Abstract
BackgroundGene set testing has become an important analysis technique in high throughput microarray and next generation sequencing studies for uncovering patterns of differential expression of various biological processes. Often, the large number of gene sets that are tested simultaneously require some sort of multiplicity correction to account for the multiplicity effect. This work provides a substantial computational improvement to an existing familywise error rate controlling multiplicity approach (the Focus Level method) for gene set testing in high throughput microarray and next generation sequencing studies using Gene Ontology graphs, which we call the Short Focus Level.ResultsThe Short Focus Level procedure, which performs a shortcut of the full Focus Level procedure, is achieved by extending the reach of graphical weighted Bonferroni testing to closed testing situations where restricted hypotheses are present, such as in the Gene Ontology graphs. The Short Focus Level multiplicity adjustment can perform the full top-down approach of the original Focus Level procedure, overcoming a significant disadvantage of the otherwise powerful Focus Level multiplicity adjustment. The computational and power differences of the Short Focus Level procedure as compared to the original Focus Level procedure are demonstrated both through simulation and using real data.ConclusionsThe Short Focus Level procedure shows a significant increase in computation speed over the original Focus Level procedure (as much as ∼15,000 times faster). The Short Focus Level should be used in place of the Focus Level procedure whenever the logical assumptions of the Gene Ontology graph structure are appropriate for the study objectives and when either no a priori focus level of interest can be specified or the focus level is selected at a higher level of the graph, where the Focus Level procedure is computationally intractable.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0349-3) contains supplementary material, which is available to authorized users.
Highlights
Gene set testing has become an important analysis technique in high throughput microarray and generation sequencing studies for uncovering patterns of differential expression of various biological processes
Each Gene Ontology (GO) Term typically contains a definition of its biological process and other annotation as well as a mapping of all known gene products involved in its specified process
The following simulations demonstrate that neither method is uniformly more powerful than the other, with each having the advantage for certain scenarios. As these simulations demonstrate, the newly proposed Short Focus Level procedure is uniformly computationally faster than the Focus Level method which will hopefully better enable its use by practitioners
Summary
Gene set testing has become an important analysis technique in high throughput microarray and generation sequencing studies for uncovering patterns of differential expression of various biological processes. This work provides a substantial computational improvement to an existing familywise error rate controlling multiplicity approach (the Focus Level method) for gene set testing in high throughput microarray and generation sequencing studies using Gene Ontology graphs, which we call the Short Focus Level. Microarray technology and generation sequencing have played an important role in discovering important associations between gene expression patterns and phenotype [1]. The Gene Ontology (GO) provides a controlled vocabulary that is not specific to any particular species This vocabulary is divided into three general ontologies, Molecular Function (MF), Cellular Component (CC), and Biological Process (BP). The remainder of this work will focus on the biological process ontology
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