Raman spectroscopy of brain and skin tissue in minipig models of Huntington’s disease

The dataset originates from the application of Raman spectroscopy to brain and skin tissues from a minipig model of Huntington's diseases. Significant differences were observed between measured spectra of tissues with and without Huntington's disease, for both brain tissue and skin tissue. There are linked to changes in the chemical composition between tissue types. Using machine learning we achieved 100% separation between data sets, indicating that the test would have an accuracy of close to 100% when used as a diagnostic tool for the disease. This suggests the technique has great potential in the rapid and accurate diagnosis of Huntington's and other neurodegenerative diseases in a clinical setting. The dataset is related to the upcoming publication Kevin Tipatet, Isla Du Boulay, Hamish Muir, Liam Davison-Gates, Zdenka Ellederov and Andrew Downes (in submission), "Raman spectroscopy of brain and skin tissue in minipig models of Huntington's disease".