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

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 am...

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Autores principales: Liang, Kun, Du, Chuanlong, You, Hankun, Nettleton, Dan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5869792/
https://www.ncbi.nlm.nih.gov/pubmed/29587646
http://dx.doi.org/10.1186/s12859-018-2106-5
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author Liang, Kun
Du, Chuanlong
You, Hankun
Nettleton, Dan
author_facet Liang, Kun
Du, Chuanlong
You, Hankun
Nettleton, Dan
author_sort Liang, Kun
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2106-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-58697922018-03-29 A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets Liang, Kun Du, Chuanlong You, Hankun Nettleton, Dan BMC Bioinformatics Methodology Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2106-5) contains supplementary material, which is available to authorized users. BioMed Central 2018-03-27 /pmc/articles/PMC5869792/ /pubmed/29587646 http://dx.doi.org/10.1186/s12859-018-2106-5 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Liang, Kun
Du, Chuanlong
You, Hankun
Nettleton, Dan
A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets
title A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets
title_full A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets
title_fullStr A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets
title_full_unstemmed A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets
title_short A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets
title_sort hidden markov tree model for testing multiple hypotheses corresponding to gene ontology gene sets
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5869792/
https://www.ncbi.nlm.nih.gov/pubmed/29587646
http://dx.doi.org/10.1186/s12859-018-2106-5
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