| dc.description.abstract | Over the latest decades, there has been a rapid development of laboratory techniques used for analyzing the pathways from the genes to the final physiology of an organism. The study of all genes in an organism is defined as genomics. In analog to this, studies of all the features along the path from the genes to the final phenotype is called transcriptomics, proteomics, metabolomics, lipidomics etc., with omics as a common name that covers them all. The comprehensive information that is now available on all the features in the cell, opens insight into biological mechanisms controlling growth and development of an organism at a level that was never before possible. This can be utilized, for example, in health care for improved diagnostics, improved medical treatment of diseases, and for personalized measures and preventive advice. In agricultural and food science, it opens opportunities for tailoring plants and animals with desirable properties, and for better microbiological control to improve food quality and safety. The data that are generated are comprehensive. Both from a biological and from a data analytical point of view, scientists are facing major challenges in the research area of analyzing the functionality that the omics data may uncover. The field of functional omics covers this. For each platform of molecular, chemical and biochemical analysis that is applied, special attentions needs to be drawn on pre-processing the data, which is beyond the scope of the present book chapter. This chapter aims to address some of the generic data analytical challenges in analyzing functional omics, which takes into realization the complexity of the comprehensive information that is uncovered by detailed studies on the molecular processes in the cells. We presents guidelines to some useful approaches for data analysis, focusing on building bridges between those that have insight into the biochemistry and biology of the cell and those that have insight into data modeling. This article points to important challenges to be considered in the analysis of omics data and it goes through practical analysis of several data sets to illustrate different useful multivariate methodologies that builds on a chemometric mindset of gaining insight into the phenomenon under study. | en_US |
