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Title: Chemometric and Statistical Analyses of ToF-SIMS Spectra of Increasingly Complex Biological Samples

Abstract

Characterizing and classifying molecular variation within biological samples is critical for determining fundamental mechanisms of biological processes that will lead to new insights including improved disease understanding. Towards these ends, time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to examine increasingly complex samples of biological relevance, including monosaccharide isomers, pure proteins, complex protein mixtures, and mouse embryo tissues. The complex mass spectral data sets produced were analyzed using five common statistical and chemometric multivariate analysis techniques: principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), soft independent modeling of class analogy (SIMCA), and decision tree analysis by recursive partitioning. PCA was found to be a valuable first step in multivariate analysis, providing insight both into the relative groupings of samples and into the molecular basis for those groupings. For the monosaccharides, pure proteins and protein mixture samples, all of LDA, PLSDA, and SIMCA were found to produce excellent classification given a sufficient number of compound variables calculated. For the mouse embryo tissues, however, SIMCA did not produce as accurate a classification. The decision tree analysis was found to be the least successful for all the data sets, providing neither as accurate a classification nor chemicalmore » insight for any of the tested samples. Based on these results we conclude that as the complexity of the sample increases, so must the sophistication of the multivariate technique used to classify the samples. PCA is a preferred first step for understanding ToF-SIMS data that can be followed by either LDA or PLSDA for effective classification analysis. This study demonstrates the strength of ToF-SIMS combined with multivariate statistical and chemometric techniques to classify increasingly complex biological samples. Applying these techniques to information-rich mass spectral data sets opens the possibilities for new applications including classification of subtly different biological samples that may provide insights into cellular processes, disease progress, and disease diagnosis.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
990410
Report Number(s):
UCRL-JRNL-235865
Journal ID: ISSN 0142-2421; SIANDQ; TRN: US201020%%362
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Journal Article
Journal Name:
Surface and Interface Analysis, vol. 41, no. 2, December 5, 2008, pp. 97-104
Additional Journal Information:
Journal Volume: 41; Journal Issue: 2; Journal ID: ISSN 0142-2421
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; CLASSIFICATION; DECISION TREE ANALYSIS; DIAGNOSIS; DISEASES; EMBRYOS; ISOMERS; MASS SPECTROSCOPY; MIXTURES; MONOSACCHARIDES; MULTIVARIATE ANALYSIS; PROTEINS; SIMULATION; SPECTRA

Citation Formats

Berman, E S, Wu, L, Fortson, S L, Nelson, D O, Kulp, K S, and Wu, K J. Chemometric and Statistical Analyses of ToF-SIMS Spectra of Increasingly Complex Biological Samples. United States: N. p., 2007. Web.
Berman, E S, Wu, L, Fortson, S L, Nelson, D O, Kulp, K S, & Wu, K J. Chemometric and Statistical Analyses of ToF-SIMS Spectra of Increasingly Complex Biological Samples. United States.
Berman, E S, Wu, L, Fortson, S L, Nelson, D O, Kulp, K S, and Wu, K J. Wed . "Chemometric and Statistical Analyses of ToF-SIMS Spectra of Increasingly Complex Biological Samples". United States. https://www.osti.gov/servlets/purl/990410.
@article{osti_990410,
title = {Chemometric and Statistical Analyses of ToF-SIMS Spectra of Increasingly Complex Biological Samples},
author = {Berman, E S and Wu, L and Fortson, S L and Nelson, D O and Kulp, K S and Wu, K J},
abstractNote = {Characterizing and classifying molecular variation within biological samples is critical for determining fundamental mechanisms of biological processes that will lead to new insights including improved disease understanding. Towards these ends, time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to examine increasingly complex samples of biological relevance, including monosaccharide isomers, pure proteins, complex protein mixtures, and mouse embryo tissues. The complex mass spectral data sets produced were analyzed using five common statistical and chemometric multivariate analysis techniques: principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), soft independent modeling of class analogy (SIMCA), and decision tree analysis by recursive partitioning. PCA was found to be a valuable first step in multivariate analysis, providing insight both into the relative groupings of samples and into the molecular basis for those groupings. For the monosaccharides, pure proteins and protein mixture samples, all of LDA, PLSDA, and SIMCA were found to produce excellent classification given a sufficient number of compound variables calculated. For the mouse embryo tissues, however, SIMCA did not produce as accurate a classification. The decision tree analysis was found to be the least successful for all the data sets, providing neither as accurate a classification nor chemical insight for any of the tested samples. Based on these results we conclude that as the complexity of the sample increases, so must the sophistication of the multivariate technique used to classify the samples. PCA is a preferred first step for understanding ToF-SIMS data that can be followed by either LDA or PLSDA for effective classification analysis. This study demonstrates the strength of ToF-SIMS combined with multivariate statistical and chemometric techniques to classify increasingly complex biological samples. Applying these techniques to information-rich mass spectral data sets opens the possibilities for new applications including classification of subtly different biological samples that may provide insights into cellular processes, disease progress, and disease diagnosis.},
doi = {},
journal = {Surface and Interface Analysis, vol. 41, no. 2, December 5, 2008, pp. 97-104},
issn = {0142-2421},
number = 2,
volume = 41,
place = {United States},
year = {2007},
month = {10}
}