Based On Statistical Analysis Of Microarray Data Mining Technology

Microarray technique is already extensively applied in the biology and medical sciences, it has become a kind of important experiment mean in biology research .The invention of microarrays allows us to study simultaneously variations of genes at the genome-wide scale. A typical gene expression data set consists of thousands or even tens of thousands of genes, and a few dozen experiments. the correspond data analysis methods are also being quickly developed. Currently, various data mining methods are used to mine the underlying gene expression modes, which may introduce reasonable interpretations in identifying groups of genes or samples. In this paper, three statistical methods are proposed for microarray data analysis.Cluster analysis is the art of finding groups in given data sets such that objects in the same group are similar to each other while objects in different groups are dissimilar. There are many applications for clustering gene expression data, but extensive quantitative evaluations of cluster results are rare. Since different analytical approaches may produce different cluster results, there is a great need to evaluate clustering techniques in order to choose an appropriate approach. In this paper various distance measures and various clustering validation indexes are carefully studied for microarray data analysis. The results show that the correlation coefficient is better to measure the distance of gene expression profile than the others, and that several measure indexes are fit to evaluate clustering validation.Principal Component Analysis (PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. It has become a useful tool in microarray...