Cancer genome projects can provide different types of data on the genetic level, which is significant for cancer research and biological processes in computational methods. Thus, computational methods used to identify cancer subtypes should fully focus on integrating these multidimensional data (e.g., DNA methylation data, mRNA expression data, etc.). Sparse reduced-rank regression (Srrr) method, a state-of-the-art multiple response linear regression method, can easily deal with high dimensional statistical data. In this paper, we introduced Srrr method combining iCluster (Srrr-cluster) to discovery cancer subtypes. Firstly, we used Srrr to estimate the coefficient matrix and then cancer subtypes were clustered by iCluster. Finally, we used our Srrr-cluster method to analyze glioblastoma and breast cancer data. The results show that our Srrr-cluster method is effective for cancer subtype identification.
['Shu-Guang Ge', 'Junfeng Xia', 'Pi-Jing Wei', 'Chun-Hou Zheng']
Srrr-cluster: Using Sparse Reduced-Rank Regression to Optimize iCluster