Unsupervised self-organising map of prostate cell Raman spectra shows disease-state subclustering
<|reference_start|>Unsupervised self-organising map of prostate cell Raman spectra shows disease-state subclustering: Prostate cancer is a disease which poses an interesting clinical question: should it be treated? A small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients, hence, new methods of approach to biomolecularly subclassify the disease are needed. Here we use an unsupervised, self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to test the feasibility of this method to differentiate, at the single-cell-level, cancer from normal using high-dimensional datasets with minimal preprocessing. The results demonstrate not only successful separation of normal prostate and cancer cells, but also a new subclustering of the prostate cancer cell-line into two groups. Initial analysis of the spectra from each of the cancer subclusters demonstrates a differential expression of lipids, which, against the normal control, may be linked to disease-related changes in cellular signalling.<|reference_end|>
@article{west2024unsupervised,
    title={Unsupervised self-organising map of prostate cell Raman spectra shows
  disease-state subclustering},
    author={Daniel West, Susan Stepney, Y. Hancock},
    journal={arXiv preprint arXiv:2403.07960},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07960},
    primaryClass={q-bio.QM cs.LG}
}