Application of microarray outlier detection methodology to psychiatric research

Carl Ernst, Alexandre Bureau, Gustavo Turecki

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number29
JournalBMC psychiatry
StatePublished - Apr 23 2008
Externally publishedYes

ASJC Scopus subject areas

  • Psychiatry and Mental health


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