Finding biomarker signatures in pooled sample designs: A simulation framework for methodological comparisons

Advances in Bioinformatics

Volumn 2010, Issue , 2010, Pages

  Repsilber, Dirk ^a   Telaar, Anna ^a   Nürnberg, Gerd ^a  

^a LEIBNIZ INSTITUTE FOR FARM ANIMAL BIOLOGY FBN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 78349290410     PISSN: 16878027     EISSN: 16878035     Source Type: Journal    
DOI: 10.1155/2010/318573     Document Type: Article

References (27)

1. Biomarkers Definitions Workgroup, Biomarkers and surrogate endpoints: preferred definitions and conceptual framework Clinical Pharmacology and Therapeutics 2001 69 3 89 95
2. Simon R., Radmacher M. D., Dobbin K., McShane L. M., Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification Journal of the National Cancer Institute 2003 95 1 14 18 (Pubitemid 36124291)
3. Golub T. R., Slonim D. K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J. P., Coller H., Loh M. L., Downing J. R., Caligiuri M. A., Bloomfield C. D., Lander E. S., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring Science 1999 286 5439 531 537
4. Feng Z., Prentice R., Srivastava S., Research issues and strategies for genomic and proteomic biomarker discovery and validation: a statistical perspective Pharmacogenomics 2004 5 6 709 719
5. Kapetanovic I. M., Rosenfeld S., Izmirlian G., Overview of commonly used bioinformatics methods and their applications Annals of the New York Academy of Sciences 2004 1020 10 21
6. Eriksson L., Johansson E., Kettaneh-Wold N., Wold S., Introduction to Multi- and Megavariate Data Analysis Using Projection Methods (PCA PLS) 1999 Ume, Sweden Umetrics AB
7. Hendriks M. M. W. B., Smit S., Akkermans W. L. M. W., Reijmers T. H., Eilers P. H. C., Hoefsloot H. C. J., Rubingh C. M., De Koster C. G., Aerts J. M., Smilde A. K., How to distinguish healthy from diseased? Classification strategy for mass spectrometry-based clinical proteomics Proteomics 2007 7 20 3672 3680
8. Wagner M., Naik D. N., Pothen A., Kasukurti S., Devineni R. R., Adam B.-L., Semmes O. J., Wright G. L. Jr., Computational protein biomarker prediction: a case study for prostate cancer BMC Bioinformatics 2004 5, article 26
9. Van't Veer L. J., Dai H., Van de Vijver M. J., He Y. D., Hart A. A.M., Mao M., Peterse H. L., Van Der Kooy K., Marton M. J., Witteveen A. T., Schreiber G. J., Kerkhoven R. M., Roberts C., Linsley P. S., Bernards R., Friend S. H., Gene expression profiling predicts clinical outcome of breast cancer Nature 2002 415 6871 530 536
10. Van De Vijver M. J., He Y. D., van 't Veer L. J., Dai H., Hart A. A.M., Voskuil D. W., Schreiber G. J., Peterse J. L., Roberts C., Marton M. J., Parrish M., Atsma D., Witteveen A., Glas A., Delahaye L., Van Der Velde T., Bartelink H., Rodenhuis S., Rutgers E. T., Friend S. H., Bernards R., A gene-expression signature as a predictor of survival in breast cancer New England Journal of Medicine 2002 347 25 1999 2009 (Pubitemid 35461656)
11. Michiels S., Koscielny S., Hill C., Prediction of cancer outcome with microarrays: a multiple random validation strategy Lancet 2005 365 9458 488 492 (Pubitemid 40215731)
12. AFFymetrix, Sample Pooling for Microarray Analysis: A Statistical Assessment of Risks and Biases 2004 Technical Note, no. 701494 Rev. 2
13. Kendziorski C. M., Zhang Y., Lan H., Attie A. D., The efficiency of pooling mRNA in microarray experiments Biostatistics 2003 4 3 465 477
14. Kendziorski C., Irizarry R. A., Chen K.-S., Haag J. D., Gould M. N., On the utility of pooling biological samples in microarray experiments Proceedings of the National Academy of Sciences of the United States of America 2005 102 12 4252 4257
15. Shih J. H., Michalowska A. M., Dobbin K., Ye Y., Qiu T. H., Green J. E., Effects of pooling mRNA in microarray class comparisons Bioinformatics 2004 20 18 3318 3325
16. Sadiq S. T., Agranoff D., Pooling serum samples may lead to loss of potential biomarkers in SELDI-ToF MS proteomic profiling Proteome Science 2008 6, article 16
17. Kerr M. K., Design considerations for efficient and effective microarray studies Biometrics 2003 59 4 822 828
18. Allison D. B., Cui X., Page G. P., Sabripour M., Microarray data analysis: from disarray to consolidation and consensus Nature Reviews Genetics 2006 7 1 55 65
19. Vapnik V. N., The Nature of Statistical Learning Theory 1998 New York, NY, USA Springer
20. Dimitriadou E., Hornik K., Leisch F., Meyer D., Weingessel A.,. e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. R package version 1.5-20, 2009
21. Breiman L., Random forests Machine Learning 2001 45 5 32
22. Indahl U. G., Liland K. H., Ns T., Canonical partial least squares-a unified PLS approach to classification and regression problems Journal of Chemometrics 2009 23 9 495 504
23. Efron B., Tibshirani R., An Introduction to the Bootstrap 1993 London, UK Chapman and Hall
24. Dudoit S., Fridlyand J., Speed T. P., Comparison of discrimination methods for the classification of tumors using gene expression data Journal of the American Statistical Association 2002 97 457 77 87
25. Parrish R. S., Spencer H. J. III, Xu P., Distribution modeling and simulation of gene expression data Computational Statistics and Data Analysis 2009 53 5 1650 1660
26. Zhang W., Carriquiry A., Nettleton D., Dekkers J. C. M., Pooling mRNA in microarray experiments and its effect on power Bioinformatics 2007 23 10 1217 1224
27. Quackenbush J., Microarray data normalization and transformation Nature Genetics 2002 32 supplement 496 501