Using Optimal F-Measure and Random Resampling in Gene Ontology Enrichment Calculations

Abstract

Background: A central question in bioinformatics is how to minimize arbitrariness and bias in analysis of patterns of enrichment in data. A prime example of such a question is enrichment of gene ontology (GO) classes in lists of genes. Our paper deals with two issues within this larger question. One is how to calculate the false discovery rate (FDR) within a set of apparently enriched ontologies, and the second how to set that FDR within the context of assessing significance for addressing biological questions, to answer these questions we compare a random resampling method with a commonly used method for assessing FDR, the Benjamini-Hochberg (BH) method. We further develop a heuristic method for evaluating Type II (false negative) errors to enable utilization of F-Measure binary classification theory for distinguishing “significant” from “non-significant” degrees of enrichment. Results: The results show the preferability and feasibility of random resampling assessment of FDR over the analytical methods with which we compare it. They also show that the reasonableness of any arbitrary threshold depends strongly on the structure of the dataset being tested, suggesting that the less arbitrary method of F-measure optimization to determine significance threshold is preferable. Conclusion: Therefore, we suggest using F-measure optimization instead of placing an arbitrary threshold to evaluate the significance of Gene Ontology Enrichment results, and using resampling to replace analytical methods