Robust multivariate analysis methods for single cell Raman spectroscopy




Kuklev, Nikita

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Usefulness of a particular clinical assay is directly correlated with its ability to extract highest possible signal from available data. This is particularly relevant for personalized radiation therapy since early plan modifications confer greater benefits to treatment outcome. Recent studies have demonstrated capability of single-cell Raman microscopy to detect cellular radiation response at clinical (below 10Gy) doses, but only in certain strongly responding cell lines and after at least two day incubation. One possible cause is rather unoptimized signal processing used. This work investigates application of several advanced multivariate methods - weighted principal component analysis (WPCA), robust PCA, probabilistic PCA, and nonlinear PCA to increase radiation response signal. Representative datasets from strongly (H460 - human lung) and weakly (LNCaP - human prostate) responding cell lines were analysed in 0-50Gy and 0-10Gy dose ranges and results quantified to determine relative and absolute algorithm performance. It was found that with careful tuning, significant improvements in sensitivity and better signal separation could be achieved as compared to conventional PCA.



Medical physics, Multivariate analysis, Radiation biology, Raman spectroscopy, Radiobiology, Cancer therapy, Raman microscopy, Personalized radiation therapy