Variable-selection ANOVA Simultaneous Component Analysis (VASCA)

Camacho, José; Vitale, Raffaele; Morales-Jimenez, D.; Gómez-Llorente, C.

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.1093/bioinformatics/btac795

PMID :

36495189

URL permanente :

http://hdl.handle.net/20.500.12210/109480

Titre :

Variable-selection ANOVA Simultaneous Component Analysis (VASCA)

Auteur(s) :

Camacho, José [Auteur]
Universidad de Granada = University of Granada [UGR]
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Morales-Jimenez, D. [Auteur]
Gómez-Llorente, C. [Auteur]

Titre de la revue :

Bioinformatics

Nom court de la revue :

Bioinformatics

Date de publication :

2022-12-18

ISSN :

1367-4811

Discipline(s) HAL :

Chimie/Chimie théorique et/ou physique

Résumé en anglais : [en]

Motivation ANOVA Simultaneous Component Analysis (ASCA) is a popular method for the analysis of multivariate data yielded by designed experiments. Meaningful associations between factors/interactions of the experimental ...
Lire la suite >Motivation ANOVA Simultaneous Component Analysis (ASCA) is a popular method for the analysis of multivariate data yielded by designed experiments. Meaningful associations between factors/interactions of the experimental design and measured variables in the dataset are typically identified via significance testing, with permutation tests being the standard go-to choice. However, in settings with large numbers of variables, like omics (genomics, transcriptomics, proteomics and metabolomics) experiments, the ‘holistic’ testing approach of ASCA (all variables considered) often overlooks statistically significant effects encoded by only a few variables (biomarkers). Results We hereby propose Variable-selection ASCA (VASCA), a method that generalizes ASCA through variable selection, augmenting its statistical power without inflating the Type-I error risk. The method is evaluated with simulations and with a real dataset from a multi-omic clinical experiment. We show that VASCA is more powerful than both ASCA and the widely adopted false discovery rate controlling procedure; the latter is used as a benchmark for variable selection based on multiple significance testing. We further illustrate the usefulness of VASCA for exploratory data analysis in comparison to the popular partial least squares discriminant analysis method and its sparse counterpart. Availability and implementation The code for VASCA is available in the MEDA Toolbox at https://github.com/josecamachop/MEDA-Toolbox (release v1.3). The simulation results and motivating example can be reproduced using the repository at https://github.com/josecamachop/VASCA/tree/v1.0.0 (DOI 10.5281/zenodo.7410623).Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CNRS

Collections :

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516

Date de dépôt :

2024-02-28T22:15:52Z
2024-03-19T09:24:30Z

Fichiers

btac795.pdf
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Ce document est diffusé sous licence Attribution 3.0 United States

Numéro de version	Lien	Date de modification
2	20.500.12210/109480*	2024-03-19T09:21:56Z
1	20.500.12210/109480.1	2024-02-28T22:15:52Z

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