Chapters authored
Kernel Methods for Dimensionality Reduction Applied to the «Omics» Data
Part of the book: Principal Component Analysis
Unveiling Chromosome Changes Compatible with Climate Warming
This work illustrates the use of multivariate descriptive statistics methods adapting different dimensional reduction techniques to the analysis of specific data. The particular nature of the data provides an opportunity to illustrate the pedagogical aim of this work. More explicitly, we will analyze and relate two different kinds of information: climate and genetic data and their change over time. We will show that the relation between both types of changes can be attributed to the unveiled genetic changes being compatible with the adaptation of Drosophila subobscura populations to warmer climates. The climate data are the monthly average temperatures of various populations in Europe and America at two different time periods separated by about 25 years. The genetic data include different profiles of Drosophila subobscura chromosomal polymorphisms that, as shown in the scientific literature, are related to the adaptation of the species to different climates. The genetic data have been obtained in the same populations and times as the climate data.
Part of the book: New Insights on Principal Component Analysis [Working title]