title: Application of microarray outlier detection methodology to psychiatric research.
abstract: BACKGROUND
  Most microarray data processing methods negate extreme expression values or alter them so that they do not lie outside the mean level of variation of the system. While microarrays generate a substantial amount of false positive and spurious results, some of the extreme expression values may be valid and could represent true biological findings.
  
  
  METHODS
  We propose a simple method to screen brain microarray data to detect individual differences across a psychiatric sample set. We demonstrate in two different samples how this method can be applied.
  
  
  RESULTS
  This method targets high-throughput technology to psychiatric research on a subject-specific basis.
  
  
  CONCLUSIONS
  Assessing microarray data for both mean group effects and individual effects can lead to more robust findings in psychiatric genetics.
authors.affiliation: McGill Group for Suicide Studies, McGill University, Montreal, Canada. carl.ernst@mail.mcgill.ca
authors.forename: Carl
authors.initials: C
authors.lastname: Ernst
authors.forename: Alexandre
authors.initials: A
authors.lastname: Bureau
authors.forename: Gustavo
authors.initials: G
authors.lastname: Turecki
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000503:physiopathology; D005189:False Positive Reactions; D020869:Gene Expression Profiling; D006801:Humans; D001523:Mental Disorders / Q000235:genetics* / Q000503:physiopathology; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011570:Psychiatry / Q000379:methods*; D016037:Single-Blind Method