Assessing the functional coherence of gene sets with metrics based on the Gene Ontology graph

Abstract

Motivation: The results of initial analyses for many high-throughput technologies commonly take the form of gene or protein sets, and one of the ensuing tasks is to evaluate the functional coherence of these sets. The study of gene set function most commonly makes use of controlled vocabulary in the form of ontology annotations. For a given gene set, the statistical significance of observing these annotations or ‘enrichment’ may be tested using a number of methods. Instead of testing for significance of individual terms, this study is concerned with the task of assessing the global functional coherence of gene sets, for which novel metrics and statistical methods have been devised.Results: The metrics of this study are based on the topological properties of graphs comprised of genes and their Gene Ontology annotations. A novel aspect of these methods is that both the enrichment of annotations and the relationships among annotations are considered when determining the significance of functional coherence. We applied our methods to perform analyses on an existing database and on microarray experimental results. Here, we demonstrated that our approach is highly discriminative in terms of differentiating coherent gene sets from random ones and that it provides biologically sensible evaluations in microarray analysis. We further used examples to show the utility of graph visualization as a tool for studying the functional coherence of gene sets.Availability: The implementation is provided as a freely accessible web application at: http://projects.dbbe.musc.edu/gosteiner. Additionally, the source code written in the Python programming language, is available under the General Public License of the Free Software Foundation.Contact: lux@musc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Hypergraph visualization and enrichment statistics: how the EGAN paradigm facilitates organic discovery from big data

“…Gene Ontology terms, for example, are actually organized in a tree-like hierarchy. This would be an interesting feature to explore with respect to modeling, visualization and enrichment algorithms 24 . The relationships rendered as edges in EGAN (entity-entity relationships and entity-set associations) are discrete: a relationship exists or it does not.…”

Conceptualization of molecular findings by mining gene annotations

“…where i and j indicate two GO terms, d G is the set of edge weights for all the edges in the GOGraph G , d P5 is the value of the weight of the 5th percentile, and | g i, j | is the number of genes that are shared between the GO terms i and j [26]. After augmenting the graph, we then found a Steiner tree and used its total weight to reflect the total information loss.…”

“…We use the Nadaraya-Watson non-parametric regression [26,28] to capture the non-linear relationship between the size n and the parameters of random gene sets of the same size, using the following equations:…”

Analysis of Parkinson's disease pathophysiology using an integrated genomics-bioinformatics approach

“…An important application of the GO is to perform the enrichment analysis that identifies which GO categories are over-represented on the gene sets of interest. Along this line, the use of GO graphs for coding the relationships among annotations was shown to further improve the enrichment analysis [19].…”