Abstract
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.
| Original language | English |
|---|---|
| Article number | 8520480 |
| Journal | Computational and Mathematical Methods in Medicine |
| Volume | 2017 |
| DOIs | |
| Publication status | Published - 2017 Jan 1 |
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ASJC Scopus subject areas
- Modelling and Simulation
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Applied Mathematics
Cite this
Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information. / Kim, Sung Hwan; Jhong, Jae Hwan; Lee, Jung Jun; Koo, Ja Yong; Lee, Byung Yong; Han, Sung Won.
In: Computational and Mathematical Methods in Medicine, Vol. 2017, 8520480, 01.01.2017.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information
AU - Kim, Sung Hwan
AU - Jhong, Jae Hwan
AU - Lee, Jung Jun
AU - Koo, Ja Yong
AU - Lee, Byung Yong
AU - Han, Sung Won
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
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U2 - 10.1155/2017/8520480
DO - 10.1155/2017/8520480
M3 - Article
C2 - 28487748
AN - SCOPUS:85018822947
VL - 2017
JO - Computational and Mathematical Methods in Medicine
JF - Computational and Mathematical Methods in Medicine
SN - 1748-670X
M1 - 8520480
ER -