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Feature-based Gene Classification and Region Clustering using Gene Expression Grid Data in Mouse Hippocampal Region
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 1,  2016, pp.54-60
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.1.54
 Title & Authors
Feature-based Gene Classification and Region Clustering using Gene Expression Grid Data in Mouse Hippocampal Region
Kang, Mi-Sun; Kim, HyeRyun; Lee, Sukchan; Kim, Myoung-Hee;
Brain gene expression information is closely related to the structural and functional characteristics of the brain. Thus, extensive research has been carried out on the relationship between gene expression patterns and the brain's structural organization. In this study, Principal Component Analysis was used to extract features of gene expression patterns, and genes were automatically classified by spatial distribution. Voxels were then clustered with classified specific region expressed genes. Finally, we visualized the clustering results for mouse hippocampal region gene expression with the Allen Brain Atlas. This experiment allowed us to classify the region-specific gene expression of the mouse hippocampal region and provided visualization of clustering results and a brain atlas in an integrated manner. This study has the potential to allow neuroscientists to search for experimental groups of genes more quickly and design an effective test according to the new form of data. It is also expected that it will enable the discovery of a more specific sub-region beyond the current known anatomical regions of the brain.
gene expression;gene classification;unsupervised learning;voxel-based clustering;
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