Robust multivariate analysis methods for single cell Raman spectroscopy
Date
2016-09-02
Authors
Kuklev, Nikita
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Abstract
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.
Description
Keywords
Medical physics, Multivariate analysis, Radiation biology, Raman spectroscopy, Radiobiology, Cancer therapy, Raman microscopy, Personalized radiation therapy