tumours might be characterised by elevated lipid signals, this information will be lost during extraction of water-soluble metabolites (as here). It would be expected that the best classification based on in vivo 1H NMR spectra would involve short echo-time measurements since these are most sensitive to glutamine signals (important for distinguishing tumour type) as well as lipid signals (possibly dependant on tumour grade)
