Bioinformatics Advance Access originally published online on September 16, 2004
Bioinformatics 2005 21(4):517-528; doi:10.1093/bioinformatics/bti029
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Bioinformatics vol. 21 issue 4 © Oxford University Press 2005; all rights reserved.
Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer
1 Department of Applied Statistics, College of Medicine, Yonsei University Seoul, South Korea
2 Cancer Metastasis Research Center, College of Medicine, Yonsei University Seoul, South Korea
3 Brain Korea 21 Project for Medical Science, College of Medicine, Yonsei University Seoul, South Korea
4 Department of Applied Mathematics, Sejong University Seoul, South Korea
*To whom correspondence should be addressed.
Motivation: It is a common practice in cancer microarray experiments 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 that 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 ncases does not necessarily end up with a matched pair sample of size n. Instead we usually have a matched pair sample of size n 1, and two independent samples of sizes n 2 and n 3, respectively, for ‘reference versus normal tissue only’ and ‘reference versus tumor tissue only’ hybridizations (n = n 1 + n 2 + n 3). Standard statistical methods need to be modified and new statistical procedures are developed for analyzing this mixed dataset.
Results: We propose a new test statistic, t 3, as a means of combining all the information in the mixed dataset for detecting differentially expressed (DE) genes between normal and tumor tissues. We employed the extended receiver operating characteristic approach to the mixed dataset. We devised a measure of disagreement between a RT–PCR experiment and a microarray experiment. Hotelling's T 2 statistic is employed to detect a set of DE genes and its prediction rate is compared with the prediction rate of a univariate procedure. We observe that Hotelling's T 2 statistic detects DE genes more efficiently than a univariate procedure and that further research is warranted on the formal test procedure using Hotelling's T 2 statistic.
Contact: bskim{at}yonsei.ac.kr
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