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Joint network and node selection for pathway-based genomic data analysis.

Zhe S, Naqvi SA, Yang Y, Qi Y - Bioinformatics (2013)

Bottom Line: Simulation results demonstrate improved predictive performance and selection accuracy of our method over alternative methods.In addition to pathway analysis, our method is expected to have a wide range of applications in selecting relevant groups of correlated high-dimensional biomarkers.The code can be downloaded at www.cs.purdue.edu/homes/szhe/software.html. alanqi@purdue.edu.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Department of Biology, and Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.

ABSTRACT

Motivation: By capturing various biochemical interactions, biological pathways provide insight into underlying biological processes. Given high-dimensional microarray or RNA-sequencing data, a critical challenge is how to integrate them with rich information from pathway databases to jointly select relevant pathways and genes for phenotype prediction or disease prognosis. Addressing this challenge can help us deepen biological understanding of phenotypes and diseases from a systems perspective.

Results: In this article, we propose a novel sparse Bayesian model for joint network and node selection. This model integrates information from networks (e.g. pathways) and nodes (e.g. genes) by a hybrid of conditional and generative components. For the conditional component, we propose a sparse prior based on graph Laplacian matrices, each of which encodes detailed correlation structures between network nodes. For the generative component, we use a spike and slab prior over network nodes. The integration of these two components, coupled with efficient variational inference, enables the selection of networks as well as correlated network nodes in the selected networks. Simulation results demonstrate improved predictive performance and selection accuracy of our method over alternative methods. Based on three expression datasets for cancer study and the KEGG pathway database, we selected relevant genes and pathways, many of which are supported by biological literature. In addition to pathway analysis, our method is expected to have a wide range of applications in selecting relevant groups of correlated high-dimensional biomarkers.

Availability: The code can be downloaded at www.cs.purdue.edu/homes/szhe/software.html.

Contact: alanqi@purdue.edu.

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Related in: MedlinePlus

The graphical model representation of NaNOS
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btt335-F1: The graphical model representation of NaNOS

Mentions: Specifically, to link the conditional and generative models together, we introduce a prior on :(5)where the variance λ controls how similar and are in our joint model. For simplicity, we set so that where if f = 0 and otherwise. The graphical model representation of the joint model is given in Figure 1.Fig. 1.


Joint network and node selection for pathway-based genomic data analysis.

Zhe S, Naqvi SA, Yang Y, Qi Y - Bioinformatics (2013)

The graphical model representation of NaNOS
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3722525&req=5

btt335-F1: The graphical model representation of NaNOS
Mentions: Specifically, to link the conditional and generative models together, we introduce a prior on :(5)where the variance λ controls how similar and are in our joint model. For simplicity, we set so that where if f = 0 and otherwise. The graphical model representation of the joint model is given in Figure 1.Fig. 1.

Bottom Line: Simulation results demonstrate improved predictive performance and selection accuracy of our method over alternative methods.In addition to pathway analysis, our method is expected to have a wide range of applications in selecting relevant groups of correlated high-dimensional biomarkers.The code can be downloaded at www.cs.purdue.edu/homes/szhe/software.html. alanqi@purdue.edu.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Department of Biology, and Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.

ABSTRACT

Motivation: By capturing various biochemical interactions, biological pathways provide insight into underlying biological processes. Given high-dimensional microarray or RNA-sequencing data, a critical challenge is how to integrate them with rich information from pathway databases to jointly select relevant pathways and genes for phenotype prediction or disease prognosis. Addressing this challenge can help us deepen biological understanding of phenotypes and diseases from a systems perspective.

Results: In this article, we propose a novel sparse Bayesian model for joint network and node selection. This model integrates information from networks (e.g. pathways) and nodes (e.g. genes) by a hybrid of conditional and generative components. For the conditional component, we propose a sparse prior based on graph Laplacian matrices, each of which encodes detailed correlation structures between network nodes. For the generative component, we use a spike and slab prior over network nodes. The integration of these two components, coupled with efficient variational inference, enables the selection of networks as well as correlated network nodes in the selected networks. Simulation results demonstrate improved predictive performance and selection accuracy of our method over alternative methods. Based on three expression datasets for cancer study and the KEGG pathway database, we selected relevant genes and pathways, many of which are supported by biological literature. In addition to pathway analysis, our method is expected to have a wide range of applications in selecting relevant groups of correlated high-dimensional biomarkers.

Availability: The code can be downloaded at www.cs.purdue.edu/homes/szhe/software.html.

Contact: alanqi@purdue.edu.

Show MeSH
Related in: MedlinePlus