Abstract
BackgroundExisting 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.MethodsThe 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.ResultsThe 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.
Highlights
IntroductionVibrational spectroscopy, including Raman spectroscopy, has become rapid, portable, and inexpensive (Zarei, 2017; Crocombe, 2018), making it ideal for use in screening assays of biological fluids, cells, or other materials
Through advances in instrumentation, vibrational spectroscopy, including Raman spectroscopy, has become rapid, portable, and inexpensive (Zarei, 2017; Crocombe, 2018), making it ideal for use in screening assays of biological fluids, cells, or other materials
For the urinalysis dataset analyzed in this study, discriminant analysis of principal components (DAPC) models built with 35–38 principal components (PCs) returned 100% accuracy, sensitivity, and specificity in leave-one-out analyses
Summary
Vibrational spectroscopy, including Raman spectroscopy, has become rapid, portable, and inexpensive (Zarei, 2017; Crocombe, 2018), making it ideal for use in screening assays of biological fluids, cells, or other materials. The Raman Chemometrics (RametrixTM) LITE Toolbox (Fisher et al, 2018) was created for MATLAB R to further streamline the creation of Raman-based chemometric screens It offers tools for Raman spectral processing along with PCA, DAPC, and other tools for spectral comparisons in an easy-to-use graphical interface. The RametrixTM 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. The RametrixTM PRO Toolbox v1.0 was constructed for MATLAB R and works with the RametrixTM 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). Models built with fewer or greater number of PCs showed inferior performance, which demonstrated the value of RametrixTM PRO in evaluating chemometric models constructed with RametrixTM LITE
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