PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.

High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often hav...

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Autores principales:	Charlie M Carpenter, Weiming Zhang, Lucas Gillenwater, Cameron Severn, Tusharkanti Ghosh, Russell Bowler, Katerina Kechris, Debashis Ghosh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/5676f381da3d41819c493b41c1b39486
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spelling	oai:doaj.org-article:5676f381da3d41819c493b41c1b394862021-12-02T19:57:28ZPaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.1553-734X1553-735810.1371/journal.pcbi.1008986https://doaj.org/article/5676f381da3d41819c493b41c1b394862021-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008986https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.e., nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway, which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This work extends previous kernel machine approaches. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or "smoothed" graph into a score test, PaIRKAT can provide more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure.Charlie M CarpenterWeiming ZhangLucas GillenwaterCameron SevernTusharkanti GhoshRussell BowlerKaterina KechrisDebashis GhoshPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 10, p e1008986 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Charlie M Carpenter Weiming Zhang Lucas Gillenwater Cameron Severn Tusharkanti Ghosh Russell Bowler Katerina Kechris Debashis Ghosh PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.
description	High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.e., nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway, which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This work extends previous kernel machine approaches. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or "smoothed" graph into a score test, PaIRKAT can provide more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure.
format	article
author	Charlie M Carpenter Weiming Zhang Lucas Gillenwater Cameron Severn Tusharkanti Ghosh Russell Bowler Katerina Kechris Debashis Ghosh
author_facet	Charlie M Carpenter Weiming Zhang Lucas Gillenwater Cameron Severn Tusharkanti Ghosh Russell Bowler Katerina Kechris Debashis Ghosh
author_sort	Charlie M Carpenter
title	PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.
title_short	PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.
title_full	PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.
title_fullStr	PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.
title_full_unstemmed	PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.
title_sort	pairkat: a pathway integrated regression-based kernel association test with applications to metabolomics and copd phenotypes.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/5676f381da3d41819c493b41c1b39486
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_version_	1718375839912951808

PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.

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