title: Integrated analysis of the heterogeneous microarray data.
abstract: BACKGROUND
  As the magnitude of the experiment increases, it is common to combine various types of microarrays such as paired and non-paired microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for heterogeneity among data sets. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. We propose the linear mixed effect model for the integrated analysis of the heterogeneous microarray data sets.
  
  
  RESULTS
  The proposed linear mixed effect model was illustrated using the data from 133 microarrays collected at three different hospitals. Though simulation studies, we compared the proposed linear mixed effect model approach with the meta-analysis and the ANOVA model approaches. The linear mixed effect model approach was shown to provide higher powers than the other approaches.
  
  
  CONCLUSIONS
  The linear mixed effect model has advantages of allowing for various types of covariance structures over ANOVA model. Further, it can handle easily the correlated microarray data such as paired microarray data and repeated microarray data from the same subject.
authors: Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
authors: Sung Gon
authors: SG
authors: Yi
authors: Taesung
authors: T
authors: Park
mesh_terms: D000704:Analysis of Variance; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D016014:Linear Models; D008113:Liver Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis