Gaussian Mixture Model-based Clustering Analysis For Gene Microarray Expression Data

Along with the research and extensive applications of DNA biochip technology, gene expression data analysis have become a hotspot in life science field.Many classical clustering methods have been widely studied by researchers at home and abroad. However, Gaussian mixture model-based clustering has rarely been involved. As we know, DNA microarray technology is a very useful tool which is contributed to the pattern of genes expressed in a cell. The main challenge now is how to analyze the resulting large amounts of gene-expression data. As mentioned above, Clustering techniques have been widely applied in analyzing microarray gene expression data. Here we use Gaussian mixture model-based clustering to analyze gene-expression data. We also introduce permutation test and conservative posterior probability adjust strategy to improve the performance of this method. Our results indicate that the method is a useful statistical tool to exploit the clustering structure of the microarray gene expression data. The method is applied to a data set containing expression levels of 1176 genes of rats with and without pneumococcal middle-ear infection.