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Statistics, Optics, Biophysics, Pharmacology and pharmacy
Raman spectroscopy is fast becoming a valuable analytical tool in a number of biomedical scenarios, most notably disease diagnostics. Importantly, the technique has also shown increasing promise in the assessment of drug interactions on a cellular and subcellular level, particularly when coupled with multivariate statistical analysis. However, an important consideration, both with Raman spectroscopy and the associated statistical methodologies, is the accuracy of these techniques and more specifically the sensitivities which can be achieved and ultimately the limits of detection of the various methods. The purpose of this study is thus the construction of a model simulated data set with the aim of testing the accuracy and sensitivity of the partial least squares regression (PLSR) approach to spectral analysis. The basis of the dataset is the experimental spectral profiles of a previously reported Raman spectroscopic analysis of the interaction of the cancer chemotherapeutic agent cisplatin in an adenocarcinomic human alveolar basal epithelial cell- line, in vitro, and is thus reflective of actual experimental data. The simulated spectroscopic data is constructed by adding known perturbations which are independently linear in drug dose, as well as cytological response, experimentally determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) cytotoxicity assay. It is demonstrated that, through appropriate choice of dose range, PLSR against the respective targets can differentiate between the spectroscopic signatures of the direct chemical effect of the drug dose and the indirect cytological effect it produces.
Keating ME, Nawaz H, Bonnier F, Byrne HJ. (2015). Multivariate statistical methodologies applied in biomedical Raman spectroscopy: assessing the validity of partial least squares regression using simulated model datasets. Analyst140(7), pp.2482-92. doi: 10.1039/c4an02167c