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The Rametrix(™) PRO Toolbox v1.0 for MATLAB(®)

BACKGROUND: Existing tools for chemometric analysis of vibrational spectroscopy data have enabled characterization of materials and biologicals by their broad molecular composition. The Rametrix(™) LITE Toolbox v1.0 for MATLAB(®) is one such tool available publicly. It applies discriminant analysis...

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Detalles Bibliográficos
Autores principales: Senger, Ryan S., Robertson, John L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951287/
https://www.ncbi.nlm.nih.gov/pubmed/31934499
http://dx.doi.org/10.7717/peerj.8179
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author Senger, Ryan S.
Robertson, John L.
author_facet Senger, Ryan S.
Robertson, John L.
author_sort Senger, Ryan S.
collection PubMed
description BACKGROUND: Existing tools for chemometric analysis of vibrational spectroscopy data have enabled characterization of materials and biologicals by their broad molecular composition. The Rametrix(™) LITE Toolbox v1.0 for MATLAB(®) is one such tool available publicly. It applies discriminant analysis of principal components (DAPC) to spectral data to classify spectra into user-defined groups. However, additional functionality is needed to better evaluate the predictive capabilities of these models when “unknown” samples are introduced. Here, the Rametrix(™) PRO Toolbox v1.0 is introduced to provide this capability. METHODS: The Rametrix(™) PRO Toolbox v1.0 was constructed for MATLAB(®) and works with the Rametrix(™) LITE Toolbox v1.0. It performs leave-one-out analysis of chemometric DAPC models and reports predictive capabilities in terms of accuracy, sensitivity (true-positives), and specificity (true-negatives). Rametrix(™)PRO is available publicly through GitHub under license agreement at: https://github.com/SengerLab/RametrixPROToolbox. Rametrix(™) PRO was used to validate Rametrix(™) LITE models used to detect chronic kidney disease (CKD) in spectra of urine obtained by Raman spectroscopy. The dataset included Raman spectra of urine from 20 healthy individuals and 31 patients undergoing peritoneal dialysis treatment for CKD. RESULTS: The number of spectral principal components (PCs) used in building the DAPC model impacted the model accuracy, sensitivity, and specificity in leave-one-out analyses. For the dataset in this study, using 35 PCs in the DAPC model resulted in 100% accuracy, sensitivity, and specificity in classifying an unknown Raman spectrum of urine as belonging to a CKD patient or a healthy volunteer. Models built with fewer or greater number of PCs showed inferior performance, which demonstrated the value of Rametrix(™) PRO in evaluating chemometric models constructed with Rametrix(™) LITE.
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spelling pubmed-69512872020-01-13 The Rametrix(™) PRO Toolbox v1.0 for MATLAB(®) Senger, Ryan S. Robertson, John L. PeerJ Bioengineering BACKGROUND: Existing tools for chemometric analysis of vibrational spectroscopy data have enabled characterization of materials and biologicals by their broad molecular composition. The Rametrix(™) LITE Toolbox v1.0 for MATLAB(®) is one such tool available publicly. It applies discriminant analysis of principal components (DAPC) to spectral data to classify spectra into user-defined groups. However, additional functionality is needed to better evaluate the predictive capabilities of these models when “unknown” samples are introduced. Here, the Rametrix(™) PRO Toolbox v1.0 is introduced to provide this capability. METHODS: The Rametrix(™) PRO Toolbox v1.0 was constructed for MATLAB(®) and works with the Rametrix(™) LITE Toolbox v1.0. It performs leave-one-out analysis of chemometric DAPC models and reports predictive capabilities in terms of accuracy, sensitivity (true-positives), and specificity (true-negatives). Rametrix(™)PRO is available publicly through GitHub under license agreement at: https://github.com/SengerLab/RametrixPROToolbox. Rametrix(™) PRO was used to validate Rametrix(™) LITE models used to detect chronic kidney disease (CKD) in spectra of urine obtained by Raman spectroscopy. The dataset included Raman spectra of urine from 20 healthy individuals and 31 patients undergoing peritoneal dialysis treatment for CKD. RESULTS: The number of spectral principal components (PCs) used in building the DAPC model impacted the model accuracy, sensitivity, and specificity in leave-one-out analyses. For the dataset in this study, using 35 PCs in the DAPC model resulted in 100% accuracy, sensitivity, and specificity in classifying an unknown Raman spectrum of urine as belonging to a CKD patient or a healthy volunteer. Models built with fewer or greater number of PCs showed inferior performance, which demonstrated the value of Rametrix(™) PRO in evaluating chemometric models constructed with Rametrix(™) LITE. PeerJ Inc. 2020-01-06 /pmc/articles/PMC6951287/ /pubmed/31934499 http://dx.doi.org/10.7717/peerj.8179 Text en ©2020 Senger and Robertson https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioengineering
Senger, Ryan S.
Robertson, John L.
The Rametrix(™) PRO Toolbox v1.0 for MATLAB(®)
title The Rametrix(™) PRO Toolbox v1.0 for MATLAB(®)
title_full The Rametrix(™) PRO Toolbox v1.0 for MATLAB(®)
title_fullStr The Rametrix(™) PRO Toolbox v1.0 for MATLAB(®)
title_full_unstemmed The Rametrix(™) PRO Toolbox v1.0 for MATLAB(®)
title_short The Rametrix(™) PRO Toolbox v1.0 for MATLAB(®)
title_sort rametrix(™) pro toolbox v1.0 for matlab(®)
topic Bioengineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951287/
https://www.ncbi.nlm.nih.gov/pubmed/31934499
http://dx.doi.org/10.7717/peerj.8179
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