title: Network-based analysis of multivariate gene expression data.
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.
authors: Department of Biostatistics and Epidemiology, New Jersey Institute of Technology, Newark, NJ, USA.
authors: Wei
authors: W
authors: Zhi
authors: Jane
authors: J
authors: Minturn
authors: Eric
authors: E
authors: Rappaport
authors: Garrett
authors: G
authors: Brodeur
authors: Hongzhe
authors: H
authors: Li
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002454:Cell Differentiation; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D020935:MAP Kinase Signaling System; D008390:Markov Chains; D015233:Models, Statistical; D015999:Multivariate Analysis; D009447:Neuroblastoma / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D020917:Receptor, trkA / Q000235:genetics / Q000378:metabolism; D020813:Receptor, trkB / Q000235:genetics / Q000378:metabolism; D012680:Sensitivity and Specificity; D059467:Transcriptome
chemical_list: D020917:Receptor, trkA; D020813:Receptor, trkB
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