Ding, ShanshanAdvancement in next-generation sequencing, transcriptomics, proteomics andother high-throughput technologies has enabled to simultaneously measure multipletypes of genomic data for cancer samples. These data may reveal new biological in-sights as compared to analyzing one single genome type. This study proposes a newintegrative supervised dimension reduction method, called integrative sliced inverseregression (integrative SIR), for simultaneous analysis of multiple omics data types ofcancer samples, including MiRNA, MRNA and proteomics, to improve prediction andinterpretation. The proposed method can reduce the dimensions of multiple omics datasimultaneously while sharing common latent structures without losing any informationin prediction. By capturing common information across data types, the new methoddemonstrates advantages over conventional methods. In this work, we classify differenttumor types like CNS, leukemia and melanoma using dimension reduction methods.M.S.University of Delaware, Program in Bioinformatics and Computational Biology
