ToF-SIMS PC-DFA analysis of prostate cancer cell lines

M. J. Baker; E. Gazi; M. D. Brown; N. W. Clarke; J. C. Vickerman; N. P. Lockyer

doi:10.1016/j.apsusc.2008.05.256

ToF-SIMS PC-DFA analysis of prostate cancer cell lines

M. J. Baker, E. Gazi, M. D. Brown, N. W. Clarke, J. C. Vickerman, N. P. Lockyer

Pure And Applied Chemistry

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

Three closely related cancer cell lines have been analysed with ToF-SIMS using a C₆₀⁺ primary ion beam. Principal component-discriminant function analysis (PC-DFA) has been applied for spectral classification. Various spectral pre-processing methods are discussed and assessed for optimum discrimination of this data set. The sum-normalised PC-DFA spectral model produced sensitivities as high as 83.3% and specificities as high as 100% at the 99% confidence limit. At this confidence limit only one errant spectrum was misclassified. The resulting loadings plots suggest that a range of lipid and amino-acid related signals are responsible for the cell line discrimination.

Original language	English
Pages (from-to)	1084-1087
Number of pages	4
Journal	Applied Surface Science
Volume	255
Issue number	4
Early online date	18 May 2008
DOIs	https://doi.org/10.1016/j.apsusc.2008.05.256
Publication status	Published - 15 Dec 2008

Keywords

cancer
cell lines
discrimination
multivariate analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.apsusc.2008.05.256

Cite this

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title = "ToF-SIMS PC-DFA analysis of prostate cancer cell lines",

abstract = "Three closely related cancer cell lines have been analysed with ToF-SIMS using a C60+ primary ion beam. Principal component-discriminant function analysis (PC-DFA) has been applied for spectral classification. Various spectral pre-processing methods are discussed and assessed for optimum discrimination of this data set. The sum-normalised PC-DFA spectral model produced sensitivities as high as 83.3% and specificities as high as 100% at the 99% confidence limit. At this confidence limit only one errant spectrum was misclassified. The resulting loadings plots suggest that a range of lipid and amino-acid related signals are responsible for the cell line discrimination. ",

keywords = "cancer, cell lines, discrimination, multivariate analysis",

author = "Baker, {M. J.} and E. Gazi and Brown, {M. D.} and Clarke, {N. W.} and Vickerman, {J. C.} and Lockyer, {N. P.}",

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T1 - ToF-SIMS PC-DFA analysis of prostate cancer cell lines

AU - Baker, M. J.

AU - Gazi, E.

AU - Brown, M. D.

AU - Clarke, N. W.

AU - Vickerman, J. C.

AU - Lockyer, N. P.

PY - 2008/12/15

Y1 - 2008/12/15

N2 - Three closely related cancer cell lines have been analysed with ToF-SIMS using a C60+ primary ion beam. Principal component-discriminant function analysis (PC-DFA) has been applied for spectral classification. Various spectral pre-processing methods are discussed and assessed for optimum discrimination of this data set. The sum-normalised PC-DFA spectral model produced sensitivities as high as 83.3% and specificities as high as 100% at the 99% confidence limit. At this confidence limit only one errant spectrum was misclassified. The resulting loadings plots suggest that a range of lipid and amino-acid related signals are responsible for the cell line discrimination.

AB - Three closely related cancer cell lines have been analysed with ToF-SIMS using a C60+ primary ion beam. Principal component-discriminant function analysis (PC-DFA) has been applied for spectral classification. Various spectral pre-processing methods are discussed and assessed for optimum discrimination of this data set. The sum-normalised PC-DFA spectral model produced sensitivities as high as 83.3% and specificities as high as 100% at the 99% confidence limit. At this confidence limit only one errant spectrum was misclassified. The resulting loadings plots suggest that a range of lipid and amino-acid related signals are responsible for the cell line discrimination.

KW - cancer

KW - cell lines

KW - discrimination

KW - multivariate analysis

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DO - 10.1016/j.apsusc.2008.05.256

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EP - 1087

JO - Applied Surface Science

JF - Applied Surface Science

IS - 4

ER -

ToF-SIMS PC-DFA analysis of prostate cancer cell lines

Abstract

Keywords

UN SDGs

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