We are analyzing https://link.springer.com/article/10.1007/s10048-006-0032-6.

Title:
Gene expression analyses reveal molecular relationships among 20 regions of the human CNS | Neurogenetics
Description:
Transcriptional profiling was performed to survey the global expression patterns of 20 anatomically distinct sites of the human central nervous system (CNS). Forty-five non-CNS tissues were also profiled to allow for comparative analyses. Using principal component analysis and hierarchical clustering, we were able to show that the expression patterns of the 20 CNS sites profiled were significantly different from all non-CNS tissues and were also similar to one another, indicating an underlying common expression signature. By focusing our analyses on the 20 sites of the CNS, we were able to show that these 20 sites could be segregated into discrete groups with underlying similarities in anatomical structure and, in many cases, functional activity. These findings suggest that gene expression data can help define CNS function at the molecular level. We have identified subsets of genes with the following patterns of expression: (1) across the CNS, suggesting homeostatic/housekeeping function; (2) in subsets of functionally related sites of the CNS identified by our unsupervised learning analyses; and (3) in single sites within the CNS, indicating their participation in distinct site-specific functions. By performing network analyses on these gene sets, we identified many pathways that are upregulated in particular sites of the CNS, some of which were previously described in the literature, validating both our dataset and approach. In summary, we have generated a database of gene expression that can be used to gain valuable insight into the molecular characterization of functions carried out by different sites of the human CNS.
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         description:Transcriptional profiling was performed to survey the global expression patterns of 20 anatomically distinct sites of the human central nervous system (CNS). Forty-five non-CNS tissues were also profiled to allow for comparative analyses. Using principal component analysis and hierarchical clustering, we were able to show that the expression patterns of the 20 CNS sites profiled were significantly different from all non-CNS tissues and were also similar to one another, indicating an underlying common expression signature. By focusing our analyses on the 20 sites of the CNS, we were able to show that these 20 sites could be segregated into discrete groups with underlying similarities in anatomical structure and, in many cases, functional activity. These findings suggest that gene expression data can help define CNS function at the molecular level. We have identified subsets of genes with the following patterns of expression: (1) across the CNS, suggesting homeostatic/housekeeping function; (2) in subsets of functionally related sites of the CNS identified by our unsupervised learning analyses; and (3) in single sites within the CNS, indicating their participation in distinct site-specific functions. By performing network analyses on these gene sets, we identified many pathways that are upregulated in particular sites of the CNS, some of which were previously described in the literature, validating both our dataset and approach. In summary, we have generated a database of gene expression that can be used to gain valuable insight into the molecular characterization of functions carried out by different sites of the human CNS.
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