Identification of targeted analyte clusters for studies of schizophrenia.

The search for biomarkers to diagnose psychiatric disorders such as schizophrenia has been underway for decades. Many molecular profiling studies in this field have focused on identifying individual marker signals that show significant differences in expression between patients and the normal population. However, signals for multiple analyte combinations that exhibit patterned behaviors have been less exploited. Here, we present a novel approach for identifying biomarkers of schizophrenia using expression of serum analytes from first onset, drug-naïve patients and normal controls. The strength of patterned signals was amplified by analyzing data in reproducing kernel spaces. This resulted in the identification of small sets of analytes referred to as targeted clusters that have discriminative power specifically for schizophrenia in both human and rat models. These clusters were associated with specific molecular signaling pathways and less strongly related to other neuropsychiatric disorders such as major depressive disorder and bipolar disorder. These results shed new light concerning how complex neuropsychiatric diseases behave at the pathway level and demonstrate the power of this approach in identification of disease-specific biomarkers and potential novel therapeutic strategies.

A microarray data analysis framework for postmortem tissues.

This paper will give a complete methodological approach to the processing of oligonucleotide microarray data from postmortem tissue, particularly brain matter. Attention will be drawn to each of the important stages in the process; specifically the quality control, gene expression value calculation, multiple hypothesis testing and correlation analyses. We shall initially discuss the theoretical foundations of each individual method and subsequently apply the ensemble to a sample data set to illustrate and visualise important points.