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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/207062
Dimensionality reduction in multigroup data: applications in integrative omics
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[eng] The idea of this research is to propose dimensionality reduction methods considering multi- groups in a dataset. In multivariate analysis, there are many multigroup methods, but they have different objectives than the goals presented in this thesis.
Classical principal component analysis (PCA) is considered in this research as it has some- thing similar to our objective which is the exploration and visualization of the dataset. However, this unsupervised method lacks in considering the multigroup configuration.
The thesis presents two multivariate dimension reduction approaches under a multigroup configuration. Statistical simulation helps us better observe and control the parameters of interest with these new methods proposed in this research. Thus, in this way, it helps us to conclude how they contribute to the literature of multivariate techniques in the visualization and exploration of high-dimensional data analysis.
The method, multigroup principal component analysis (mgPCA), is based on maximizing the interdistances between pairs of observations when the observations belong to different groups.
The second method, multigroup dimension reduction (MDR), determines linear varieties that minimize overlap by comparing observations in one group with the rest of the observations in the other groups.
It is worth mentioning that a package was created in the R statistical programming lan- guage called MultiGroupO containing our two dimensionality reduction methods, and with vignettes, for better explanation and visualization of our multivariate multigroup approaches on omics datasets or any data analysis.
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MILLAPÁN TOLEDO, Carolina andrea. Dimensionality reduction in multigroup data: applications in integrative omics. [consulta: 27 de novembre de 2025]. [Disponible a: https://hdl.handle.net/2445/207062]