Modeling oncology gene pathways network with multiple genotypes and phenotypes via a copula method

Authors:
Le Bao;Zhou Zhu;Jingjing Ye
Affiliations:
Department of Statistics, University of Washington, Seattle, WA;Structural and Computational Biology, Pfizer Global Research and Development, Pfizer Inc., San Diego, CA;Global Non-Clinical Statistics, Pfizer Global Research and Development, Pfizer Inc, San Diego, CA
Venue:
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Year:
2009

Citing 6
Cited 0

An effective structure learning method for constructing gene networks

Bioinformatics
An Introduction to Copulas (Springer Series in Statistics)

An Introduction to Copulas (Springer Series in Statistics)
Comparison of semiparametric and parametric methods for estimating copulas

Computational Statistics & Data Analysis
A Markov random field model for network-based analysis of genomic data

Bioinformatics
Network-constrained regularization and variable selection for analysis of genomic data

Bioinformatics
Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Identification of interactions between molecular features (e.g. mutation, gene expression change) and gross phenotypes in diseases and other biological processes is one of the important challenges in genomic research. Popular approaches such as GSEA are limited to hypothesis tests of bivariate association. However, a specific phenotype is often dependent upon multiple molecular features. It is thus worth considering all possible interactions jointly for a more precise and realistic representation of the cellular network. In this article, a semiparametric copula model is developed to jointly model genotypes, pathways and phenotypes to accomplish this object. A two-step procedure for reconstruction of the network is described. Simulation studies indicate that the method is effective and accurate for the network reconstruction. Application using NCI60 cancer cell line data identifies several subsets of molecular features that jointly perform as the predictors of clinical phenotypes. The copula model is expected to have a broad impact on biomedical research, ranging from cancer treatment to disease prevention.