Cargando…
Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information
Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease s...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5405575/ https://www.ncbi.nlm.nih.gov/pubmed/28487748 http://dx.doi.org/10.1155/2017/8520480 |
_version_ | 1783231797070397440 |
---|---|
author | Kim, SungHwan Jhong, Jae-Hwan Lee, JungJun Koo, Ja-Yong Lee, ByungYong Han, SungWon |
author_facet | Kim, SungHwan Jhong, Jae-Hwan Lee, JungJun Koo, Ja-Yong Lee, ByungYong Han, SungWon |
author_sort | Kim, SungHwan |
collection | PubMed |
description | Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author's webpage. |
format | Online Article Text |
id | pubmed-5405575 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-54055752017-05-09 Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information Kim, SungHwan Jhong, Jae-Hwan Lee, JungJun Koo, Ja-Yong Lee, ByungYong Han, SungWon Comput Math Methods Med Research Article Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author's webpage. Hindawi 2017 2017-04-12 /pmc/articles/PMC5405575/ /pubmed/28487748 http://dx.doi.org/10.1155/2017/8520480 Text en Copyright © 2017 SungHwan Kim et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Kim, SungHwan Jhong, Jae-Hwan Lee, JungJun Koo, Ja-Yong Lee, ByungYong Han, SungWon Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
title | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
title_full | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
title_fullStr | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
title_full_unstemmed | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
title_short | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
title_sort | node-structured integrative gaussian graphical model guided by pathway information |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5405575/ https://www.ncbi.nlm.nih.gov/pubmed/28487748 http://dx.doi.org/10.1155/2017/8520480 |
work_keys_str_mv | AT kimsunghwan nodestructuredintegrativegaussiangraphicalmodelguidedbypathwayinformation AT jhongjaehwan nodestructuredintegrativegaussiangraphicalmodelguidedbypathwayinformation AT leejungjun nodestructuredintegrativegaussiangraphicalmodelguidedbypathwayinformation AT koojayong nodestructuredintegrativegaussiangraphicalmodelguidedbypathwayinformation AT leebyungyong nodestructuredintegrativegaussiangraphicalmodelguidedbypathwayinformation AT hansungwon nodestructuredintegrativegaussiangraphicalmodelguidedbypathwayinformation |