Campus Units

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Document Type

Article

Publication Version

Published Version

Publication Date

2018

Journal or Book Title

BMC Bioinformatics

Volume

19

First Page

107

DOI

10.1186/s12859-018-2106-5

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 other existing methods that honor the logical restrictions.

Conclusions: The HMTM method provides an individual estimate of posterior probability of being differentially expressed for each gene set, which can be useful for result interpretation. The R package can be found on https://github.com/k22liang/HMTGO.

Comments

This article is published as Liang, Kun, Chuanlong Du, Hankun You, and Dan Nettleton. "A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets." BMC bioinformatics 19 (2018): 107. doi: 10.1186/s12859-018-2106-5.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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The Authors

Language

en

File Format

application/pdf

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