title: Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior.
abstract: BACKGROUND
  To utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources. The objective of this study is to develop a novel meta-analysis approach, Consistent Differential Expression Pattern (CDEP), to identify genes with common differential expression patterns across different datasets.
  
  
  RESULTS
  We combined False Discovery Rate (FDR) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis. These Type I error rates from all datasets were then used to identify genes with common differential expression patterns. Our simulation study showed that CDEP achieved higher statistical power and maintained low Type I error rate when compared with two recently proposed meta-analysis approaches. We applied CDEP to analyze microarray data from different laboratories that compared transcription profiles between metastatic and primary cancer of different types. Many genes identified as differentially expressed consistently across different cancer types are in pathways related to metastatic behavior, such as ECM-receptor interaction, focal adhesion, and blood vessel development. We also identified novel genes such as AMIGO2, Gem, and CXCL11 that have not been shown to associate with, but may play roles in, metastasis.
  
  
  CONCLUSIONS
  CDEP is a flexible approach that borrows information from each dataset in a meta-analysis in order to identify genes being differentially expressed consistently. We have shown that CDEP can gain higher statistical power than other existing approaches under a variety of settings considered in the simulation study, suggesting its robustness and insensitivity to data variation commonly associated with microarray experiments.
  
  
  BACKGROUND
  CDEP is implemented in R and freely available at: http://genomebioinfo.musc.edu/CDEP/.
  
  
  BACKGROUND
  zhengw@musc.edu.
authors.affiliation: Department of Biochemistry and Molecular Biology, Medical University of South Carolina, 135 Cannon St, Charleston, SC 29425, USA.
authors.forename: Lam C
authors.initials: LC
authors.lastname: Tsoi
authors.forename: Tingting
authors.initials: T
authors.lastname: Qin
authors.forename: Elizabeth H
authors.initials: EH
authors.lastname: Slate
authors.forename: W Jim
authors.initials: WJ
authors.lastname: Zheng
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D015201:Meta-Analysis as Topic; D009362:Neoplasm Metastasis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods
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