Positive control as a validation strategy in multiblock analysis: an implementation to a breast cancer subtypes study
In molecular biology, modern technologies generate large databases of certain phenomena allowing to study the same group of individuals from different omics views. This provides data blocks that can be analyzed together through techniques, such as Multiple Factor Analysis (MFA), that allow a multidimensional exploratory approach. When two data blocks provide different information on the same phenomenon, the differences observed could arise from actual biological facts but, they could as well be a result of a confounding factor such as the technology used to obtain the data. In this study, we used MFA to analyze two data blocks (a transcriptomic block and a proteomic block) of gene expression on breast cancer patients, and added a third data block (also transcriptomic but gathered through a different technology) as a positive control. Both transcriptomic data blocks provided highly similar information between them, but different to the information provided by the proteomic block; hence the information provided by the different blocks represent different attributes of a biological phenomenon.