Analysis of longitudinal metabolomic data using multivariate curve resolution-alternating least squares and pathway analysis

Extraction of meaningful biological information from longitudinal metabolomic studies is a major challenge and typically involves multivariate analysis and dimensional reduction methods for data visualization such as Principal Component Analysis or Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Besides, a variety of computational tools have been developed to identify changes in metabolic pathways including functional analysis and pathway analysis. In this work, the joint analysis of results from MCR-ALS and metabolic pathway analysis is proposed to facilitate the interpretation of dynamic changes in longitudinal metabolomic data. The strategy is based on the use of MCR-ALS to remove unstructured random variation in the raw data, thus facilitating the interpretation of dynamic changes observed by metabolic pathway analysis over time. A simulated data set representing dynamic longitudinal changes in the intensities of a subset of metabolites from three metabolic pathways was initially used to test the applicability of MCR-ALS to support pathway analysis for detecting pathway perturbations. Then, the strategy is applied to real data acquired for the analysis of changes during CD8+ T cell activation. Results obtained show that MCR-ALS facilitates the interpretation of longitudinal metabolomic profiles in multivariate data sets by identifying metabolic pathways associated with each detected dynamic component

Evaluation of the effect of chance correlations on variable selection using Partial Least Squares -Discriminant Analysis

Variable subset selection is often mandatory in high throughput metabolomics and proteomics. However, depending on the variable to sample ratio there is a significant susceptibility of variable selection towards chance correlations. The evaluation of the predictive capabilities of PLSDA models estimated by cross-validation after feature selection provides overly optimistic results if the selection is performed on the entire set and no external validation set is available. In this work, a simulation of the statistical null hypothesis is proposed to test whether the discrimination capability of a PLSDA model after variable selection estimated by cross-validation is statistically higher than that attributed to the presence of chance correlations in the original data set. Statistical significance of PLSDA CV-figures of merit obtained after variable selection is expressed by means of p-values calculated by using a permutation test that included the variable selection step. The reliability of the approach is evaluated using two variable selection methods on experimental and simulated data sets with and without induced class differences. The proposed approach can be considered as a useful tool when no external validation set is available and provides a straightforward way to evaluate differences between variable selection methods.JE and JK acknowledge the "Sara Borrell" Grants (CD11/00154 and CD12/00667) from the Instituto Carlos III (Ministry of Economy and Competitiveness). DPG acknowledge the "V Segles" Grant provided by the University of Valencia to carry out this study. MV acknowledges the FISPI11/0313 Grant from the Instituto Carlos III (Ministry of Economy and Competitiveness). AF acknowledges the DPI2011-28112-C04-02 Grant from Spanish Ministry of Science and Innovation (MICINN). GQ acknowledges the financial support from the Spanish Ministry of Economy and Competitivity (SAF2012-39948).Kuligowski, J.; Pérez Guaita, D.; Escobar, J.; Guardia, MDL.; Vento, M.; Ferrer Riquelme, AJ.; Quintás, G. (2013). Evaluation of the effect of chance correlations on variable selection using Partial Least Squares -Discriminant Analysis. Talanta. 116:835-840. https://doi.org/10.1016/j.talanta.2013.07.048S83584011

Proyecto, investigación e innovación en urbanismo, arquitectura y diseño industrial

Actas de congresoLas VII Jornadas de Investigación “Encuentro y Reflexión” y I Jornadas de Investigación de becarios y doctorandos. Proyecto, investigación e innovación en Urbanismo, Arquitectura y Diseño Industrial se centraron en cuatro ejes: el proyecto; la dimensión tecnológica y la gestión; la dimensión social y cultural y la enseñanza en Arquitectura, Urbanismo y Diseño Industrial, sustentados en las líneas prioritarias de investigación definidas epistemológicamente en el Consejo Asesor de Ciencia y Tecnología de esta Universidad Nacional de Córdoba. Con el objetivo de afianzar continuidad, formación y transferencia de métodos, metodología y recursos se incorporó becarios y doctorandos de los Institutos de investigación. La Comisión Honoraria la integraron las tres Secretarias de Investigación de la Facultad, arquitectas Marta Polo, quien fundó y María del Carmen Franchello y Nora Gutiérrez Crespo quienes continuaron la tradición de la buena práctica del debate en la cotidianeidad de la propia Facultad. Los textos que conforman las VII Jornadas son los avances y resultados de las investigaciones realizadas en el bienio 2016-2018.Fil: Novello, María Alejandra. Universidad Nacional de Córdoba. Facultad de Arquitectura, Urbanismo y Diseño; ArgentinaFil: Repiso, Luciana. Universidad Nacional de Córdoba. Facultad de Arquitectura, Urbanismo y Diseño; ArgentinaFil: Mir, Guillermo. Universidad Nacional de Córdoba. Facultad de Arquitectura, Urbanismo y Diseño; ArgentinaFil: Brizuela, Natalia. Universidad Nacional de Córdoba. Facultad de Arquitectura, Urbanismo y Diseño; ArgentinaFil: Herrera, Fernanda. Universidad Nacional de Córdoba. Facultad de Arquitectura, Urbanismo y Diseño; ArgentinaFil: Períes, Lucas. Universidad Nacional de Córdoba. Facultad de Arquitectura, Urbanismo y Diseño; ArgentinaFil: Romo, Claudia. Universidad Nacional de Córdoba. Facultad de Arquitectura, Urbanismo y Diseño; ArgentinaFil: Gordillo, Natalia. Universidad Nacional de Córdoba. Facultad de Arquitectura, Urbanismo y Diseño; ArgentinaFil: Andrade, Elena Beatriz. Universidad Nacional de Córdoba. Facultad de Arquitectura, Urbanismo y Diseño; Argentin

Teaching of Machine Learning and Chemometrics in Analytical Chemistry Based on Interactive Hands-on Activities

[EN] Despite the growing popularity of machine learning (ML), the teaching of this disruptive field in analytical chemistry is challenging due to the lack of enough programming background in both, professors, and students. Because of that, this subject is sometimes underrated or even ignored in chemistry curriculums. In this work, we firstly surveyed the previous knowledge in multivariate analysis and programming by students enrolled in the master’s degree in chemistry. Upon recognizing a deficiency in fundamental programming and statistical principles, we carried out actions to close the gap between ML and analytical chemistry in under- and post-graduate level. Accordingly, we proposed the use of the interactive software Orange and the programming of apps with MATLAB for teaching ML in the laboratory lessons of analytical chemistry. With this approach, two laboratory lessons were designed and conducted which are focused on analysis of foodstuffs by infrared spectroscopy and using ML in daily contexts. The evaluation of the methodologies proposed indicated that the use of interactive software made ML more appealing to the students and contributed to a better understanding of ML concepts.[ES] A pesar de la creciente popularidad de las técnicas de aprendizaje automático (“machine learning”, en inglés) su enseñanza en química analítica es un reto debido a la falta de conocimientos en programación por parte del alumnado y el profesorado. Debido a ello, estas técnicas suelen obviarse o incluirse sucintamente en los currículos. En este trabajo, estudiamos los conocimientos previos de programación y análisis multivariante del alumnado matriculado en el Máster en Química e identificamos diferentes deficiencias en conceptos básicos de programación y estadística. En consecuencia, para cerrar la brecha entre el aprendizaje automático y la química analítica, planteamos el uso del programa interactivo Orange y la programación de aplicaciones con MATLAB. Utilizando este enfoque, diseñamos y llevamos a cabo dos sesiones prácticas consistentes en el análisis de alimentos mediante espectroscopía infrarroja y en la implementación de modelos de aprendizaje automático aplicados a contextos cotidianos. Tras evaluar las metodologías propuestas, comprobamos que estas hacen el aprendizaje automático más atractivo para el estudiantado contribuyendo a su mejor aprendizaje.Ayuda Margarita Salas (ref. UP2021-044-MS21-084) del Ministerio de Universidades-Next Generation EU; Ayuda RyC (ref. RYC2019-026556-I) Ministerio de Investigación y Ciencia (MCIN/AEI/10.13039/501100011033).Sánchez Illana, Á.; Wood, B.; Pérez Guaita, D. (2023). Enseñanza del machine learning y la quimiometría en química analítica mediante propuestas prácticas e interactivas. Editorial Universitat Politècnica de València. 246-260. https://doi.org/10.4995/INRED2023.2023.1667924626

Combining Pharmacokinetics and Vibrational Spectroscopy: MCR-ALS Hard-and-Soft Modelling of Drug Uptake In Vitro Using Tailored Kinetic Constraints

Raman microspectroscopy is a label-free technique which is very suited for the investigation of pharmacokinetics of cellular uptake, mechanisms of interaction, and efficacies of drugs in vitro. However, the complexity of the spectra makes the identification of spectral patterns associated with the drug and subsequent cellular responses difficult. Indeed, multivariate methods that relate spectral features to the inoculation time do not normally take into account the kinetics involved, and important theoretical information which could assist in the elucidation of the relevant spectral signatures is excluded. Here, we propose the integration of kinetic equations in the modelling of drug uptake and subsequent cellular responses using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and tailored kinetic constraints, based on a system of ordinary differential equations. Advantages of and challenges to the methodology were evaluated using simulated Raman spectral data sets and real Raman spectra acquired from A549 and Calu-1 human lung cells inoculated with doxorubicin, in vitro. The results suggest a dependency of the outcome on the system of equations used, and the importance of the temporal resolution of the data set to enable the use of complex equations. Nevertheless, the use of tailored kinetic constraints during MCR-ALS allowed a more comprehensive modelling of the system, enabling the elucidation of not only the time-dependent concentration profiles and spectral features of the drug binding and cellular responses, but also an accurate computation of the kinetic constants.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 796287. D.P.-G. acknowledges the financial support of the 2019 Ramón y Cajal (RYC) Contract Aids (RYC2019-026556-I) funded by MCIN/AEI/10.13039/501100011033 y FSE “El FSE invierte en tu futuro’’ and Grant RPID2020-119326RA-I0 funded by MCIN/AEI/10.13039/501100011033. GQ acknowledges support from the Agencia Estatal de Investigacíon (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER) (CTQ2016-79561-P). Experimental work was carried out under Science Foundation Ireland Principle Investigator Award 11/PI/1108.Peer reviewe

Cluster-Partial Least Squares (c-PLS) regression analysis: application to miRNA and metabolomic data

<p>This study introduces a novel variable selection approach called cluster PLS (c-PLS) that aims to assess the joint impact of variable groups selected based on biological characteristics (such as miRNA-regulated metabolic pathway) on the predictive performance of a multivariate model. The usefulness of c-PLS is shown using a miRNomic datasets obtained from the analysis of 24 liver tissue biopsies collected in the frame of a clinical study of steatosis.</p><p>Results obtained show that c-PLS enables analyzing the effect of biologically relevant variable clusters, facilitating the identification of biological processes associated with the independent variable, and the prioritization of the biological factors influencing model performance, thereby improving the understanding of the biological factors driving model predictions. While the strategy is tested for the evaluation of PLS models, it could be extended to other linear and non-linear multivariate models.</p>GQ acknowledges the grant PID2021-125573OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe, by the "European Union". JK acknowledges the financial support by the Instituto de Salud Carlos III [grant numbers PI20/00964 and CPII21/00003] (Co-funded by European Regional Development Fund "A way to make Europe"). RJ acknowledges grant PI20/00690 funded by Instituto de Salud Carlos III-FIS (Co-funded by European Regional Development Fund "A way to make Europe"). D.P.-G. acknowledges the financial support of the 2019 Ramón y Cajal (RYC) Contract Aids (RYC2019-026556-I) funded by MCIN/AEI/10.13039/501100011033 and FSE "El FSE invierte en tu futuro'' and the grant RPID2020-119326RA-I0 funded by MCIN/AEI/10.13039/501100011033. MMT acknowledges the grant RYC2021-031346-I, funded by MCIN/AEI/10.13039/501100011033 and by the European Union "NextGenerationEU''/PRTR