Application of microarray outlier detection methodology to psychiatric research

BMC Psychiatry. 2008 Apr 23;8:29. doi: 10.1186/1471-244X-8-29.


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.

Conclusion: Assessing microarray data for both mean group effects and individual effects can lead to more robust findings in psychiatric genetics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Brain / anatomy & histology
  • Brain / physiopathology
  • False Positive Reactions
  • Gene Expression Profiling
  • Humans
  • Mental Disorders / genetics*
  • Mental Disorders / physiopathology
  • Oligonucleotide Array Sequence Analysis / methods*
  • Psychiatry / methods*
  • Single-Blind Method