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

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dc.contributor.authorSenger, Ryan S.en
dc.contributor.authorRobertson, John L.en
dc.contributor.departmentBiological Systems Engineeringen
dc.contributor.departmentBiomedical Engineering and Mechanicsen
dc.contributor.departmentChemical Engineeringen
dc.contributor.departmentFralin Biomedical Research Instituteen
dc.date.accessioned2020-05-27T14:02:21Zen
dc.date.available2020-05-27T14:02:21Zen
dc.date.issued2020-01-06en
dc.description.abstractBackground. Existing tools for chemometric analysis of vibrational spectroscopy data have enabled characterization of materials and biologicals by their broad molecular composition. The Rametrix (TM) LITE Toolbox v1.0 for MATLAB (R) 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 (TM) PRO Toolbox v1.0 is introduced to provide this capability. Methods. The Rametrix (TM) PRO Toolbox v1.0 was constructed for MATLAB (R) and works with the Rametrix (TM) 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 (TM) PRO is available publicly through GitHub under license agreement at: https://github.com/SengerLab/RametrixPROToolbox. Rametrix (TM) PRO was used to validate Rametrix (TM) 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 (TM) PRO in evaluating chemometric models constructed with Rametrix (TM) LITE.en
dc.description.notesThe following grant information was disclosed by the authors: HATCH.en
dc.description.sponsorshipHATCHUnited States Department of Agriculture (USDA)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.7717/peerj.8179en
dc.identifier.issn2167-8359en
dc.identifier.othere8179en
dc.identifier.pmid31934499en
dc.identifier.urihttp://hdl.handle.net/10919/98563en
dc.identifier.volume8en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectRaman spectroscopyen
dc.subjectMATLABen
dc.subjectPrincipal component analysisen
dc.subjectDiscriminant analysisen
dc.subjectSpectral data analysisen
dc.subjectPredictionen
dc.subjectUrineen
dc.subjectNephrologyen
dc.titleThe Rametrix (TM) PRO Toolbox v1.0 for MATLAB (R)en
dc.title.serialPeerJen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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