Sparse N-way partial least squares with R package sNPLS

[EN] We introduce the R package sNPLS that performs N-way partial least squares (N-PLS) regression and Sparse (L1-penalized) N-PLS regression in three-way arrays. N-PLS regression is superior to other methods for three-way data based in unfolding, thanks to a better stabilization of the decomposition. This provides better interpretability and improves predictions. The sparse version also adds variable selection through L1 penalization. The sparse version of N-PLS is able to provide lower prediction errors and to further improve interpretability and usability of the N-PLS results. After a short introduction to both methods, the different functions of the package are presented by displaying their use in simulated and a real dataset.Research in this study was partially supported by the Conselleria de Educacion, Investigacion, Cultura y Deporte de la Generalitat Valenciana under the project PROMETEO/2016/093.Hervás-Marín, D.; Prats-Montalbán, JM.; Lahoz Rodríguez, AG.; Ferrer, A. (2018). Sparse N-way partial least squares with R package sNPLS. Chemometrics and Intelligent Laboratory Systems. 179:54-63. https://doi.org/10.1016/j.chemolab.2018.06.005S546317

Sparse N-way Partial Least Squares by L1-penalization

[EN] N-PLS, as the natural extension of PLS to N-way structures, tries to maximize the covariance between an X and a Y N-way data arrays. It provides a useful framework for fitting prediction models to N-way data. However, N-PLS by itself does not perform variable selection, which indeed can facilitate interpretation in different situations (e.g. the so-called ¿¿omics¿ data). In this work, we propose a method for variable selection within N-PLS by introducing sparsity in the weights matrices WJ and WK by means of L1-penalization. The sparse version of N-PLS is able to provide lower prediction errors by filtering all the noise variables and to further improve interpretability and usability of the N-PLS results. To test Sparse N-PLS performance two different simulated data sets were used, whereas to show its utility in a biological context a real time course metabolomics data set was used.Hervás-Marín, D.; Prats-Montalbán, JM.; Garcia-Cañaveras, J.; Lahoz Rodríguez, AG.; Ferrer, A. (2019). Sparse N-way Partial Least Squares by L1-penalization. Chemometrics and Intelligent Laboratory Systems. 185:85-91. https://doi.org/10.1016/j.chemolab.2019.01.004S859118

A score model for the continuous grading of early allograft dysfunction severity

[EN] Early allograft dysfunction (EAD) dramatically influences graft and patient outcomes. A lack of consensus on an EAD definition hinders comparisons of liver transplant outcomes and management of recipients among and within centers. We sought to develop a model for the quantitative assessment of early allograft function [Model for Early Allograft Function Scoring (MEAF)] after transplantation. A retrospective study including 1026 consecutive liver transplants was performed for MEAF score development. Multivariate data analysis was used to select a small number of postoperative variables that adequately describe EAD. Then, the distribution of these variables was mathematically modeled to assign a score for each actual variable value. A model, based on easily obtainable clinical parameters (ie, alanine aminotransferase, international normalized ratio, and bilirubin) and scoring liver function from 0 to 10, was built. The MEAF score showed a significant association with patient and graft survival at 3-, 6- and 12-month follow-ups. Hepatic steatosis and age for donors; cold/warm ischemia times and postreperfusion syndrome for surgery; and intensive care unit and hospital stays, Model for End-Stage Liver Disease and Child-Pugh scores, body mass index, and fresh frozen plasma transfusions for recipients were factors associated significantly with EAD. The model was satisfactorily validated by its application to an independent set of 200 patients who underwent liver transplantation at a different center. In conclusion, a model for the quantitative assessment of EAD severity has been developed and validated for the first time. The MEAF provides a more accurate graft function assessment than current categorical classifications and may help clinicians to make early enough decisions on retransplantation benefits. Furthermore, the MEAF score is a predictor of recipient and graft survival.This work was supported by the Carlos III Institute of Health of the Spanish Ministry of Science and Innovation (PI11/02942). Agustin Lahoz is grateful for a Miguel Server contract (CP08/00125) from the Spanish Ministry of Science and Innovation/Carlos III Institute of HealthPareja, E.; Cortes, M.; Hervás-Marín, D.; Mir, J.; Valdivieso, A.; Castell, JV.; Lahoz Rodríguez, AG. (2015). A score model for the continuous grading of early allograft dysfunction severity. Liver Transplantation. 21(1):38-46. https://doi.org/10.1002/lt.23990384621