Genomic and clinical data correlation for relapse prediction in cancer samples

This chapter aims to describe concepts and methods required to understand the development of this investigation. An introduction to cancer research and bioinformatics is provided, considering a description, brief historical review, current status and important challenges for the future. The chapter also considers techniques in statistics and data analysis that provide the basis for the search of relations between genomic and clinical data; this includes data dimensionality reduction techniques, clustering and regression, gene ontology, pathway and co-regulation analysis and the analysis of gene expression distribution. For each subject, a definition is provided, along with a short explanation on how to implement it. Since the beginning of the study of systems biology as an interdisciplinary subject, many concepts have been applied and redeveloped to provide innovative views to old biology problems. With the explosion of data collection, the analysis has become more important, and is still one step behind, since all the collected data remains a partial mystery. The methodologies and possibilities of data analysis are too numerous to be explained in total; however this chapter comprises many of the basic techniques in bioinformatics, and explains the ones that we have used in this investigation.