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Published on: July 29, 2022
Network-based analysis of multivariate gene expression data
Wei Zhi1, Jane Minturn, Eric Rappaport
1Department of Biostatistics and Epidemiology, New Jersey Institute of Technology, Newark, NJ, USA.
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, where the expression levels of a give gene are expected to be dependent. One important question from such multivariate gene expression experiments is to identify genes that show different expression patterns over treatment dosages or over time; these genes can also point to the pathways that are perturbed during a given biological process. Several empirical Bayes approaches have been developed for identifying the differentially expressed genes in order to account for the parallel structure of the data and to borrow information across all the genes. However, these methods assume that the genes are independent. In this paper, we introduce an alternative empirical Bayes approach for analysis of multivariate gene expression data by assuming a discrete Markov random field (MRF) prior, where the dependency of the differential expression patterns of genes on the networks are modeled by a Markov random field. Simulation studies indicated that the method is quite effective in identifying genes and the modified subnetworks and has higher sensitivity than the commonly used procedures that do not use the pathway information, with similar observed false discovery rates. We applied the proposed methods for analysis of a microarray time course gene expression study of TrkA- and TrkB-transfected neuroblastoma cell lines and identified genes and subnetworks on MAPK, focal adhesion, and prion disease pathways that may explain cell differentiation in TrkA-transfected cell lines.
Insights
This study introduces a new empirical Bayes method using Markov random fields to analyze gene expression data, improving the identification of differentially expressed genes and pathways. The approach enhances sensitivity in detecting biological signals in complex genomic datasets.
Area of Science:
- Genomics
- Bioinformatics
- Systems Biology
Background:
- Multivariate gene expression data are crucial for understanding genomic responses under ordered conditions.
- Identifying genes with differential expression patterns over time or dosage is key to uncovering biological processes.
- Existing empirical Bayes methods often assume gene independence, limiting their effectiveness.
Purpose of the Study:
- To develop an alternative empirical Bayes approach for multivariate gene expression data analysis.
- To model gene dependencies using a discrete Markov random field (MRF) prior.
- To improve the identification of differentially expressed genes and perturbed pathways.
Main Methods:
- Introduced a novel empirical Bayes approach incorporating a discrete Markov random field (MRF) prior.
- Modeled dependencies between differential expression patterns of genes within biological networks.
- Utilized simulation studies and a real-world microarray time course dataset.
Main Results:
- The proposed MRF-based method effectively identifies differentially expressed genes and subnetworks.
- Demonstrated higher sensitivity compared to methods that ignore pathway information.
- Achieved similar false discovery rates to existing procedures.
Conclusions:
- The MRF prior approach offers a powerful tool for analyzing multivariate gene expression data by leveraging gene network dependencies.
- The method successfully identified key genes and subnetworks in MAPK, focal adhesion, and prion disease pathways.
- These findings provide insights into cell differentiation mechanisms in neuroblastoma cell lines.
