title: Matching methods for observational microarray studies.
abstract: BACKGROUND
  We address the problem of identifying differentially expressed genes between two conditions in the scenario where the data arise from an observational study, in which confounding factors are likely to be present.
  
  
  RESULTS
  We suggest to use matching methods to balance two groups of observed cases on measured covariates, and to identify differentially expressed genes using a test suited to matched data. We illustrate this approach on two microarray studies: the first study consists of data from patients with two cancer subtypes, and the second study consists of data from AMKL patients with and without Down syndrome.
  
  
  BACKGROUND
  R code (www.r-project.org) for implementing our approach is included as Supplementary Material.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia, PA 19104-6340, USA. ruheller@whatron.upenn.edu
authors.forename: Ruth
authors.initials: R
authors.lastname: Heller
authors.forename: Elisabetta
authors.initials: E
authors.lastname: Manduchi
authors.forename: Dylan S
authors.initials: DS
authors.lastname: Small
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004314:Down Syndrome / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000235:genetics; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods