Gene category testing problems involve testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph.The logical relationships among the nodes in the graph imply that only some configurations of true and false null hypotheses are possible and that a test for a given node should depend on data from neighboring nodes.We use a multivariate nonparametric permutation test to obtain a p-value for each gene category.We then model these p-values with a hiddeu Markov model that takes the relationships among categories into account.Using a Markov chain Monte Carlo approach, we provide coherent decisions about the differential expression status of each category in this structured multiple hypothesis testing problem.The method will be illustrated by testing Gene Ontology terms for evidence of differential expression.