title: A mixture model approach to the tests of concordance and discordance between two large-scale experiments with two-sample groups.
abstract: BACKGROUND
  Due to advances in experimental technologies, such as microarray, mass spectrometry and nuclear magnetic resonance, it is feasible to obtain large-scale data sets, in which measurements for a large number of features can be simultaneously collected. However, the sample sizes of these data sets are usually small due to their relatively high costs, which leads to the issue of concordance among different data sets collected for the same study: features should have consistent behavior in different data sets. There is a lack of rigorous statistical methods for evaluating this concordance or discordance.
  
  
  METHODS
  Based on a three-component normal-mixture model, we propose two likelihood ratio tests for evaluating the concordance and discordance between two large-scale data sets with two sample groups. The parameter estimation is achieved through the expectation-maximization (E-M) algorithm. A normal-distribution-quantile-based method is used for data transformation.
  
  
  RESULTS
  To evaluate the proposed tests, we conducted some simulation studies, which suggested their satisfactory performances. As applications, the proposed tests were applied to three SELDI-MS data sets with replicates. One data set has replicates from different platforms and the other two have replicates from the same platform. We found that data generated by SELDI-MS showed satisfactory concordance between replicates from the same platform but unsatisfactory concordance between replicates from different platforms.
  
  
  BACKGROUND
  The R codes are freely available at http://home.gwu.edu/~ylai/research/Concordance.
authors: Department of Statistics and Biostatistics Center, The George Washington University, 2140 Pennsylvania Avenue, N.W. Washington, DC 20052, USA. ylai@gwu.edu
authors: Yinglei
authors: Y
authors: Lai
authors: Bao-ling
authors: BL
authors: Adam
authors: Robert
authors: R
authors: Podolsky
authors: Jin-Xiong
authors: JX
authors: She
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D005260:Female; D006801:Humans; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D010051:Ovarian Neoplasms / Q000235:genetics; D011471:Prostatic Neoplasms / Q000235:genetics; D040901:Proteomics; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization