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A Network-Constrained Empirical Bayes Method For Analysis Of Genomic Data, Caiyan Li, Zhi Wei, Hongzhe Li
A Network-Constrained Empirical Bayes Method For Analysis Of Genomic Data, Caiyan Li, Zhi Wei, Hongzhe Li
UPenn Biostatistics Working Papers
Empirical Bayes methods are widely used in the analysis of microarray gene expression data in order to identify the differentially expressed genes or genes that are associated with other general phenotypes. Available methods often assume that genes are independent. However, genes are expected to function interactively and to form molecular modules to affect the phenotypes. In order to account for regulatory dependency among genes, we propose in this paper a network-constrained empirical Bayes method for analyzing genomic data in the framework of general linear models, where the dependency of genes is modeled by a discrete Markov random field model defined …
Incorporation Of Genetic Pathway Information Into Analysis Of Multivariate Gene Expression Data, Zhi Wei, Jane E. Minturn, Eric Rappaport, Garrett Brodeur, Hongzhe Li
Incorporation Of Genetic Pathway Information Into Analysis Of Multivariate Gene Expression Data, Zhi Wei, Jane E. Minturn, Eric Rappaport, Garrett Brodeur, Hongzhe Li
UPenn Biostatistics Working Papers
Abstract: Multivariate microarray gene expression data are commonly collected to study the genomic responses under ordered conditions such as over increasing/decreasing dose levels or over time during biological processes. One important question from such multivariate gene expression experiments is to identify genes that show different expression patterns over treatment dosages or over time and pathways that are perturbed during a given biological process. In this paper, we develop a hidden Markov random field model for multivariate expression data in order to identify genes and subnetworks that are related to biological processes, where the dependency of the differential expression patterns of …