2.1. Stage 1: Meta-Analysis for Detection of Highly Differentially Expressed Genes
Meta-analysis involves the joint study of multiple databases to obtain conclusions that apply across all of them. Meta-analysis can help detect potential genetic cancer biomarkers through the study of microarray databases. However, to this end, a series of difficulties are apparent: (a) Microarray experiments that are publicly available use different technologies, platforms and, most of the times, different scales. Incommensurability renders many meta-analyses efforts unfeasible [1] due to the inability to make comparisons across all experiments of interest. Even when the same units are used, often time, data normalization is required for comparability. (b) There is not an efficient, systematic method to carry out meta-analysis. Most of the studies analyze one particular database and try to generalize the results to other databases or analyze several databases separately and try to make sense of all the independent results [2]. (c) The issue of having a large number of measurements and genes generally results in large number of significant genes that must be validated [3]. (d) Meta-analysis of microarrays—and of high throughput biological experiments in general—is a laborious process that is often outpaced by the development of technology to generate ever-larger data sets. That is, data generation capabilities are larger and grow faster than our abilities to make sense and translate these data into usable knowledge. (e) Large repositories of public data generated through costly microarray experiments could go underanalyzed and underutilized in the fight for cancer when the researchers’ attention shifts to the next high-throughput technology. The problem of making sense of large quantities of data, however, will persist.