Application of microarray outlier detection methodology to psychiatric research.

Publication Type:

Journal Article


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


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


<p><b>BACKGROUND: </b>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.</p><p><b>METHODS: </b>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.</p><p><b>RESULTS: </b>This method targets high-throughput technology to psychiatric research on a subject-specific basis.</p><p><b>CONCLUSION: </b>Assessing microarray data for both mean group effects and individual effects can lead to more robust findings in psychiatric genetics.</p>

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