Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks

Advanced data analysis tools are crucial for the application of ToF-SIMS analysis to biological samples. Here, we demonstrate that by using a training set approach principal components analysis (PCA) can be performed on large 3D ToF-SIMS images of neuronal cell cultures. The method readily provides...

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Main Authors: Van Nuffel, Sebastiaan, Parmenter, Christopher D.J., Scurr, David J., Russell, Noah A., Zelzer, Mischa
Format: Article
Published: Royal Society of Chemistry 2015
Online Access:https://eprints.nottingham.ac.uk/31033/
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author Van Nuffel, Sebastiaan
Parmenter, Christopher D.J.
Scurr, David J.
Russell, Noah A.
Zelzer, Mischa
author_facet Van Nuffel, Sebastiaan
Parmenter, Christopher D.J.
Scurr, David J.
Russell, Noah A.
Zelzer, Mischa
author_sort Van Nuffel, Sebastiaan
building Nottingham Research Data Repository
collection Online Access
description Advanced data analysis tools are crucial for the application of ToF-SIMS analysis to biological samples. Here, we demonstrate that by using a training set approach principal components analysis (PCA) can be performed on large 3D ToF-SIMS images of neuronal cell cultures. The method readily provides access to sample component information and significantly improves the images’ signal-to-noise ratio (SNR).
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institution University of Nottingham Malaysia Campus
institution_category Local University
last_indexed 2025-11-14T19:11:02Z
publishDate 2015
publisher Royal Society of Chemistry
recordtype eprints
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spelling nottingham-310332020-05-04T17:22:08Z https://eprints.nottingham.ac.uk/31033/ Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks Van Nuffel, Sebastiaan Parmenter, Christopher D.J. Scurr, David J. Russell, Noah A. Zelzer, Mischa Advanced data analysis tools are crucial for the application of ToF-SIMS analysis to biological samples. Here, we demonstrate that by using a training set approach principal components analysis (PCA) can be performed on large 3D ToF-SIMS images of neuronal cell cultures. The method readily provides access to sample component information and significantly improves the images’ signal-to-noise ratio (SNR). Royal Society of Chemistry 2015-11-20 Article PeerReviewed Van Nuffel, Sebastiaan, Parmenter, Christopher D.J., Scurr, David J., Russell, Noah A. and Zelzer, Mischa (2015) Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks. Analyst, 141 (1). pp. 90-95. ISSN 1364-5528 http://pubs.rsc.org/en/Content/ArticleLanding/2016/AN/C5AN01743B doi:10.1039/c5an01743b doi:10.1039/c5an01743b
spellingShingle Van Nuffel, Sebastiaan
Parmenter, Christopher D.J.
Scurr, David J.
Russell, Noah A.
Zelzer, Mischa
Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks
title Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks
title_full Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks
title_fullStr Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks
title_full_unstemmed Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks
title_short Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks
title_sort multivariate analysis of 3d tof-sims images: method validation and application to cultured neuronal networks
url https://eprints.nottingham.ac.uk/31033/
https://eprints.nottingham.ac.uk/31033/
https://eprints.nottingham.ac.uk/31033/