title: Mixture-model based estimation of gene expression variance from public database improves identification of differentially expressed genes in small sized microarray data.
abstract: BACKGROUND
  The small number of samples in many microarray experiments is a challenge for the correct identification of differentially expressed gens (DEGs) by conventional statistical means. Information from public microarray databases can help more efficient identification of DEGs. To model various experimental conditions of a public microarray database, we applied Gaussian mixture model and extracted bi- or tri-modal distributions of gene expression. Prior variance of Baldi's Bayesian framework was estimate for the analysis of the small sample-sized datasets.
  
  
  RESULTS
  First, we estimated the prior variance of a gene expression by pooling variances obtained from mixture modeling of large samples in the public microarray database. Then, using the prior variance, we identified DEGs in small sample-sized test datasets using the Baldi's framework. For benchmark study, we generated test datasets having several samples from relatively large datasets. Our proposed method outperformed other benchmark methods in terms of detecting gold-standard DEGs from the test datasets. The results may be a challenging evidence for usage of public microarray databases in microarray data analysis.
authors.affiliation: Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 110-799, Korea.
authors.forename: Mingoo
authors.initials: M
authors.lastname: Kim
authors.forename: Sung Bum
authors.initials: SB
authors.lastname: Cho
authors.forename: Ju Han
authors.initials: JH
authors.lastname: Kim
mesh_terms: D001499:Bayes Theorem; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods