@misc{10481/79812,
year = {2022},
month = {12},
url = {https://hdl.handle.net/10481/79812},
abstract = {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.},
organization = {Agencia Andaluza del Conocimiento, Regional Government of Andalucia , in Spain},
organization = {European Commission	B-TIC-136-UGR20},
organization = {State Research Agency (AEI) of Spain},
organization = {European Social Fund (ESF)	RYC2020-030536-I},
organization = {AEI	PID2020-118139RB-I00},
publisher = {Oxford University Press},
title = {Variable-selection ANOVA Simultaneous Component Analysis (VASCA)},
doi = {10.1093/bioinformatics/btac795},
author = {Camacho Páez, José and Morales Jiménez, David and Gómez Llorente, Carolina},
}