title: Multi-level mixed effects models for bead arrays.
abstract: BACKGROUND
  Bead arrays are becoming a popular platform for high-throughput expression arrays. However, the number of the beads targeting a transcript and the variation of their intensities differ from sample to sample in these arrays. This property results in different accuracy of expression intensities of a transcript across arrays.
  
  
  RESULTS
  We provide evidence, with publicly available spike-in data, that the false discovery rate of differential expression is reduced by modeling bead-level variability with a multi-level mixed effects model. We compare the performance of our proposed model to existing analysis methods for bead arrays: the unweighted t-test and other weighted methods. Additionally, we provide theoretical insights into when the multi-level mixed effects model outperforms other methods. Finally, we provide a software program for differential expression analysis using the multi-level mixed effects model that analyzes tens of thousands of genes efficiently.
  
  
  BACKGROUND
  The software program is freely available on web at http://ephpublic.aecom.yu.edu/sites/rkim/Supplementary.
authors.affiliation: Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA. ryung.kim@einstein.yu.edu
authors.forename: Ryung S
authors.initials: RS
authors.lastname: Kim
authors.forename: Juan
authors.initials: J
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D000818:Animals; D051379:Mice; D008863:Microspheres; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012680:Sensitivity and Specificity; D012984:Software