Advances in Bioinformatics
Volume 2010 (2010), Article ID 318573, 8 pages
http://dx.doi.org/10.1155/2010/318573
Research Article
Finding Biomarker Signatures in Pooled Sample Designs: A Simulation Framework for Methodological Comparisons
Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Wilhelm-Stahl-Allee 2, D-18196 Dummerstorf, Germany
Received 14 November 2009; Accepted 5 May 2010
Academic Editor: Jeremy Buhler
Copyright © 2010 Anna Telaar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Linked References
- Biomarkers Definitions Workgroup, “Biomarkers and surrogate endpoints: preferred definitions and conceptual framework,” Clinical Pharmacology and Therapeutics, vol. 69, no. 3, pp. 89–95, 2001. View at Publisher · View at Google Scholar · View at Scopus
- R. Simon, M. D. Radmacher, K. Dobbin, and L. M. McShane, “Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification,” Journal of the National Cancer Institute, vol. 95, no. 1, pp. 14–18, 2003. View at Google Scholar · View at Scopus
- T. R. Golub, D. K. Slonim, P. Tamayo et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” Science, vol. 286, no. 5439, pp. 531–537, 1999. View at Publisher · View at Google Scholar · View at Scopus
- Z. Feng, R. Prentice, and S. Srivastava, “Research issues and strategies for genomic and proteomic biomarker discovery and validation: a statistical perspective,” Pharmacogenomics, vol. 5, no. 6, pp. 709–719, 2004. View at Publisher · View at Google Scholar · View at Scopus
- I. M. Kapetanovic, S. Rosenfeld, and G. Izmirlian, “Overview of commonly used bioinformatics methods and their applications,” Annals of the New York Academy of Sciences, vol. 1020, pp. 10–21, 2004. View at Publisher · View at Google Scholar · View at Scopus
- L. Eriksson, E. Johansson, N. Kettaneh-Wold, and S. Wold, Introduction to Multi- and Megavariate Data Analysis Using Projection Methods (PCA & PLS), Umetrics AB, Umeå, Sweden, 1999.
- M. M. W. B. Hendriks, S. Smit, W. L. M. W. Akkermans et al., “How to distinguish healthy from diseased? Classification strategy for mass spectrometry-based clinical proteomics,” Proteomics, vol. 7, no. 20, pp. 3672–3680, 2007. View at Publisher · View at Google Scholar · View at Scopus
- M. Wagner, D. N. Naik, A. Pothen et al., “Computational protein biomarker prediction: a case study for prostate cancer,” BMC Bioinformatics, vol. 5, article 26, 2004. View at Publisher · View at Google Scholar · View at Scopus
- L. J. Van't Veer, H. Dai, M. J. Van de Vijver et al., “Gene expression profiling predicts clinical outcome of breast cancer,” Nature, vol. 415, no. 6871, pp. 530–536, 2002. View at Publisher · View at Google Scholar · View at Scopus
- M. J. Van De Vijver, Y. D. He, L. J. van 't Veer et al., “A gene-expression signature as a predictor of survival in breast cancer,” New England Journal of Medicine, vol. 347, no. 25, pp. 1999–2009, 2002. View at Publisher · View at Google Scholar · View at Scopus
- S. Michiels, S. Koscielny, and C. Hill, “Prediction of cancer outcome with microarrays: a multiple random validation strategy,” Lancet, vol. 365, no. 9458, pp. 488–492, 2005. View at Publisher · View at Google Scholar · View at Scopus
- AFFymetrix, Sample Pooling for Microarray Analysis: A Statistical Assessment of Risks and Biases, Technical Note, no. 701494 Rev. 2, 2004.
- C. M. Kendziorski, Y. Zhang, H. Lan, and A. D. Attie, “The efficiency of pooling mRNA in microarray experiments,” Biostatistics, vol. 4, no. 3, pp. 465–477, 2003. View at Google Scholar · View at Scopus
- C. Kendziorski, R. A. Irizarry, K.-S. Chen, J. D. Haag, and M. N. Gould, “On the utility of pooling biological samples in microarray experiments,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 12, pp. 4252–4257, 2005. View at Publisher · View at Google Scholar · View at Scopus
- J. H. Shih, A. M. Michalowska, K. Dobbin, Y. Ye, T. H. Qiu, and J. E. Green, “Effects of pooling mRNA in microarray class comparisons,” Bioinformatics, vol. 20, no. 18, pp. 3318–3325, 2004. View at Publisher · View at Google Scholar · View at Scopus
- S. T. Sadiq and D. Agranoff, “Pooling serum samples may lead to loss of potential biomarkers in SELDI-ToF MS proteomic profiling,” Proteome Science, vol. 6, article 16, 2008. View at Publisher · View at Google Scholar · View at Scopus
- M. K. Kerr, “Design considerations for efficient and effective microarray studies,” Biometrics, vol. 59, no. 4, pp. 822–828, 2003. View at Publisher · View at Google Scholar · View at Scopus
- D. B. Allison, X. Cui, G. P. Page, and M. Sabripour, “Microarray data analysis: from disarray to consolidation and consensus,” Nature Reviews Genetics, vol. 7, no. 1, pp. 55–65, 2006. View at Publisher · View at Google Scholar · View at Scopus
- V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1998.
- E. Dimitriadou, K. Hornik, F. Leisch, D. Meyer, and A. Weingessel, e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. R package version 1.5-20, 2009.
- L. Breiman, “Random forests,” Machine Learning, vol. 45, pp. 5–32, 2001. View at Google Scholar
- U. G. Indahl, K. H. Liland, and T. Næs, “Canonical partial least squares-a unified PLS approach to classification and regression problems,” Journal of Chemometrics, vol. 23, no. 9, pp. 495–504, 2009. View at Publisher · View at Google Scholar · View at Scopus
- B. Efron and R. Tibshirani, An Introduction to the Bootstrap, Chapman and Hall, London, UK, 1993.
- S. Dudoit, J. Fridlyand, and T. P. Speed, “Comparison of discrimination methods for the classification of tumors using gene expression data,” Journal of the American Statistical Association, vol. 97, no. 457, pp. 77–87, 2002. View at Publisher · View at Google Scholar · View at Scopus
- R. S. Parrish, H. J. Spencer III, and P. Xu, “Distribution modeling and simulation of gene expression data,” Computational Statistics and Data Analysis, vol. 53, no. 5, pp. 1650–1660, 2009. View at Publisher · View at Google Scholar · View at Scopus
- W. Zhang, A. Carriquiry, D. Nettleton, and J. C. M. Dekkers, “Pooling mRNA in microarray experiments and its effect on power,” Bioinformatics, vol. 23, no. 10, pp. 1217–1224, 2007. View at Publisher · View at Google Scholar · View at Scopus
- J. Quackenbush, “Microarray data normalization and transformation,” Nature Genetics, vol. 32, supplement, pp. 496–501, 2002. View at Google Scholar