Application of microarray outlier detection methodology to psychiatric research

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.