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

ABSTRACT:

Background

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.

Results

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

SUBMITTER: Liang K 

PROVIDER: S-EPMC5869792 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature