Analyzing omics data from clinical interventions with ASCA

J. Camacho Páez, C. Díaz, P. Sánchez Rovira

Analysis of Variance Simultaneous Component Analysis (ASCA) is a multivariate methodology that combines the variance factorization and statistical inference capability of the Analysis of Variance (ANOVA) with the exploratory data analysis power of Principal Component Analysis (PCA). Permutation tests are the standard technique for significance testing in ASCA. In this presentation, we focus on longitudinal intervention studies with multivariate outcomes, a relevant experimental design in clinical studies where the outcome is an omics profile (such as in genomics, metabolomics, and the like). We show that choosing the best permutation approach is far from intuitive and that there is a significant risk of deriving incorrect conclusions in real-life analyses.

Keywords: ASCA, Permutation Tests, Longitudinal Intervention Studies, Omics, Power Analysis, Power Curves

Scheduled

Multivariate Analysis II
June 8, 2022  5:20 PM
Cloister room

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Capacidad de clasificación de un marcador bivariante a través de restricciones lineales

S. Pérez Fernández, P. Martínez Camblor, N. Corral Blanco

A diagonality measure for covariance matrices. Statistical applications.

C. Matrán Bea, D. Rodríguez Vítores

Autoevaluación en un concurso de 1000 proyectos

J. J. Egozcue, V. Pawlowsky-Glahn

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