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Co-expressing Patterns of Schizophrenia Candidate Genes in Brain Regions

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8261))

Abstract

Transcriptional profiling of human brain will be great helpful understanding the molecular mechanism of schizophrenia, which is a severe threaten to human health. In this study, top 250 schizophrenia candidate genes were selected, and then expression pattern of these 250 schizophrenia candidate genes were analyzed in six brain regions, including frontal lobe, temporal lobe,parietal lobe,basal ganglia,occipital lobe,and hippocamel by 2D hieratical clustering method. We also used stability analyzing to evaluate clustering methods, and pearson-pairwise correlation analysis was executed between each two different tissues in each sub-region. 2D hieratical clustering results indicated certain gene pathology have similar expression level in some brain sub-regions while different in some others.We found that local pattern of the sample clustering can reflects the cytoarchitecture of basal ganglia,hippocamel and occipital lobe,while in temporal lobe,parieatal lobe and frontal lobe the situation is less discriminable.As respect to the gene cluster pattern,we found from the GO term of each gene cluster that there are some genes have the similar co-expressing level across some brain regions,while in some other regions,the results we found is just the opposite that the co-expressing level are appears to be very different.Our experimental results strongly proved that local pattern of schizophrenia candidate genes were not only just simply reflecting sub-brain cytoarchitecture but also on functional coordination between each molecular components.And those co-expression pattern in all the brain regions,it may be helpful to understand the pathology mechanisms of schizophrenia related to each sub-brain.

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© 2013 Springer-Verlag Berlin Heidelberg

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Lu, X., Feng, B., Deng, Y., Hu, D. (2013). Co-expressing Patterns of Schizophrenia Candidate Genes in Brain Regions. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_52

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  • DOI: https://doi.org/10.1007/978-3-642-42057-3_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-42056-6

  • Online ISBN: 978-3-642-42057-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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