659e381e-8372-47ff-82f7-acb9b81f0e65 1.0 Multivariate Exploratory Data Analysis Toolbox Template 2023-12-31T00:00:00 This workflow integrates the MEDA Toolbox for Matlab and Octave, focusing on data simulation, Principal Component Analysis (PCA), and result visualization. Key steps include simulating multivariate data, applying PCA for data modeling, and creating interactive visualizations. The MEDA Toolbox combines traditional and advanced methods, such as ANOVA Simultaneous Component Analysis (ASCA). The aim is to integrate the MEDA Toolbox into LifeWatch, providing tools for enhanced data analysis and visualization in research.<div><br></div><div>Background</div><div>This workflow is a template for the integration of the Multivariate Exploratory Data Analysis Toolbox (MEDA Toolbox, https://github.com/codaslab/MEDA-Toolbox) in LifeWatch. The MEDA Toolbox for Matlab and Octave is a set of multivariate analysis tools for the exploration of data sets. There are several alternative tools in the market for that purpose, both commercial and free. The PLS_Toolbox from Eigenvector Inc. is a very nice example. The MEDA Toolbox is not intended to replace or compete with any of these toolkits. Rather, the MEDA Toolbox is a complementary tool that includes several contributions of the Computational Data Science Laboratory (CoDaS Lab) to the field of data analysis. Thus, traditional exploratory plots based on Principal Component Analysis (PCA) or Partial Least Squares (PLS), such as score, loading, and residual plots, are combined with new methods: MEDA, oMEDA, SVI plots, ADICOV, EKF &amp; CKF cross-validation, CSP, GPCA, etc. A main tool in the MEDA Toolbox which has received a lot of attention lately is ANOVA Simultaneous Component Analysis (ASCA). The ASCA code in the MEDA Toolbox is one of the most advanced internationally.</div><div><br></div><div>Introduction</div><div>The workflow integrates three examples of functionality within the MEDA Toolbox. First, there is a data simulation step, in which a matrix of random data is simulated with a user-defined correlation level. The output is sent to a modeling step, in which Principal Component Analysis (PCA) is computed. The PCA model is then sent to a visualization module.</div><div><br></div><div>Aims</div><div>The main goal of this template is the integration of the MEDA Toolbox in LifeWatch, including data simulation, data modeling, and data visualization routines.</div><div><br></div><div>Scientific Questions</div><div>This workflow only exemplifies the integration of the MEDA Toolbox. No specific questions are addressed.</div> José Camacho University of Granada josecamacho@ugr.es Francisco Manuel SÁNCHEZ-CANO LifeWatch ERIC ICT Core franciscom.sanchez@lifewatch.eu Antonio José SÁENZ-ALBANÉS LifeWatch ERIC ICT Core aj.saenz@lifewatch.eu ICT Core Group LifeWatch ERIC ICT Core ict.coordination@lifewatch.eu Ana MELLADO-GARCÍA LifeWatch ERIC ICT Core ana.mellado@lifewatch.eu Multivariate Exploratory Data Analysis MEDA Toolbox Matlab Octave Data Simulation Principal Component Analysis (PCA) Data Visualization ANOVA Simultaneous Component Analysis (ASCA) Data Science Interactive Visualizations Computational Data Science Laboratory (CoDaS Lab) To be confirmed simuleMV Simulation of random multivariate data with correlation. Output Model A data structure (struct named ‘model’) with fields related to a PCA model: ‘var’ contains the total variance in the input data, ‘lvs’ is a list of principal components considered, ‘loads’ contain the PCA loadings, ‘scores’ contains the PCA scores, ‘type’ contains the string ‘PCA’ pca_pp Principal Component Analysis based on the Singular Value Decomposition. Scores Scatter plot of scores https://helpdesk.lifewatch.eu DOI