With the advancements of next-generation sequencing (NGS) technologies, a massive volume of genetic data has been generated. It makes possible the study of complex disease by computational approaches. In the context of cancer, there is a huge variety of mutation data in public databases. However, it is not feasible to use all available data in every analysis; thus, a data subset must be selected. This work is aiming to investigate and understand the mutational characteristics presented in different cancer mutation data sets of the same type of cancer. To achieve this goal, exploration and visualization of cancer mutation data were performed. Several analyses are presented for three common types of cancer: 1) Breast Invasive Carcinoma (BRCA); 2) Lung Adenocarcinoma (LUAD); and Prostate Adenocarcinoma (PRAD). For each cancer type, three distinct data sets were analyzed in order to understand if there are significant differences or similarities among them. The analyses show that BRCA and LUAD have evidence of similarity among their data sets, while PRAD is likely heterogeneous.
Analyzing different cancer mutation data sets from breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), and prostate adenocarcinoma (PRAD)
