Using a coloring algorithm

A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets

Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding
We develop a computationally efficient method based on a hidden Markov tree model (HMTM). Our method is several orders of magnitude faster than the existing fully Bayesian method. Through simulation and an expression quantitative trait loci study, we show that the HMTM method provides more powerful results than other existing methods that honor the logical restrictions.

Jumping the Markov tree adjusting path thinning for the new resolution.  This paper needs discussion in our new book. In fact, there is already a lot of stuff on this, too much for me to read.
The model works directly from the directed graph of gene decomposition.  Good stuff.