| dc.description.abstract | With more and more vigorous development of the field of bioinformatics, microarray chips have been widely used, our study is the use of microarray chips to do the following analysis, the data source is based on BioGPS, and it provides normalized processing chip data, these chips data have also been uploaded to the GEO belongs to NCBI, this database has a variety of chip experimental data, our goal is to identify human normal tissue specific genes, when we identify these genes, we can know each tissue or cell type which genes are mainly express, then we want to deeply understand the corresponding relationship between these human normal tissues, so we take these specific genes based on different conditions and next use hierarchical clustering analysis method to do cluster, and then we use several different methods of classification and try to explain these grouping results.
From our study, we using two different screening methods of tissue-specific genes; the first screening method to identify genes with large expression levels variation between different tissues and cell types, the second screening method we take a reference to the existing paper, identify each tissue and cell type their specific high expression genes, ,with more conditions restriction, it also has to meet the genes between different tissues and cell types highest expression level division the second high expression level ratio of greater than two times, from the results, we find the first screening method of tissue-specific genes hierarchical clustering would be more consistent in the four classification of developmental germinal layer concept,physiology, five zangs and six fus, and the twelve main meridians; after determination of the method, we further explore the use of tissue specific genes expression levels hierarchical clustering results are also more ideal than use of tissue specific genes on the chromosome’s distributions; at last, we compare Chinese medicine and Western medicine, Chinese medicine and Western medicine points of view to explain the hierarchical clustering results is nck and neck.
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