Classification across gene expression microarray studies

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages

  Buness, Andreas ^a   Ruschhaupt, Markus ^a,b   Kuner, Ruprecht ^a   Tresch, Achim ^b  

^a GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION METHODS; CLASSIFICATION PERFORMANCE; EVALUATION FRAMEWORK; GENE EXPRESSION MICROARRAY; GENERALIZATION PERFORMANCE; MISCLASSIFICATION RATES; SYSTEMATIC EVALUATION;

CLASSIFICATION (OF INFORMATION); DATA INTEGRATION; DECISION TREES; DIAGNOSIS; DISEASES; GENE EXPRESSION; SUPPORT VECTOR MACHINES;

MICROARRAYS;

ARTICLE; BIOLOGY; BREAST TUMOR; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENETICS; METHODOLOGY;

BREAST NEOPLASMS; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 75449097597     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-453     Document Type: Article

References (28)

1. Chang HY, Nuyten DSA, Sneddon JB, Hastie T, Tibshirani R, Sorlie T, Dai H, He YD, van't Veer LJ, Bartelink H, Rijn M, Brown PO, Vijver MJ. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci USA 2005, 102(10):3738-43. 10.1073/pnas.0409462102, 548329,548329, 15701700.
2. Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, Pawitan Y, Hall P, Klaar S, Liu ET, Bergh J. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci USA 2005, 102(38):13550-5. 10.1073/pnas.0506230102, 1197273,1197273, 16141321.
3. van't Veer LJ, Dai H, Vijver MJ, He YD, Hart AAM, Mao M, Peterse HL, Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415(6871):530-536. 10.1038/415530a, 11823860.
4. Wang Y, Klijn JGM, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, van Gelder MEM, Yu J, Jatkoe T, Berns EMJJ, Atkins D, Foekens JA. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005, 365(9460):671-9.
5. Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 2005, 365(9458):488-92. 10.1016/S0140-6736(05)17866-0, 15705458.
6. Ein-Dor L, Zuk O, Domany E. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci USA 2006, 103(15):5923-8. 10.1073/pnas.0601231103, 1458674, 16585533.
7. Stec J, Wang J, Coombes K, Ayers M, Hoersch S, Gold DL, Ross JS, Hess KR, Tirrell S, Linette G, Hortobagyi GN, Symmans WF, Pusztai L. Comparison of the predictive accuracy of DNA array-based multigene classifiers across cDNA arrays and Affymetrix GeneChips. J Mol Diagn 2005, 7(3):357-67. 1867535, 16049308.
8. Warnat P, Eils R, Brors B. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes. BMC Bioinformatics 2005, 6:265. 10.1186/1471-2105-6-265, 1312314, 16271137.
9. Consortium MAQC. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 2006, 24(9):1151-1161. 10.1038/nbt1239, 16964229.
10. Geman D, d'Avignon C, Naiman DQ, Winslow RL. Classifying gene expression profiles from pairwise mRNA comparisons. Stat Appl Genet Mol Biol 2004, 3. Article19., 1989150, 16646797.
11. Xu L, Tan AC, Naiman DQ, Geman D, Winslow RL. Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data. Bioinformatics 2005, 21(20):3905-11. 10.1093/bioinformatics/bti647, 16131522.
12. Tan AC, Naiman DQ, Xu L, Winslow RL, Geman D. Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics 2005, 21(20):3896-904. 10.1093/bioinformatics/bti631, 1987374, 16105897.
13. Xu L, Geman D, Winslow RL. Large-scale integration of cancer microarray data identifies a robust common cancer signature. BMC Bioinformatics 2007, 8:275. 10.1186/1471-2105-8-275, 1950528, 17663766.
14. Xu L, Tan AC, Winslow RL, Geman D. Merging microarray data from separate breast cancer studies provides a robust prognostic test. BMC Bioinformatics 2008, 9:125. 10.1186/1471-2105-9-125, 2409450, 18304324.
15. Schneider J, Ruschhaupt M, Buness A, Asslaber M, Regitnig P, Zatloukal K, Schippinger W, Ploner F, Poustka A, Sültmann H. Identification and meta-analysis of a small gene expression signature for the diagnosis of estrogen receptor status in invasive ductal breast cancer. Int J Cancer 2006, 119(12):2974-2979. 10.1002/ijc.22234, 17019712.
16. Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-Kains B, Desmedt C, Larsimont D, Cardoso F, Peterse H, Nuyten D, Buyse M, de Vijver MJV, Bergh J, Piccart M, Delorenzi M. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006, 98(4):262-72.
17. Goncalves A, Esterni B, Bertucci F, Sauvan R, Chabannon C, Cubizolles M, Bardou VJ, Houvenaegel G, Jacquemier J, Granjeaud S, Meng XY, Fung ET, Birnbaum D, Maraninchi D, Viens P, Borg JP. Postoperative serum proteomic profiles may predict metastatic relapse in high-risk primary breast cancer patients receiving adjuvant chemotherapy. Oncogene 2006, 25(7):981-989. 10.1038/sj.onc.1209131, 16186794.
18. Esseghir S, Kennedy A, Seedhar P, Nerurkar A, Poulsom R, Reis-Filho JS, Isacke CM. Identification of NTN4, TRA1, and STC2 as prognostic markers in breast cancer in a screen for signal sequence encoding proteins. Clin Cancer Res 2007, 13(11):3164-3173. 10.1158/1078-0432.CCR-07-0224, 17545519.
19. Shimo A, Tanikawa C, Nishidate T, Lin ML, Matsuda K, Park JH, Ueki T, Ohta T, Hirata K, Fukuda M, Nakamura Y, Katagiri T. Involvement of kinesin family member 2C/mitotic centromere-associated kinesin overexpression in mammary carcinogenesis. Cancer Sci 2008, 99:62-70.
20. McLachlan E, Shao Q, Wang HL, Langlois S, Laird DW. Connexins act as tumor suppressors in three-dimensional mammary cell organoids by regulating differentiation and angiogenesis. Cancer Res 2006, 66(20):9886-9894. 10.1158/0008-5472.CAN-05-4302, 17047050.
21. Klebanov L, Yakovlev A. Diverse correlation structures in gene expression data and their utility in improving statistical inference. Ann Appl Statist 2007, 1(2):538-559.
22. Buness A, Kuner R, Ruschhaupt M, Poustka A, Sültmann H, Tresch A. Identification of aberrant chromosomal regions from gene expression microarray studies applied to human breast cancer. Bioinformatics 2007, 23(17):2273-80. 10.1093/bioinformatics/btm340, 17599933.
23. Ihaka R, Gentleman R. A language for data analysis and graphics. Journal of Computational and Graphical Statistics 1996, 5:299-314.
24. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004, 5(10):R80. 10.1186/gb-2004-5-10-r80, 545600, 15461798.
25. Liaw A, Wiener M. Classification and Regression by randomForest. R News 2002, 2(3):18-22.
26. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 2002, 99(10):6567-72. 10.1073/pnas.082099299, 124443, 12011421.
27. Vapnik V. The nature of statistical learning theory 1999, Springer Verlag.
28. Ruschhaupt M, Huber W, Poustka A, Mansmann U. A compendium to ensure computational reproducibility in high-dimensional classification tasks. Stat Appl Genet Mol Biol 2004, 3. Article37..