Bioinformatics Advance Access published online on September 16, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti029
Bioinformatics © Oxford University Press 2004; all rights reserved
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1 Dept. of Applied Statistics, Yonsei Univ., Seoul, S.Korea
* To whom correspondence should be addressed. E-mail: bskim{at}yonsei.ac.kr.
Motivation: It is a common practice in the cancer microarray experiment that a normal tissue is collected from the same individual from whom the tumor tissue was taken. The indirect design is usually adopted for the experiment which uses a common reference RNA hybridized both to normal and tumor tissues. However, it is often the case that the test material is not large enough for the experimenter to extract enough RNA to conduct the microarray experiment. Hence, collecting n cases does not necessarily end up with a matched pair sample of size n. Instead we usually have a matched pair sample of size n1, and two independent samples of sizes n2 and n3, respectively for "reference versus normal tissue only" and "reference versus tumor tissue only" hybridizations (n=n1+n2+n3). Standard statistical methods need to be modified and new statistical procedures are developed for analyzing this mixed data set. Results: We propose a new test statistics, t3, as a means of combining all the information in the mixed data set for detecting differentially expressed (DE) genes between normal and tumor tissues. We employed the extended ROC approach to the mixed data set. We devised a measure of disagreement between a RT-PCR experiment and a microarray experiment. Hotelling's T2 statistic is employed to detect a set of DE genes and its prediction rate is compared against the prediction rate of a univariate procedure. We observe that Hotelling's T2 statistic detects DE genes more efficiently than a univariate procedure and further research is warranted on the formal test procedure using Hotelling's T2 statistic.
Revised August 28, 2004
Accepted September 12, 2004
Article
Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer
2 Cancer Metastasis Research Center, College of Medicine, Yonsei. Univ., Seoul, S.Korea
3 Dept. of Applied Mathematics, Sejong Univ., Seoul, S.Korea
4 Cancer Metastasis Research Center, College of Medicine, Yonsei. Univ., Seoul, S.Korea; Brain Korea 21 Project for Medical Science, College of Medicine, Yonsei Univ., Seoul, S.Korea
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