Application of microarray outlier detection methodology to psychiatric research.

Publication Type:

Journal Article

Authors:

Ernst, Carl; Alexandre Bureau; Turecki, Gustavo

Source:

BMC Psychiatry, Volume 8, p.29 (2008)

Keywords:

Algorithms, Brain, False Positive Reactions, Gene Expression Profiling, Humans, Mental Disorders, Oligonucleotide Array Sequence Analysis, Psychiatry, Single-Blind Method

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.