Summary
DNA microarray profiles are plagued by the issue of large number of variables but small number of samples and are often notorious for their low signal-to-noise ratio for clinical applications. Therefore, a great need for meta-analysis techniques is emerging to yield more valid and informative results than each experiment separately. By exploring the power of several studies in one single analysis, meta-analysis of many cancer gene-profiling data increases the statistical power to detect differentially expressed genes and allows assessment of heterogeneity. OrderedList is such a method that was specially proposed for cancer gene expression data meta-analysis. It is superior to other methods in that it does not rely on strong effects of differential gene expression in a single study but on consistent regulated genes across multiple studies. This chapter introduces the R implementation of this methodology on real data sets to identify biomarkers for adenocarcinoma lung cancer.
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Acknowledgments
The author thanks Dr. Lottaz C. for helpful advice and Zhang Q.Q. for carefully proof reading the example R codes. This work was supported by the Natural Science Foundation, 60671018, 60771024.
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© 2009 Humana Press, a part of Springer Science+Business Media, LLC
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Yang, X., Sun, X. (2009). Meta-analysis of Cancer Gene-Profiling Data. In: Grützmann, R., Pilarsky, C. (eds) Cancer Gene Profiling. Methods in Molecular Biology, vol 576. Humana Press. https://doi.org/10.1007/978-1-59745-545-9_21
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DOI: https://doi.org/10.1007/978-1-59745-545-9_21
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