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DNA analysis is now a data intensive discipline. New technology has transformed biomedical research by making a plethora of molecular data available at reduced costs and great speeds. Large consortiums and many individual laboratories have already generated vast datasets: as an example, one such database, the Gene Expression Omnibus (GEO contains more than 1.8 million samples. This data is readily, publicly available but analyzing it requires computational and statistical resources. A popular concern in biological research is to identify those genomic pathways that are related to the organism’s reaction to treatment or disease. There are numerous techniques that try to reduce the false positive errors and rank the pathways according to the degree of the phenotype relationship strength. This goal is accompanied by several challenges: finding parsimonious models with a good balance between simplicity and complexity and designing methods for pathway selection using appropriate significance thresholds. Often, it is difficult to escape the temptation of "ad-hoc" procedures that may work for particular examples but cannot be properly expanded to general cases. Over the years, many methods have been proposed but over-representation analysis (ORA) remains the most popular. The underlying assumption of ORA is that pathways with an irregular number of differentially expressed genes are responsible for the phenotype to the detriment of lesser differentially expressed pathways. Under the umbrella of logistic regression, we propose a method that aims to improve ORA. We show that traditional hypergeometric ORA methods are fully described by and can be considered a special case of the logistic regression methods. Logistic regression presents the advantage that while it produces simple models, they are richer, and they describe the biological process in a more accurate fashion. While logistic regression has been proposed before as a solution for ORA, we prove the over-encompassing nature of the method and we also propose a flavor of regression that can be aimed at different scenarios. Furthermore, logistic regression has a solid mathematical basis and produces results that have biological justification. |
