Application of microarray outlier detection methodology to psychiatric research

Carl Ernst¹ , Alexandre Bureau² and Gustavo Turecki¹^,3

¹McGill Group for Suicide Studies, McGill University, Montreal, Canada

²Centre de recherche Université Laval Robert-Giffard and Department of social and preventive medicine, Université Laval, Canada

³Douglas Hospital Research Centre, Pavilion Frank B Common, Rm. F-3125, 6875 LaSalle, Blvd., Verdun, Montreal, Quebec, H4H 1R3, Canada

author email corresponding author email

BMC Psychiatry 2008, 8:29doi:10.1186/1471-244X-8-29


Published:	23 April 2008

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.

Conclusion

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