Oncogenes and Subtypes of Diffuse Large B-Cell Lymphoma Discoveries from Microarray Database

This paper presents an effective analysis scheme for Diffuse Large B-Cell Lymphoma (DLBCL) microarray datasets. Analysis of variable (ANOVA) is a well known statistics tools. It is useful to get the oncogenes to distinguish the normal and cancerous tissues. But, it can not further obtain the sub-types of cancerous tissues effectively. Hierarchical clustering is a well known analysis method for data mining. Therefore, it is also useful and fit to classify oncogenes to obtain some sub-types. ANOVA and hierarchical clustering both are employed to help us analyze B-cell Lymphoma datasets. In our analysis results, ANOVA can obtain 11 oncogenes of DLBCL from Stanford DLBCL microarray database successfully and accurately. Then, the 11 oncogenes are used for hierarchical clustering to identify the subtypes of cancerous tissues. In our hierarchical clustering analysis, we use 20 GC B-like DLBCL and 15 Activated B-like DLBCL actual samples used for analyzing. The analysis result shows that the hierarchical clustering can distinguish GC B-like DLBCL and Activated B-like DLBCL samples successfully.