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Statistical Method of Ranking Candidate Genes for the Biomarker

  • Kim, Byung-Soo (Department of Applied Statistics, Yonsei University) ;
  • Kim, In-Young (Department of Epidemiology and Public Health, School of Medicine, Yale University) ;
  • Lee, Sun-Ho (Department of Applied Mathematics, Sejong University) ;
  • Rha, Sun-Young (Cancer Metastasis Research Cancer, College of Medicine, Yonsei University)
  • Published : 2007.04.30

Abstract

Receive operating characteristic (ROC) approach can be employed to rank candidate genes from a microarray experiment, in particular, for the biomarker development with the purpose of population screening of a cancer. In the cancer microarray experiment based on n patients the researcher often wants to compare the tumor tissue with the normal tissue within the same individual using a common reference RNA. Ideally, this experiment produces n pairs of microarray data. However, it is often the case that there are missing values either in the normal or tumor tissue data. Practically, we have $n_1$ pairs of complete observations, $n_2$ "normal only" and $n_3$ "tumor only" data for the microarray. We refer to this data set as a mixed data set. We develop a ROC approach on the mixed data set to rank candidate genes for the biomarker development for the colorectal cancer screening. It turns out that the correlation between two ranks in terms of ROC and t statistics based on the top 50 genes of ROC rank is less than 0.6. This result indicates that employing a right approach of ranking candidate genes for the biomarker development is important for the allocation of resources.

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